Heat Shock Protein 90 (HSP90) Inhibitors as Anticancer Medicines: A Review on the Computer-Aided Drug Discovery Approaches over the Past Five Years

✅ 全文

热休克蛋白90(HSP90)抑制剂作为抗癌药物:基于计算机辅助药物发现方法的五年研究综述

作者 Ayanda M. Magwenyane; Samuel Chima Ugbaja; Daniel G. Amoako; Anou M. Somboro; René B. Khan; Hezekiel M. Kumalo 期刊 Computational and Mathematical Methods in Medicine 发表日期 2022 ISSN 1748-670X DOI 10.1155/2022/2147763 类型 原创研究 (Original Research)

📄 中文摘要 Chinese Abstract

中文
癌症是一项重大的全球健康负担,2018年预计有1810万新发病例和960万死亡病例。传统化疗同时影响正常细胞和癌细胞,导致显著的副作用,因此亟需更具选择性的抗癌药物。热休克蛋白90(HSP90)在多种癌症中过表达,在稳定参与肿瘤生长、侵袭和转移的致癌客户蛋白方面发挥关键作用,使其成为一个极具吸引力的治疗靶点。然而,尽管已有大量研究,尚无HSP90抑制剂获得FDA批准,主要原因在于其毒性、热休克反应(HSR)的诱导以及较差的药代动力学特性。本综述聚焦于过去五年(2016–2021年)中用于开发高效、选择性HSP90抑制剂作为抗癌药物的计算机辅助药物设计(CADD)方法。

📋 英文结构化总结 English Structured Summary

全文整理

EN

Background:

Cancer is a major global health burden, with 18.1 million new cases and 9.6 million deaths predicted in 2018. Traditional chemotherapy affects both normal and cancer cells, leading to significant side effects, highlighting the need for more selective anticancer agents. Heat shock protein 90 (HSP90) is overexpressed in many cancers and plays a critical role in stabilizing oncogenic client proteins involved in tumor growth, invasion, and metastasis. This makes HSP90 an attractive therapeutic target. However, despite extensive research, no HSP90 inhibitor has received FDA approval, largely due to toxicity, induction of heat shock response (HSR), and poor pharmacokinetic properties. This review focuses on computer-aided drug design (CADD) approaches used over the past five years (2016–2021) to develop potent and selective HSP90 inhibitors as anticancer agents.

Methods:

This is a review article that synthesizes findings from recent studies employing various CADD methodologies for HSP90 inhibitor development. The approaches include homology modeling, quantitative structure-activity relationship (QSAR), molecular docking, virtual screening, pharmacophore modeling and validation, and molecular dynamics (MD) simulations. These computational techniques were applied to design, optimize, and evaluate novel HSP90 inhibitors targeting the N-terminal, middle, and C-terminal domains of the protein. Studies published between 2016 and 2021 were analyzed to summarize advances in structural modeling, binding interaction analysis, and prediction of ADMET properties.

Results:

Recent computational studies have identified multiple promising HSP90 inhibitors with high binding affinity and selectivity. For example, in 2016, Mahmoud et al. used pharmacophore modeling and virtual screening to identify 24 hits with HSP90 inhibitory activity, 15 of which showed IC50 values in the micromolar range. Baby et al. identified two strong inhibitors, Q1G and T21, which formed key hydrogen bonds with Asp93, Ser52, and Tyr139. In 2017, Abbasi et al. utilized 3D-QSAR and MD simulations to show that hydroxyl groups on resorcinol rings are crucial for binding. Terracciano et al. (2018) discovered two novel C-terminal inhibitors that induced cancer cell death without triggering HSR—a key advantage over N-terminal inhibitors. Natural product-based inhibitors were also explored; Rampogu et al. (2019) identified three phytochemicals with superior docking scores (up to 73.04) compared to geldanamycin (48.27) and radicicol (40.90). MD simulations confirmed stable binding of top candidates to HSP90.

Data Summary:

Key quantitative findings include: docking scores ranging from –8.7 to –10.7 kcal/mol for active compounds; IC50 values in the micromolar range for multiple hits; and binding free energies validated through MD simulations. Pharmacophore models typically featured two hydrogen bond donors, two acceptors, and two hydrophobic features. Virtual screening of over 3,200 natural compounds yielded 135 hits, with three showing superior properties to clinical candidates. Several studies reported stable RMSD values (<2 Å) during 50–100 ns MD simulations, confirming complex stability.

Conclusions:

CADD has significantly advanced the discovery of HSP90 inhibitors by enabling rational design, reducing reliance on costly and time-consuming experimental screening. The integration of QSAR, docking, MD simulations, and pharmacophore modeling has led to the identification of novel scaffolds—including resorcinol derivatives, pyrazolopyrimidines, and natural compounds—with improved selectivity and reduced toxicity. Notably, C-terminal and middle-domain inhibitors offer potential to avoid HSR induction, a major limitation of N-terminal inhibitors. Despite progress, challenges remain in achieving clinical success, underscoring the need for continued optimization using advanced computational models targeting all three HSP90 domains.

Practical Significance:

The application of CADD in HSP90 inhibitor development accelerates anticancer drug discovery by predicting efficacy, selectivity, and safety profiles early in the pipeline. These approaches can reduce failure rates in clinical trials by identifying compounds with optimal binding, minimal off-target effects, and favorable ADMET properties. Ultimately, this supports the creation of more effective, less toxic cancer therapies, particularly for tumors resistant to conventional treatments.

📋 中文结构化总结 Chinese Structured Summary

中文

背景:

癌症是一项重大的全球健康负担,2018年预计有1810万新发病例和960万死亡病例。传统化疗同时影响正常细胞和癌细胞,导致显著的副作用,因此亟需更具选择性的抗癌药物。热休克蛋白90(HSP90)在多种癌症中过表达,在稳定参与肿瘤生长、侵袭和转移的致癌客户蛋白方面发挥关键作用,使其成为一个极具吸引力的治疗靶点。然而,尽管已有大量研究,尚无HSP90抑制剂获得FDA批准,主要原因在于其毒性、热休克反应(HSR)的诱导以及较差的药代动力学特性。本综述聚焦于过去五年(2016–2021年)中用于开发高效、选择性HSP90抑制剂作为抗癌药物的计算机辅助药物设计(CADD)方法。

方法:

本文为综述性文章,综合了近年来采用多种CADD方法开发HSP90抑制剂的研究结果。所涉及的方法包括同源建模、定量构效关系(QSAR)、分子对接、虚拟筛选、药效团建模与验证以及分子动力学(MD)模拟。这些计算技术被用于设计、优化和评估靶向HSP90蛋白N端、中间区域及C端结构域的新型HSP90抑制剂。对2016年至2021年间发表的研究进行了分析,以总结在结构建模、结合相互作用分析及ADMET性质预测方面的进展。

结果:

近期的多项计算研究已鉴定出多种具有高结合亲和力和选择性的有前景的HSP90抑制剂。例如,2016年,Mahmoud等人利用药效团建模和虚拟筛选鉴定出24个具有HSP90抑制活性的命中化合物,其中15个的IC50值处于微摩尔范围。Baby等人鉴定出两种强效抑制剂Q1G和T21,它们与Asp93、Ser52和Tyr139形成了关键的氢键。2017年,Abbasi等人利用3D-QSAR和MD模拟表明,间苯二酚环上的羟基对结合至关重要。Terracciano等人(2018年)发现了两种新型C端抑制剂,可诱导癌细胞死亡而不触发HSR——这是相较于N端抑制剂的关键优势。此外,还探索了基于天然产物的抑制剂;Rampogu等人(2019年)鉴定出三种植物化学成分,其对接得分(最高达73.04)优于格尔德霉素(48.27)和根赤壳菌素(40.90)。MD模拟证实了顶级候选化合物与HSP90的稳定结合。

数据总结:

关键定量结果包括:活性化合物的对接得分范围为–8.7至–10.7 kcal/mol;多个命中化合物的IC50值处于微摩尔范围;结合自由能通过MD模拟得到验证。药效团模型通常包含两个氢键供体、两个氢键受体和两个疏水特征。对超过3200种天然化合物的虚拟筛选产生了135个命中化合物,其中三种表现出优于临床候选化合物的性质。多项研究报道在50–100 ns的MD模拟中RMSD值稳定在2 Å以下,证实了复合物的稳定性。

结论:

CADD通过实现合理设计、减少对昂贵且耗时的实验筛选的依赖,显著推进了HSP90抑制剂的发现。QSAR、对接、MD模拟和药效团建模的整合已导致多种新型骨架的鉴定,包括间苯二酚衍生物、吡唑并嘧啶类化合物和天然化合物,这些骨架具有更高的选择性和更低的毒性。值得注意的是,C端和中间结构域抑制剂有望避免HSR的诱导,这是N端抑制剂的主要局限性。尽管取得了进展,实现临床成功仍面临挑战,这凸显了利用靶向HSP90全部三个结构域的高级计算模型进行持续优化的必要性。

实际意义:

CADD在HSP90抑制剂开发中的应用通过早期预测疗效、选择性和安全性特征,加速了抗癌药物的发现。这些方法可通过鉴定具有最佳结合能力、最小脱靶效应和良好ADMET性质的化合物来降低临床试验的失败率。最终,这有助于开发更有效、毒性更低的癌症疗法,尤其适用于对常规治疗耐药的肿瘤。

📖 英文全文 English Full Text

EN

pmc Comput Math Methods Med Comput Math Methods Med 1426 cmmm cmmm Computational and Mathematical Methods in Medicine 1748-670X 1748-6718 Wiley PMC9173959 PMC9173959.1 9173959 9173959 35685897 10.1155/2022/2147763 1 Review Article Heat Shock Protein 90 (HSP90) Inhibitors as Anticancer Medicines: A Review on the Computer-Aided Drug Discovery Approaches over the Past Five Years Magwenyane Ayanda M.

1 Ugbaja Samuel C.

1 https://orcid.org/0000-0003-3551-3458 Amoako Daniel G.

1

2 https://orcid.org/0000-0002-1085-7740 Somboro Anou M.

1

2 https://orcid.org/0000-0002-3589-6869 Khan Rene B.

1 https://orcid.org/0000-0002-4037-1549 Kumalo Hezekiel M. kumaloh@ukzn.ac.za

1

1 Drug Research and Innovation Unit, Discipline of Medical Biochemistry, School of Laboratory Medicine and Medical Science, University of KwaZulu-Natal, Durban 4000, South Africa

2 Biomedical Resource Unit, College of Health Sciences, University of KwaZulu-Natal, Durban 4000, South Africa Academic Editor: David Diller 2022 31 5 2022 2022 398264 2147763 12 10 2021 8 5 2022 19 5 2022 31 05 2022 08 06 2022 13 06 2022 Copyright © 2022 Ayanda M. Magwenyane et al. 2022 https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Cancer is a disease caused by the uncontrolled, abnormal growth of cells in different anatomic sites. In 2018, it was predicted that the worldwide cancer burden would rise to 18.1 million new cases and 9.6 million deaths. Anticancer compounds, often known as chemotherapeutic medicines, have gained much interest in recent cancer research. These medicines work through various biological processes in targeting cells at various stages of the cell's life cycle. One of the most significant roadblocks to developing anticancer drugs is that traditional chemotherapy affects normal cells and cancer cells, resulting in substantial side effects. Recently, advancements in new drug development methodologies and the prediction of the targeted interatomic and intermolecular ligand interaction sites have been beneficial. This has prompted further research into developing and discovering novel chemical species as preferred therapeutic compounds against specific cancer types. Identifying new drug molecules with high selectivity and specificity for cancer is a prerequisite in the treatment and management of the disease. The overexpression of HSP90 occurs in patients with cancer, and the HSP90 triggers unstable harmful kinase functions, which enhance carcinogenesis. Therefore, the development of potent HSP90 inhibitors with high selectivity and specificity becomes very imperative. The activities of HSP90 as chaperones and cochaperones are complex due to the conformational dynamism, and this could be one of the reasons why no HSP90 drugs have made it beyond the clinical trials. Nevertheless, HSP90 modulations appear to be preferred due to the competitive inhibition of the targeted N-terminal adenosine triphosphate pocket. This study, therefore, presents an overview of the various computational models implored in the development of HSP90 inhibitors as anticancer medicines. We hereby suggest an extensive investigation of advanced computational modelling of the three different domains of HSP90 for potent, effective inhibitor design with minimal off-target effects. National Research Foundation College of Health Sciences, University of Kwa-Zulu Natal pmc-status-qastatus 0 pmc-status-live yes pmc-status-embargo no pmc-status-released yes pmc-prop-open-access yes pmc-prop-olf no pmc-prop-manuscript no pmc-prop-legally-suppressed no pmc-prop-has-pdf yes pmc-prop-has-supplement no pmc-prop-pdf-only no pmc-prop-suppress-copyright no pmc-prop-is-real-version no pmc-prop-is-scanned-article no pmc-prop-preprint no pmc-prop-in-epmc yes pmc-license-ref CC BY 1. Introduction Cancer is a term used to describe a collection of diseases defined by the uncontrolled development, growth, and spread of aberrant cells outside their normal bounds. According to World Health Organization (WHO) data, cancer is the second biggest cause of mortality worldwide, accounting for 9.6 million fatalities in 2018, or one in every six deaths [ 1 – 3 ]. The repeated upsurge of specific cancers is caused by several factors, including population expansion, ageing, and the altering forms of cancer determinants which could be socially or economically related [ 4 ]. Aside from the earlier mentioned concerns, lifestyle-related and physical behaviours such as nutrition, alcohol usage, and physiological behaviours have all been linked to cancer risk and burden [ 1 , 5 ]. Cancer can also be described as a group of diseases caused by cells' uncontrolled, abnormal growth in different anatomic sites [ 6 ]. New cells arise while old cells die in a natural, harmless cycle of dividing cells. Cancer disturbs cell division, causing old cells to persist and new cells to form prematurely, resulting in tumours visible as aberrant cell proliferation that causes swelling. It is important to note that some malignancies, such as leukaemia, do not form tumours and are identified in the bloodstream [ 6 ]. There are two sorts of tumours: benign and malignant. Benign tumours are noncancerous but can grow. However, such tumours do not grow and are noninvasive. In addition, benign tumours do not develop back when successfully removed through surgery. For example, the outcome may be fatal if a benign tumour is found pressing on the brain or other vital organs [ 7 ]. Cancerous growths are referred to as malignant tumours and can be aggressively and uncontrollably replicated [ 8 ]. Another characteristic feature of malignant tumours is their ability to metastasise, a process of invading other body parts [ 9 ]. Cancer progress depends on external as well as internal factors, including those of the environment. The external considerations include cigarette/tobacco products, radiation, and infectious species. On the other hand, genetic mutations, hormonal abnormalities of the immune system, and mutated metabolism are some of the internal aetiologies of cancer [ 10 ]. Tobacco use kills seven million people every year globally, with two-thirds of users expected to die from the disease. Smoking accounts for 22 percent of global cancer fatalities [ 11 ]. A weekly dose of a single wine bottle is predicted to have an increased absolute lifetime cancer risk of 1.0 percent and 1.4 percent for nonsmoking males and females, respectively [ 11 ]. The absolute average cancer risk increase for one bottle of wine per week is equivalent to five (men) or ten cigarettes per week (women) [ 11 ]. More than 20 percent of deaths from cancer in the developed world arise from infections that include hepatitis B and C viruses as well as human papillomavirus [ 12 ]. Hereditary abnormalities are thought to be the cause of 5–10% of cancers. In 2018, it was predicted that the worldwide cancer burden would rise to 18.1 million new cases and 9.6 million deaths. In the worldwide setting, 1 in 5 men and 1 in 6 women develop cancer over their lifetime, with 1 in 8 men and 1 in 11 women succumbing to the disease. The 5-year prevalence, or the number of persons living within five years following a cancer diagnosis, is predicted to be 43.8 million people worldwide [ 13 ]. Most common cancers remain undetected for months or even years due to slow accumulation of their mutations. DNA mutations usually occur at a rate of 1 in every 20 million genes per cell division [ 6 ]. Sequel to the stress resulting from the ongoing research, researchers and pharmaceutical industries are under significant pressure to design and develop highly selective and efficient drugs for the treatment and management of various deadly cancers [ 1 ]. Anticancer compounds, often known as chemotherapeutic medicines, have gained much interest in recent cancer research [ 14 ]. These medicines work through various biological processes in targeting cells at multiple stages of the cell's life cycle. Recently, advancements in new drug development methodologies and the prediction of the targeted interatomic and intermolecular ligand interaction sites have been beneficial. This has prompted further research into developing and discovering novel chemical species as preferred therapeutic compounds against specific cancer kinds [ 15 ]. One of the most significant roadblocks in developing anticancer drugs is that traditional chemotherapy affects normal cells and cancer cells, resulting in substantial side effects [ 1 ]. Identifying new drug molecules with high selectivity and specificity for cancer is, therefore, a prerequisite in treating and managing the disease [ 1 , 15 ]. There is a literature gap in terms of recent experimental and, in particular, computational data on the design and development of new HSP90 inhibitors. As a result, this review offers an insightful update on the various computational models implored in the development of HSP90 inhibitors as anticancer medicines over the past five years (2016 to 2021). 2. Overview of Some Cancer Therapeutic Targets for Drug Discovery For many decades, care for patients with cancer was limited to only a few options. This included surgery, radiation therapy for minor solid tumours, and chemotherapy for blood cancers and solid metastatic tumours [ 16 ]. Human protein tyrosine kinases (PTKs) have a significant contribution to human cancer development and have surfaced as potential targets for cancer prevention [ 17 , 18 ]. The discovery of small-molecule species with high selectivity and specificity to inhibit targeted proteins with cancer cells appears to be the brilliant strategy in cancer therapy. The epidermal growth factor receptor (EGFR) overexpression has been detected in most cancers. Therefore, targeting EGFR (proteins that attach to epidermal growth factors) has been one of the effective cancer therapies. Rapid cell division occurs when there is an overexpression of EGFR [ 19 ]. These EGFR proteins play a significant role in the signal's transmission network responsible for the survival and division of cells. Over the years, discovering small-molecule inhibitors of EGFR tyrosine kinase has attracted more significant resources from the pharmaceutical industry [ 20 ]. Overexpression of EGFR is linked to a poor clinical outcome in a variety of cancers, including cancers of the head and neck, larynx, oesophagus, stomach, pancreas, colon, renal cell, bladder, breast, ovaries, cervix, prostate, non-small-cell lung (NSCL) cancer, papillary thyroid cancers, melanoma, and gliomas [ 21 ]. Recently, numerous EGFR tyrosine kinase inhibitors (TKIs) have been identified, including canertinib, erlotinib, and gefitinib. Nevertheless, there are still current research studies towards discovering more potent and effective EGFR inhibitors due to some observed resistance to the available inhibitors [ 22 ]. Researchers have also targeted cyclin-dependent protein kinases (CDKs) in the treatment of cancer. CDKs are essential proteins responsible for regulating the expression of most transmission mechanisms via the cell cycles [ 23 ]. In addition, the CDKs contribute immensely to other cell cycle mechanisms such as functions of the neurons, metabolic activities, and gene transcriptions [ 23 ]. Cyclin-dependent kinases (CDKs) are serine/threonine kinases responsible for protein phosphorylation on serine and threonine residues [ 24 , 25 ]. According to studies, the overactivity of CDKs or inoperative CDK-inhibiting proteins is observed in some malignancies, thereby serving as promising targets for anticancer drugs [ 26 ]. Therefore, the development and design of drugs targeting CDK overexpression became imperative. Some CDK inhibitors, such as seliciclib, a cyclin-dependent kinase (CDK) inhibitor tested as an anticancer treatment, are already in phase II clinical trials [ 27 , 28 ]. Another cancer treatment target is poly(ADP-ribose) polymerase (PARP), a common nuclear enzyme that is a marker of deoxyribonucleic acid (DNA) damage. The DNA repair enzyme poly(ADP-ribose) polymerase (PARP) shows significant and abundant expression in the nucleus of mammals. Researchers have been so interested in PARP due to its structural and fascinating inhibition properties [ 29 ]. The Food and Drug Administration (FDA) has approved several PARP inhibitors, targeting several cancer types [ 30 ]. These PARP inhibitors also operate as radiosensitisers by delaying single-strand break reparation. These inhibitors subsequently promote double-strand break production, a hypothesis that has been used in many preclinical combinatory models of PARP inhibitor and ionising radiation therapy [ 30 ]. In addition, this enzyme aids DNA repair by facilitating ADP-ribosylation of DNA, histones, and several DNA repair enzymes [ 31 ]. For several illnesses, including cancer, PARPs have been the target of intensive structure-based drug design efforts. Some common examples of PARP targeted inhibitors include iniparib in the phase I trial, while BMN-673 and olaparib are in phase II clinical trials [ 32 ]. However, while traditional cancer therapies such as radiation and chemotherapy are effective, heat shock protein 90 (HSP90) is a promising cancer treatment target. Pancreatic cancer stem cells (CSCs) are involved in promoting pancreatic cancer invasion and metastasis. CSCs are influenced by protease activation receptor 1 (PAR1) by inducing CSC-like properties in Aspc-1 cells. Therefore, doxycycline has been reported to inhibit PAR1, which effectively inhibits the CSC-like properties of pancreatic cancer cells and activation of the FAK/PI3K/AKT pathway, and enhances the therapeutic effect of 5 FU [ 33 ]. The role of protease-activated receptor 2 (PAR2) in gefitinib resistance was investigated, and its expression was found to be significantly increased when non-small-cell lung cancer (NSCLC) cells or tumour tissue exhibited gefitinib resistance. PAR2 was thus inhibited, suggesting a reversal effect in gefitinib resistance, in that gefitinib modulates EGFR transactivation, cell viability, migration, and apoptosis in gefitinib-sensitive and -resistant NSCLC cells. The study showed that the combination of gefitinib and PAR2 (P2pal-185) significantly blocked ERK phosphorylation and epithelial mesenchymal transition (EMT) compared to gefitinib alone. PAR2 was proposed as a novel target and pathway to overcome gefitinib resistance in NSCLC [ 34 ]. Signal Transducer and Activator of Transcription 3 (Stat 3) was introduced as a promising target for the treatment of breast cancer. The novel Stat 3 inhibitor, named Statmp-151, was investigated in the breast cancer cell lines MCF-7 and MDA-MB-231 and in the murine breast cancer cell line 4T1. The results showed that Statmp-151 could be a potential drug for the treatment of breast cancer [ 35 ]. 2.1. Overview of Heat Shock Protein Heat shock proteins (HSPs) are a family of very abundant, essential, and evolutionarily conserved molecular chaperones. They maintain cellular homeostasis in response to stimuli that promote protein denaturation, such as hypoxia, anoxia, high temperatures, drugs, and other chemical compounds. HSP molecular mass, their subgroups, and cancer development are summarized in Table 1 . HSPs are categorised according to their molecular mass, with HSP70 being a subgroup of HSPs with a molecular weight of 70 kDa. HSP27, HSP40, HSP60, HSP70, HSP90, and large HSF are the main groupings categorised on the basis of their molecular weight. There has been a significant need to identify these large, overlapping proteins, as the number of HSP members is increasing and their names are confusing, as they are likely to have a high degree of similarity in some situations but differ greatly in others. HSPs are thought to play a key role in the molecular pathways that contribute to the growth and spread of cancer. HSPs may also have clinical applications as biomarkers for cancer diagnosis and disease progression assessment or as therapeutic targets for cancer therapy. HSPs could be used as therapeutic targets in the treatment of cancer, leading to the development of new chemotherapeutic agents. HSP70 and HSP90 are the two best-studied members of the HSP family. The proteins GRP78 (a member of the HSP70 family) and HSP90 are targeted by most of the new cancer drugs currently being developed. Some of these drugs have been tested in clinical trials and have been shown to be effective against cancer cells in vitro and in animal xenograft models in vivo. It is not yet clear why cancer cells require different amounts of HSPs than normal cells, and more knowledge could lead to the discovery of a therapeutic window for the development of more effective and less toxic HSP inhibitors against cancer. HSP inhibitors could cause serious organ-specific toxicity (liver or eye toxicity) that is difficult to treat. It may be possible to circumvent the organ-specific toxicities of HSP inhibitors by identifying HSP functions that are specific to cancer cells. Some HSP inhibitors may be ineffective against cancer. To control cellular processes, members of the HSP family communicate and coordinate in a signalling network. When one HSP is inhibited, other HSPs may be overexpressed to compensate for the inhibitory effect of the single HSP inhibitor. For example, inhibition of HSP90 leads to overexpression of HSP27 and other HSPs, resulting in a heat shock response. The nomenclature and classification of human HSPs can be further improved to support functional descriptions of HSPs and drug discovery. Tumours in which HSP90 is overexpressed include pancreatic, ovarian, breast, lung, and endometrial cancers, squamous cell carcinoma of the oropharynx, and multiple myeloma [ 36 – 38 ]. In lung cancer, oesophageal cancer, bladder cancer, melanoma, and leukaemia, high expression of HSP90 has been shown to be a predictor of poor prognosis [ 39 , 40 ]. HSP90 is a potential therapeutic target to suppress tumour development and progression as it plays a critical role in cancer biology, and many HSP90 inhibitors have been explored in clinical trials. As anticancer drugs, HSP90 inhibitors offer several advantages because many signalling proteins are HSP90 client proteins, HSP90 inhibitors can act on numerous signalling pathways simultaneously. Therefore, anti-HSP90 therapy is less likely to lead to tumour cell survival than therapy with only one target protein. HSP90 is the most abundant chaperone protein in eukaryotes, accounting for around 1% to 2% of cytosolic proteins [ 41 ]. Under a range of intracellular and extracellular stressful situations, HSP90 supports the proper folding of newly generated proteins and helps in refolding denatured proteins [ 42 ]. The heat shock proteins generally are molecular chaperones that play key roles in many aspects of life. They are involved in the refolding of misfolded proteins, which helps to maintain cellular homeostasis. Heat shock factor (HSF) is activated in response to environmental stress and binds to heat shock elements (HSEs), increasing HSP translation and consequently high levels of HSP synthesis [ 43 ]. The heat shock protein 90 (HSP90) molecular chaperone occurs ubiquitously in eukaryotes and prokaryotes, where it plays a significant role in the maintenance of cell stability [ 44 , 45 ]. Reliant on ATP, HSP90 is involved in activation, proper folding, assembly, transportation, conformation preservation, and breakdown of target proteins [ 46 , 47 ]. However, these target proteins encompass many elevated or transmuted carcinogenic proteins, including p53 and hTERT, some of which are associated with cancer characteristics [ 48 , 49 ]. These customer proteins in tumour generation, growth, invasion, and metastasis make HSP90 an appealing therapeutic cancer target [ 50 , 51 ]. The overexpression of HSP90 has been observed in patients with cancer, and it has been observed that HSP90 triggers the unstable harmful kinase functions, which enhance carcinogenesis [ 52 , 53 ]. Thus, highly efficient inhibitors are developed and synthesised for the treatment of HSP90-associated cancers. Therefore, the chemical bonding of inhibitors to HSP90 leads to customer protein breakdown that causes improper protein folding, which blocks tumourigenesis and escapes the drawbacks of drug resistance [ 54 ]. In such an instance, the oncoprotein implicated in several carcinogenic pathways is concurrently eliminated, creating a combinative tumour attack and significantly increasing the cancer cure rate. HSP90 inhibitors, therefore, have positive applications in the treatment of tumours [ 43 , 55 ]. 3. The Structural Description of HSP90 The HSP90 proteins exist as homodimers where an individual monomer comprises three domains: the N-terminal domain, middle domain, and C-terminal domain ( Figure 1 ) [ 56 ]. The N-terminal domain, which is a member of the GHKL superfamily, constitutes the major ATPase domain of HSP90. The N-terminal domain possesses a super-charged linker part with varying size (length), having a similar structure with gyrase, topoisomerase, and histidine kinase. It also constitutes other isoforms and species that link it to the middle domain [ 56 – 59 ]. The middle domain performs a significant function in hydrolysing the adenosine triphosphate (ATP) [ 60 ]. The C-terminus is responsible for the formation of the significant dimer interface of the HSP90. The Met-Glu-Glu-Val-Asp (MEEVD) motif, a fundamental interaction site for a subset of cochaperones with tetratricopeptide repeat (TPR) domains, is also found at the C-terminus [ 61 ]. Biological research has shown that a polypeptide is a homodimer where each monomer consists of three conserved regions that are flexibly related (N-, M-, and C-terminal domains). The N-terminal domain includes the nucleotide (ATP and ADP) and drug binding cleft, generally known as the “Bergerat fold” that is shut by a molecular “cork” of amino acids upon ATP binding and opens when an adenosine diphosphate (ADP) is attached. The middle domain is resistant to proteolysis and is designed to bind ATP's client proteins, a few cochaperones, and π -phosphate [ 60 ]. HSP90's ATPase activity is unquantifiable when that specific protein segment is absent and the site of protein dimerization is the C-terminal domain where a pentapeptide motif (Met-Glu-Glu-Val-Asp or MEEVD) that serves as a tetratricopeptide repeat (TPR) acceptor containing cochaperones is found. At the atomic level, the arrangement of this region, whose abstraction does not significantly disturb the functionality of the HSP90 protein, has yet to be solved [ 64 , 65 ]. 4. Development of Heat Shock Protein 90 Inhibitors The process responsible for protein regulation of the cells known as homeostasis or proteostasis helps in the constant adoption of the cells to dynamic environments. HSP90 as molecular chaperones help proteins adopt and fold while avoiding misfold and aggregates resulting from stress [ 56 ]. The activities of HSP90 as chaperones and cochaperones are complex due to the conformational dynamism. HSP90 modulations appear to be preferred due to the competitive inhibition of the targeted N-terminal adenosine triphosphate pocket [ 56 , 66 ]. Hitherto, among the 19 N-terminal targeted HSP90 inhibitors that made it to the clinical trials, none has been approved by FDA. This is because of harmful health effects such as the heat shock response (HSR) induction [ 63 ]. Figure 2 shows 2D structures of some of the failed N-terminal HSP90 inhibitors at the clinical trials. Compounds known as “C-terminal inhibitors,” which exploit several ways to control HSP90 function, have been produced as alternatives, either as natural product-based counterparts or through rational design [ 56 ]. One method of manipulating molecular chaperones is to use inhibitors produced from novobiocin to target the HSP90 C-terminus. Novobiocin structure-activity relationships have led to the discovery of neuroprotective or cytotoxic chemicals. C-terminal inhibitors are the only ones that can distinguish a prosurvival heat shock response from a cytotoxic reaction caused by client protein degradation [ 66 ]. Over the years, researchers explored the use of natural products in the development of HSP90 inhibitors. Delmotte and Delmotte-Plaquee discovered radicicol (RD) as an extract from monosporium border and used it as a macrocyclic lactone antibiotic [ 63 , 67 ]. In 1998, Schulte et al. identified radicicol as a competitive HSP90 inhibitor to geldanamycin (GA) [ 68 ]. Radicicol adoption of folding (perpendicular instead of parallel) conformations using the macrocyclic and aromatic rings differentiates it from geldanamycin. However, it is like radicicol in mimicking the conformational interactions with aspartate 93 (Asp93). Nevertheless, in vivo studies revealed some loopholes in radicicol's anticancer potency due to its short half-life and fast metabolic reactions [ 63 , 69 ]. Therefore, inhibiting HSP90 with GA has decreased cancerous cells' growth and the breakdown of oncogenic proteins [ 70 ]. Analysis of the crystal structure determined the binding site for GA and RD within HSP90 in the N-terminal ATP-binding domain and mimicked the open ADP-binding conformation [ 71 ]. While GA and RD effectively targeted and disturbed HSP90 activity, their toxicity, and low stability, their clinical application was unsuccessful. 4.1. HSP90 N-Terminal Inhibitors GA and RD selectivity towards HSP90 is due to HSP90's special N-terminal ATP-binding Bergerat fold geometric pocket, contained in the GHKL subgroup of ATPases [ 72 , 73 ]. Less toxic and highly stable inhibitors within the ATP-binding pocket emerged from this selectivity to mimic GA and RD interactions. The earliest small-molecule inhibitor of HSP90 to reach clinical trials was a chemical analogue of GA ( Figure 3 ), 17-allamino-17-demethoxy geldanamycin (17-AAG). This compound substituted the moiety 17-methoxy with a group 17-alkylamino to reduce toxicity [ 74 , 75 ]. Although anticancer efficiency was demonstrated in phase I clinical trials, particularly when combined with trastuzumab in HER-2–positive breast cancer patients, the development of 17-AAG was terminated because of poor aqueous solubility and patent problems [ 76 , 77 ]. Other analogues of benzoquinone ansamycin used in clinical trials include 17-DMAG, IPI-504, and 17-AG, and their structures are shown in Figure 3 . The IPI-504 analogue was the most promising as it proceeded to clinical trial phases II and III. IPI-504 is a reduced quinone variant of GA, showing heightened HSP90 sensitivity and decreasing liver toxicity in patients. Nonetheless, IPI-504 progress was halted due to ineffectiveness in clinical trials [ 77 ]. There are no benzoquinone ansamycin compounds left on clinical review. Radicicol imitates HSP90's ADP-bound configuration and interacts similarly with Asp93 geldanamycin. Contrastingly, RD is oriented differently to GA when binding and has a higher degree of binding to the ATP pocket [ 71 , 78 ]. Although not as structurally remarkable as GA, RD also takes on a folded configuration, with an essentially perpendicular rather than parallel macrocycle and aromatic ring [ 78 ]. However, RD does not exhibit antitumour activity because of its rapid in vivo metabolism [ 69 ]. Several synthetic analogues were produced by employing established conformational determinants of the RD-HSP90 bond complex. This resulted in the development of KF25706, a stable metabolic compound that demonstrated antiproliferative effectiveness in numerous human cancer cell lines and xenograft rodent models [ 79 ]. Although ideal for application, the complex nature of KF25706 has rendered it challenging for upscaling development. In addition to being vital for the inhibition of HSP90, the behaviour of RD's resorcinol moiety appears to be analogous to that of ATP's adenine ring. Several inhibitors have targeted this pharmacophore using the resorcinol ring and are undergoing clinical evaluation. It has been shown that STA-9090 (ganetespib), developed by Synta Pharmaceuticals Corp., is a resorcinol triazole molecule that has a high degree of binding with HSP90 and incapacitates it at as low concentrations as 0.01  μ M. Moreover, STA-9090 shows augmented invasion of tumours with low toxicity [ 79 ]. Rowlands et al. assessed a collection of 56 000 compounds and identified CCT018159, a molecule that includes the resorcinol-anchoring unit of radicicol [ 80 – 82 ]. Additional development of CCT018159 contributed to the formation of NVP, a resorcinol isoxazole amide approved by Novartis for clinical evaluation. More analogues of resorcinol include KW-2478 (Kyowa Hakko Kirin Pharma) and AT13387 (Astex). While analogues of benzoquinone ansamycin and radicicol are yet to be licensed for clinical application, the binding pocket reactions uncovered were utilised, and those compounds have been beneficial to developing an extra category of HSP90 inhibitors. The first fully synthetic derivatives produced were the purine-based compounds. These compounds utilised the folded conformation that GA and RD adopted following attachment to HSP90. Further studies of fully synthetic derivatives and their interactions with HSP90 contributed to PUH71 synthesis. This compound is affinitive of oncogenic cells and is only required to inhibit HSP90 activity at low concentrations. PU-H71 is currently under review for patients with advanced malignancies in a phase I clinical trial [ 83 – 85 ]. 4.2. HSP90 C-Terminal Inhibitors A second ATP-binding pocket was discovered inside the C-terminus of HSP90 using nucleotide affinity cleavage. The primary discovered C-terminal inhibitor was the natural product novobiocin [ 86 , 87 ]. The novobiocin interaction site is close to the domain of C-terminal dimerisation and binds in a curved position, synonymous with ADP. Novobiocin's interaction with the C-terminus led to target protein breakdown, while its interaction with HSP90 remains unsteady [ 88 ]. The novobiocin structure was used to synthesise compound A4 and its analogues. These A4 analogues were coumarin-modified ring systems that mimic adenine and guanine with additional strategically positioned hydrogen bond acceptors and donors to suit higher specificity into the pocket [ 89 ]. The most potent novobiocin analogue produced was KU-174, designed to imitate the ATP-bound conformation [ 90 ]. This compound has demonstrated potency in several cancer cell lines as it breaks down clients without heat shock response (HSR) [ 91 ]. The platinum-comprising chemotherapeutical cisplatin and the microtubule stabiliser taxol are other C-terminal inhibitors [ 92 ]. Presently, there is no FDA-approved C-terminal HSP90 inhibitor, which appears to be of much concern to the pharmaceutical industries and scientists. There is, therefore, the need for more resources to be channelled towards this area of research. Interestingly, one of the challenges of N-domain-targeted agents is addressed by the ability of these compounds to inhibit HSP90 function without HSR induction, making C-terminal inhibitors compounds of interest for future investigation and exploration. 4.3. Middle Domain Inhibitors Sansalvamide A (San A) is an isolated cyclic pentapeptide from the sea fungus Fusarium species [ 93 ]. Sansalvamide A attaches to the middomain N-terminal fragment of HSP90 and exerts the ability to allosterically interrupt the contacts of C-terminal binding cochaperones and client proteins [ 93 ]. Interestingly, Di-Sansalvamide A (Di-San A), a dimerised version of San A, was found to bind HSP90's C-middle domain, indicating that Di-San A physically averts C-terminal binding clients from being bound [ 93 ]. Three compounds derived from San A, H-10, H-15, and LY-15, have been investigated in melanoma cells as possible inhibitors of HSP90. Such agents inhibited concentration- and time-dependent melanoma cell line growth. In addition, LY-15 and H-10 induced apoptosis-related mitochondrial pathways associated with caspase-3 and caspase-9 activation but not caspase-8 [ 94 – 96 ]. 4.4. Some HSP90 Inhibitors in Clinical Trials While the compounds currently in clinical trials have a wide range of structures, a closer look reveals that they can generally be categorised based on their similarity to GA, RD, or the purine scaffold. Only SNX-5422 does not fall into any of these classifications ( Figure 2 ). As is common in drug discovery, natural products play an important role in lead discovery. GA in the case of HSP90, the active compounds of SNX-5422, RD, and ATP have played an important role in the development of small-molecule HSP90 inhibitors. None of these agents are acceptable as a therapeutic, but they have all served as good lead molecules or starting points for the majority of drugs currently being tested in clinical trials ( Table 2 ). 5. Molecular Reactivity, Allosteric Dynamics, and Allosteric Design The HSP90 plays a significant role in numerous metabolic pathways associated with cancer. Numerous trials targeting HSP90 have been conducted in drug discovery departments. In addition, several HSP90 inhibitors have been discovered but failed due to toxicity issues. Therefore, researchers have introduced allosteric perturbation as an alternative strategy to pharmacologically induce HSP90 ATPase activities and closure dynamics while modulating tumour cell death [ 98 – 100 ]. Furthermore, several advanced computational methods have been used in combination with experimental approaches to offer atomistic insights into the mechanistics of allosteric ligand recognition and its in vivo and in vitro processes, starting from the fully unbound state of HSP90 [ 101 – 103 ]. Stetz et al. [ 103 ] used a combination of molecular simulations and other studies of reaction experiments to disrupt the HSP90 conformation with structural analyses and coefficients to characterize the practical function of posttranslational modification (PTM) focal sites. The findings revealed that in the HSP90 conformation, a limited number of conserved PTMs serve as global biological mediators of allosteric dynamics and conversation, while maximally flexible PTM sites act as companions and sensors of allosteric structural changes. In 2020, Stetz et al. [ 102 ] examined the mechanism of allosteric interaction between HSP90 and Cdc37 phosphorylation sites during cartridge splicing. To quantify the heterogeneous consequences of phosphorylated sites and kinase-precise conversion switches, researchers used a combination of evolutionary assays, crude molecular simulations with noise-based fully collaborative modeling, and analyses of unbound and determinate HSP90 and Cdc37 systems. The findings show that the kinase-precise phosphorylation switches that convey signals to HSP90 vary their regulatory characteristics in part in response to current atopic predisposition. To see the transmission molecules inside the form and in HSP90, Astl et al. [ 101 ] employed an integrated laptop version with evolution and coefficient assessment, experimental protein linkage and structure modelling, molecular simulation, strength evaluation, and community modeling. They devised a network chain mechanism to ensure uniform coherence between compliance switches and identified important regulatory checkpoints that facilitate interactions and long-term dialogues with chaperones. The findings of this research add reveal further insights of the allergic law of HSP90 chaperones, as well as a model for the basic mechanism of conversation and a version of HSP90 with binding partners in the practical cycle. The tight ATPase response inside the vibrant site is related to the global structural and conformational dynamics of HSP90, according to large-scale biophysical investigations and molecular simulations. These findings offer a mechanistic model for the coupling of protein dynamics and catalysis and in HSP90, as well as a test of how the effects of extended coupling can affect enzymatic activity. Computational modelling and computer-aided drug design have immensely contributed to the successful development of drugs, especially in the contemporary pharmaceutical and drug industries [ 104 ]. Integrating computer-aided drug design (CADD) into the development of HSP90 has contributed to the enhancement of selective drug targeting with reduced toxic and off-target effects [ 1 ]. Computational methodologies contribute significantly to the study of biomolecular structures and function [ 105 ]. This is due to the vast number of therapeutic receptor X-ray structures existing in the present day. Conventionally, designing novel drugs is usually a cumbersome, costly, and prolonged process. However, CADD methods ( Figure 4 ) have constructively enhanced multitasking processes involved in drug development such as homology modelling, analysing the interacting proteins, predicting the binding sites, developing and validating the pharmacophore, molecular docking, and molecular dynamic simulations [ 1 ]. Some of these CADD are briefly described below. 5.1. Homology Modeling Homology modelling has significantly been helpful in modelling undetermined structures of HSP90 enzymes. Nevertheless, some already determined experimental structures have been stored and saved in the Protein Data Bank (PDB) [ 106 , 107 ]. Formerly, the investigation of interatomic and intermolecular properties of HSP90 was limited [ 63 ]. Recently, researchers have explored the molecular modelling method in designing three-dimensional models of HSP90, which have provided a substantial understanding of its structural and mechanical dynamic properties [ 108 ]. 5.2. QSAR Quantitative structure-activity relationship (QSAR) methods are applied to estimate the correlation between physicochemical parameters, structures of chemical compounds, and their biomolecular properties [ 109 ]. QSAR has successfully been used to design new and potent drugs in the pharmaceutical and drug industries. As a computer-aided drug design method, it has helped design potent HSP90 drugs and inhibitors [ 107 , 110 , 111 ]. 5.3. Molecular Docking This CADD technique has been employed in predicting and evaluating the ligand-receptor binding poses and modes with the receptor's acting sites. The process entails docking, subsequently scoring the various poses and applying them to determine and calculate binding free energies [ 112 ]. This docking technique has been pivotal in computational drug discovery and modelling, such as designing HSP90 selective inhibition [ 113 , 114 ]. Molecular docking extends to methods used to decipher the binding mechanism of small receptor ligands (macromolecules) [ 105 , 115 ]. It is typically executed on structure-based rational drug design to classify precise small-molecule ligand conformations and approximate the frequency of interactions between ligands and proteins [ 116 , 117 ]. The Cartesian coordinates of different receptors and ligands are utilised throughout the docking process to predict an appropriate conformation of the ligands for the resultant complex of ligand and receptor. To measure the ligand-receptor binding energy, molecular mechanics is used. Ligands and corresponding receptors interact dynamically, based on the molecular lock and critical method [ 118 , 119 ]. The binding energies of the different ligands and receptors are compared to the inhibitor's bioactivity against the particular enzyme [ 120 ]. 5.4. Virtual Screening Virtual screening involves the utilisation of the CADD in obtaining active lead molecules (compounds) from a considerable deposit or library of compounds. The method entails analysing three-dimensional structures of compounds resulting experimentally via X-ray crystallography or nuclear magnetic resonance (NMR) [ 112 ]. Virtual screening has been applied in the design of new HSP90 drugs with high selective inhibition [ 121 ]. 5.5. Pharmacophore Development and Validation Pharmacophore modelling has been used in the design of lead compounds when the structural properties of the protein are not resolved [ 122 ]. This technique has been employed in the discovery of compounds with the desired and selective inhibition [ 122 , 123 ]. This technique has been in the concept of CADD, virtual screening, and pharmacophore development which have lately been useful in drug repurposing. This approach has been helpful in the development of HSP90 inhibition towards cancer therapy [ 124 ]. 5.6. Molecular Dynamic (MD) Simulation This technique investigates the dynamic mechanisms of the ligand-protein complex under different environments and conditions [ 125 ]. Molecular dynamic simulation has been extensively valuable for understanding proteins and other biomolecular compounds with regard to structural conformations and therapeutic purposes [ 126 ]. They were employing the instruments of molecular dynamic simulations in the development of HSP90 inhibitors [ 126 ]. The MD simulation binding energy evaluation and other postanalyses have been employed to validate the efficacy and potency of the HSP90 inhibitor-receptor complex [ 127 ]. Hitherto, researchers have yet to extensively explore the promising potentials of employing the CADD in designing particular and selective covalent heat shock protein90 inhibitors [ 128 , 129 ]. 5.7. Computational Studies (2016-2021) Computational modelling and the application of computer-aided drug design (CADD) methods have been widely utilised to discover highly specific inhibitors for HSP90 towards cancer therapy [ 130 ]. In 2016, Mahmoud et al. discovered new HSP90 inhibitors by extensively employing CADD techniques such as pharmacophore modeling, molecular docking, QSAR, and virtual cocrystallisation pharmacophore [ 131 ]. The study identified twenty-four hits that showed HSP90 inhibition potentials with fifteen others having lower IC50 in micromolar [ 131 ]. In another research in 2016 by Baby et al., the authors employed the combinatory techniques of pharmacophore development and molecular docking in the identification of antagonist compounds of HSP90. The identified HSP90 antagonist compounds were chosen from heterocyclic molecules utilising GOLD 3.1 [ 122 , 132 ]. Two inhibitors, Q1G and T21 (Figures 5(a) and 5(b) ), had strong binding affinities and inhibitory effects, according to the findings. Q1G produced a network of H-bonds with the amino acid residues Asp93, Ser52, and Tyr139, whereas T21 generated H-bond interactions with Tyr139 and Asp93, indicating that these are important residues for HSP90 inhibition [ 122 , 132 ]. Two hydrogen bond donors, two hydrogen bond acceptors, and two hydrophobic characteristics made up the best pharmacophore model [ 122 , 132 ]. Furthermore, the molecular processes of HSP90 allosteric activation were discovered using MD simulations in conformational research by Vettoretti et al. This research also highlighted the structural consequences of allosteric modulation on HSP90, as well as its dynamical features in the active state, offering useful information for the development of new functional modulators [ 133 ]. In 2017, Abbasi et al. predicted novel HSP90 inhibitors from 3,4-isoxazolediamide scaffold through combined computational techniques of quantitative structure-activity relationship, molecular docking, and subsequent MD simulations [ 134 ]. The importance of size, shape, symmetry, and branching of HSP90 inhibitory molecules was evident from the results. Docking studies showed that 2 hydroxyl groups in the resorcinol ring were important and necessary for the complex affinity. The orientation of the three groups was associated with the substitution of different R groups. The molecular dynamic (MD) simulation results were a comparison of a new compound and a best synthesised compound, where the novel compound (Figures 5(c) – 5(e) ) settled in an active site with lower binding energy than the best synthesised. Similarly, Garg et al. validated the effectiveness of the structure-activity relationship (SAR) method in the development of new C-terminal HSP90 inhibitors. The biological properties of these novel HSP90 inhibitors were further evaluated [ 130 ]. Again in 2017, Kumar et al. applied virtual screening of a library of compounds in the identification of potent molecules that could inhibit the oncogenic HSP90 interactome connected with breast cancer. This investigation identified 5 active lead compounds with appreciable binding energies in the range of -8.7 kcal/mol −1 to 10.7 kcal/mol −1 [ 104 ]. In 2018, Terracciano et al. came up with the discovery of two novel potent C-terminal HSP90 inhibitors [ 135 ]. The two novel molecular species induced the death of cancerous cells while significantly downregulating HSP90. These new HSP90 inhibitors displayed a high capacity for interfering with the region of the HSP90 C terminal, which resulted due to the poor success of traditional N-terminal domain inhibitors, and an alternate inhibitory method was provided [ 135 – 139 ]. These discoveries were attained by employing exceptional refinement, molecular docking, and subsequent molecular dynamic simulations [ 135 ]. Sepehri and Ghavami studied tetrahydropyrido[4,3-d]pyrimidine derivatives ( Figure 5(f) ) as HSP90 inhibitors through molecular docking and 3D-QSAR CoMFA [ 140 ]. According to extracted contour maps or the CoMFA model, three inhibitors were obtained and docked to the N-terminal domain binding site of the HSP90. These compounds acquired essential binding energies [ 140 ]. Furthermore, Abbasi et al. also in 2018 predicted new HSP90 inhibitors based on isoxazole scaffold ( Figure 5(g) ) through 3D-QSAR, molecular docking, and molecular dynamics [ 141 ]. Using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA), the steric and electrostatic contour maps were made and hydrophobic and hydrogen bonds established, while donors and acceptors were generated. Consequently, novel compounds were predicted. The predicted compound binding modes in the HSP90 binding site were investigated and evaluated using molecular docking and molecular dynamics (MD), which were found to be stable in the binding site [ 141 ]. In 2019, Mettu et al. designed and synthesised novel pyrazolyl 2-aminopyrimidine derivatives ( Figure 5(h) ) as HSP90 inhibitors, which were evaluated using molecular docking studies [ 142 ]. The studies showed that synthesised molecules retained all the essential binding interactions with HSP90 [ 142 ]. In another study in 2019, Rampogu et al. focused on natural compounds as possible inhibitors of HSP90 for breast cancer-pharmacophore-guided molecular modeling [ 143 ]. The database of 3210 natural phytochemicals was assessed to retrieve the potential inhibitors after thorough confirmation of the model pharmacophore. Sequel to the screening, 135 phytochemical compounds were retrieved and further sorted by drug-likeness factors, including Lipinski's role of five, ADMET properties, and molecular docking-based scoring [ 144 , 145 ]. Three phytochemical molecules obtained from docking studies displayed better properties than investigated clinical therapeutics. In addition, the hit compound and reference compounds with docking scores of 48.27 (geldanamycin), 40.90 (radicicol), 73.04 (Hit1), 72.92 (Hit2), and 68.12 (Hit3) (Figures 5(i) and 5(j) ) were validated for their binding stability through molecular dynamics [ 143 ]. In 2020, Nazar et al. applied an in silico approach, molecular docking, molecular dynamic simulations, and binding free energy calculations to understand HSP90 inhibition mechanisms to identify novel cancer therapeutics [ 125 ]. On the grounds of GOLD fitness score and orientation to docked HSP90 inhibitors with their analogues, they were designated as the superlative molecules. The interactions of these inhibitors with the HSP90 active site were observed to be significant. Molecular dynamics (MD) of top-docked molecules (Figures 5(k) and 5(l) ) ensured a strong binding interaction between inhibition and the HSP90 active site. The results yielded new perceptions into the design of cancer-targeting HSP90 [ 125 ]. In another report, Nazar et al. designed new heat shock protein (HSP90) inhibitors by pharmacophore modeling and a virtual screening workflow was utilised to determine the molecule's key structure (ZINC02819805) ( Figure 5(m) ) [ 125 ]. Pyrazolopyranopyrimidine derivatives were designed through the optimisation of compound ZINC02819805. The key critical interactions between one of the designed compounds and HSP90 were deliberated by molecular dynamic simulation and displayed stability [ 125 ]. He et al. (2020) conducted in silico studies to design new vibsanin B derivatives (Figures 5(n) and 5(o) ) and HSP90 inhibitors based on 3D-QSAR, molecular docking, and molecular dynamic simulation [ 146 ]. The directive information for structural information of the inhibitors was conducted from CoMFA and CoMSIA [ 147 ]. The stability of inhibitors in the binding site was evaluated through molecular docking and dynamics, which also suggested that numerous key residues had a significant contribution to the activity. The majority of virtually designed compounds in this research presented a sensible ADMET profile, which offered theoretical support for the structural medication of HSP90 [ 147 , 148 ]. Godoy-Castillo et al. identified the naphthoquinone derivative inhibitor's binding site in HSP90 through an induced-fit docking, molecular dynamics, and 3D-QSAR study [ 149 ]. Molecular docking and dynamic simulations brought an understanding of the binding modes and the respective protein-inhibitor interactions. The results provided grounds for rational modifications of novel molecules founded from the quinone scaffold ( Figure 5(p) ), to create HSP90 inhibitors of high potency for high antitumour activity [ 149 ]. Tomašič et al. also discovered new HSP90 C-terminal inhibitors using 3D-pharmacophore obtained from molecular dynamic simulations [ 150 ]. A suitable binding site was identified by pharmacophore models and virtual screening derived from a unique approach that allows one to derive and analyse ligand-protein interaction from molecular dynamic trajectories. Among the retrieved compounds from virtual screening, two compounds were biologically tested. A compound that offered a unique scaffold with promising properties for future synthetic optimisation and molecule development is required to evaluate HSP90 C-terminal domain as the focus of interest to develop anticancer drugs [ 151 ]. Shadrack et al. conducted a computational study on the role of water and conformational fluctuations in HSP90 in response to inhibitors [ 152 ]. The authors suggested docking practices for the repurposing of FDA-approved HSP90 drugs. The apo, holo, and receptor ensemble (relaxed complex) structures, the role of water, and HSP90 conformational modifications were described [ 152 , 153 ]. Docking energies to the inclusion of water were reported to be more sensitive when executed on the crystal structure as contrasted with the RCS ensemble. The results serve as a possible basis for the development of HSP90 inhibitors [ 154 ]. Bekker et al. used a multicanonical molecular dynamic-based dynamic docking to thoroughly investigate the configurational space of an inhibitor binding to the N-terminal domain of HSP90 [ 155 ]. The dynamic docking method in this study effectively predicted the inherent binding site while thoroughly testing a wide configurational space, exerting an altering influence on the protein structure upon binding [ 155 ]. Cai et al. synthesised new pyrazole-containing imide derivatives ( Figure 5(q) ) and assessed them using molecular dynamic simulation [ 156 ]. The HSP90 was suggested as the probable drug target of these compounds with the assistance of pharmacophore and molecular docking. The stability of these compounds was evaluated by molecular dynamics [ 156 ]. Magwenyane et al. studied the structural and molecular insight into the remedial properties of radicicol (RD) and NVP-YUA922 (NVP) ( Figure 6 ) by inhibition of cancer using DFT, molecular docking, and MD to understand the HSP90 N-terminal dynamics [ 63 ]. Density functional theory (DFT) calculations predicted NVP to have high favourability with solvation free energy at -23.3 kcal/mol and the highest stability energy of 75.5 kcal/mol for a major atomic delocalisation. Molecular dynamic (MD) assessment revealed high stability of NVP bound to HSP90 (NT-NVP) when compared to RD (NT-RD). The HSP90 protein displayed a greater binding affinity for NT-NVP compared to NT-RD, where the vital residues prominent in the binding are Gly 97, Asp 93, and Thr 184. The discoveries therein serve as constructive perceptions into HSP90 dynamics and will assist in the construction of potent new inhibitors for cancer therapy [ 63 ]. Dike et al. applied the in silico approach to identify small-molecule modulators to disrupt the HSP90-Cdc37 protein-protein interaction boundary for cancer therapy [ 157 ]. Four molecules were discovered from a collection of above 60 000 compounds. The molecular dynamic (MD) simulation elucidated that all four molecules were kept inside the interface and strong affection for HSP90-Cdc37. Therefore, the molecule in suggestion could be critical to successfully inhibit the HSP90-Cdc37 interface [ 157 ]. In 2021, Mak et al. discovered two drug-like novel HSP90 CTD inhibitors by using virtual screening and intrinsic protein fluorescence quenching binding assays, preparing for future developments of novel therapeutics that utilise molecular chaperone inhibitors [ 151 ]. In another study, Rezvani et al. identified two new HSP90 inhibitors through the in silico techniques of molecular docking, MD simulations, and density functional theory [ 1 ]. Zinc15 structural queries were used to locate related compounds in HSP90 inhibitors in various clinical trials stages (78 percent). Twenty-nine small molecules were obtained and docked into an ensemble of HSP90-NTDs using a predetermined similarity cut-off. H-bond, hydrophobic, and salt bridge interactions were found to be determining forces in complex formation using molecular docking and intermolecular binding studies. The binding pocket of HSP90 was efficiently accommodated by compounds 19 and 20 due to their somewhat diverse conformations. Asn51 and Phe138 were discovered to be important residues that interacted with 19 and 20 in a stable manner [ 1 ]. Wang et al. employed the quantitative structure-activity relationship (QSAR) technique to investigate aminopyrazole-substituted resorcylate compounds as HSP90 inhibitors [ 158 ]. The new HSP90 inhibitors, aminopyrazole-substituted resorcylate compounds, have a wide range of HSP90 inhibitory action and were created to develop new antibacterial medications. The fungal selectivity of novel HSP90 inhibitors was predicted using a quantitative structure-activity relationship technique. The best linear model's R correlation coefficient was 0.89 and 0.11 and resulted in the development of two nonlinear models [ 158 ]. In another study, Tomašič et al. applied the three-dimensional pharmacophore profiling on selective DNA gyrase and HSP90 inhibitors [ 159 ]. The authors designed selective three-dimensional pharmacophore systems for GyrB, human topoisomerase II α (TopoII), and the HSP90 N-terminal domain (NTD) used as starting points for hit expansion and lead optimisation. Using their off-target pharmacophore modeling, they were able to predict the selective on-target binding of GyrB inhibitors. In vitro studies on HSP90 and TopoII for selected compounds 1 and 2 corroborated these findings. In vitro studies against E. coli DNA gyrase and human TopoII validated the prediction of selective HSP90 NTD inhibition for 3 and 4, which was also confirmed in in vitro assays against E. coli DNA gyrase and human TopoII. It was confirmed that designing three-dimensional chemical parameter-based pharmacophore models are useful instruments for predicting the activity and selectivity of known and novel HSP90 and GyrB inhibitors [ 159 ]. Rezvan et al. used a Zinc15 structural interrogation to reveal comparable compounds (≥78%) in HSP90 inhibitors that were at different stages of clinical trials. Small molecules were found docked to an ensemble of HSP90 NTDs using a predetermined similarity cut-off. Two molecules with very different conformations were shown to be well tolerated in the binding pocket of HSP90. Asn51 and Phe138 were found to be important residues that interacted stably with compounds. Even though the basic mechanism of action of the proposed compounds is unknown and remains to be investigated, the results of this work point to critical structural features for future structure-guided optimisation towards potent inhibitors of HSP90-NTD [ 1 ]. 6. Conclusion There has been a paradigm change in recent years toward the development of highly selective inhibitors of oncogenic HSP90. This was important to overcome roadblocks that hampered the postclinical approval of already available medications, notwithstanding their efficacy in a variety of preclinical and clinical investigations. Conventionally, designing novel drugs is usually a cumbersome, costly, and prolonged process. However, CADD methods have constructively enhanced multitasking processes involved in drug development such as homology modelling, analysing the interacting proteins, predicting the binding sites, developing and validating the pharmacophore, molecular docking, and molecular dynamic. The application of modern CADD techniques to HSP90 research has yielded structural and molecular insights that have aided in the identification and improvement of new HSP90 inhibitors with increased selectivity and activity. Despite the progress made thus far, a new dynamic in silico techniques with experimental validations is still needed to get highly selective results. We also suggest an extensive investigation of advanced computational modelling of the three different domains of HSP90 for potent, effective inhibitor design with minimal off-target effects. Acknowledgments We acknowledge the College of Health Sciences, University of Kwa-Zulu Natal, Durban, South Africa, and the South African National Research Foundation for supporting this study and the Centre for High-Performance Computing, South Africa ( www.chpc.ac.za ), for the computational resource. Data Availability The generated data used to support the findings of this study are included in the article. Conflicts of Interest The authors declare no conflict of interest. 1

Rezvani S.

Ebadi A.

Razzaghi-Asl N.

In silico identification of potential Hsp90 inhibitors via ensemble docking, DFT and molecular dynamics simulations

Journal of Biomolecular Structure and Dynamics 2021 1 12 10.1080/07391102.2021.1947383 34286666 2

Bray F.

Ferlay J.

Soerjomataram I.

Siegel R. L.

Torre L. A.

Jemal A.

Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries

CA: a Cancer Journal for Clinicians 2018 68 394 424 10.3322/caac.21492 2-s2.0-85053395052 30207593 3

Wu C.

Li M.

Meng H.

Analysis of status and countermeasures of cancer incidence and mortality in China

Science China. Life Sciences 2019 62 5 640 647 10.1007/s11427-018-9461-5 2-s2.0-85063278302 30900169 4

Wu S.

Zhu W.

Thompson P.

Hannun Y. A.

Evaluating intrinsic and non-intrinsic cancer risk factors

Nature Communications 2018 9, article 3490 10.1038/s41467-018-05467-z 2-s2.0-85052407450 PMC6113228 30154431 5

Kabat G. C.

Matthews C. E.

Kamensky V.

Hollenbeck A. R.

Rohan T. E.

Adherence to cancer prevention guidelines and cancer incidence, cancer mortality, and total mortality: a prospective cohort study

The American Journal of Clinical Nutrition 2015 101 3 558 569 10.3945/ajcn.114.094854 2-s2.0-84928885185 25733641 PMC4340061 6

Kumalo H. M.

Bhakat S.

Soliman M. E.

Heat-shock protein 90 (Hsp90) as anticancer target for drug discovery: an ample computational perspective

Chemical Biology and Drug Design 2015 86 5 1131 1160 10.1111/cbdd.12582 2-s2.0-84944074758 25958815 7

Bland K. I.

Copeland E. M.

Klimberg V. S.

Gradishar W. J.

The Breast E-Book: Comprehensive Management of Benign and Malignant Diseases

2017 Elsevier Health Sciences 8 Teng Y.

Yu Y.

Li S.

Ultraviolet radiation and basal cell carcinoma: an environmental perspective

Frontiers in Public Health 2021 9, article 666528 10.3389/fpubh.2021.666528 34368047 PMC8339433 9

Zhang W.

Wang F.

Hu C.

Zhou Y.

Gao H.

Hu J.

The progress and perspective of nanoparticle-enabled tumor metastasis treatment

Acta Pharmaceutica Sinica B 2020 10 11 2037 2053 10.1016/j.apsb.2020.07.013 33304778 PMC7714986 10

Anwar S.

Almatroudi A.

Alsahli M. A.

Khan M. A.

Khan A. A.

Rahmani A. H.

Natural products: implication in cancer prevention and treatment through modulating various biological activities

Anti-Cancer Agents in Medicinal Chemistry 2020 20 17 2025 2040 10.2174/1871520620666200705220307 32628596 11

Hydes T. J.

Burton R.

Inskip H.

Bellis M. A.

Sheron N.

A comparison of gender-linked population cancer risks between alcohol and tobacco: how many cigarettes are there in a bottle of wine?

BMC Public Health 2019 19, article 316 10.1186/s12889-019-6576-9 2-s2.0-85063595250 PMC6437970 30917803 12

Siegel R.

Ma J.

Zou Z.

Jemal A.

Cancer statistics, 2014 CA: a Cancer Journal for Clinicians

2014 64 9 29 10.3322/caac.21208 2-s2.0-84892805731 24399786 13

Araghi M.

Soerjomataram I.

Jenkins M.

Global trends in colorectal cancer mortality: projections to the year 2035

International Journal of Cancer 2019 144 12 2992 3000 10.1002/ijc.32055 2-s2.0-85059683838 30536395 14

Bhayye S. S.

Brahmachari G.

Nayek N.

Roy S.

Roy K.

Target prioritization of novel substituted 5-aryl-2-oxo-/thioxo-2,3-dihydro-1 H -benzo[6,7]chromeno[2,3- d ]pyrimidine-4,6,11(5 H )-triones as anticancer agents using in-silico approach 2019 38 1415 1424 10.1080/07391102.2019.1606735 2-s2.0-85065180899 30968736 15

Cui W.

Aouidate A.

Wang S.

Yu Q.

Li Y.

Yuan S.

Discovering anti-cancer drugs via computational methods

Frontiers in Pharmacology 2020 11 p. 733 10.3389/fphar.2020.00733 32508653 PMC7251168 16

Alsaab H. O.

Alghamdi M. S.

Alotaibi A. S.

Progress in clinical trials of photodynamic therapy for solid tumors and the role of nanomedicine

Cancers 2020 12 10 p. 2793 10.3390/cancers12102793 33003374 PMC7601252 17

Abrantes J. L. F.

Tornatore T. F.

Pelizzaro-Rocha K. J.

Crosstalk between kinases, phosphatases and miRNAs in cancer

Biochimie 2014 107 167 187 10.1016/j.biochi.2014.09.011 2-s2.0-84915817225 25230087 18

Aschner Y.

Zemans R. L.

Yamashita C. M.

Downey G. P.

Matrix metalloproteinases and protein tyrosine kinases: potential novel targets in acute lung injury and ARDS

Chest 2014 146 4 1081 1091 10.1378/chest.14-0397 2-s2.0-84907919355 25287998 PMC4188143 19

Ayati A.

Moghimi S.

Salarinejad S.

Safavi M.

Pouramiri B.

Foroumadi A.

A review on progression of epidermal growth factor receptor (EGFR) inhibitors as an efficient approach in cancer targeted therapy

Bioorganic Chemistry 2020 99 p. 103811 10.1016/j.bioorg.2020.103811 32278207 20

Boland B. S.

Sandborn W. J.

Chang J. T.

Update on Janus kinase antagonists in inflammatory bowel disease

Gastroenterology Clinics 2014 43 3 603 617 10.1016/j.gtc.2014.05.011 2-s2.0-84905724744 25110261 PMC4129380 21

Nicholson R. I.

Gee J. M. W.

Harper M. E.

EGFR and cancer prognosis European Journal of Cancer

2001 37 9 15 10.1016/S0959-8049(01)00231-3 11597399 22

Widatalla S. E.

Korolkova O. Y.

Whalen D. S.

Lapatinib-induced annexin A6 upregulation as an adaptive response of triple-negative breast cancer cells to EGFR tyrosine kinase inhibitors

Carcinogenesis 2019 40 8 998 1009 10.1093/carcin/bgy192 2-s2.0-85067543301 30590459 PMC6736109 23

Roskoski R.

Cyclin-dependent protein serine/threonine kinase inhibitors as anticancer drugs

Pharmacological Research 2019 139 471 488 10.1016/j.phrs.2018.11.035 2-s2.0-85059483751 30508677 24

Asada A.

Yamazaki R.

Kino Y.

Cyclin-dependent kinase 5 phosphorylates and induces the degradation of ataxin-2

Neuroscience Letters 2014 563 112 117 10.1016/j.neulet.2014.01.046 2-s2.0-84894065968 24486837 25

Lai M. W.

Chen T. C.

Pang S. T.

Yeh C. T.

Overexpression of cyclin-dependent kinase-associated protein phosphatase enhances cell proliferation in renal cancer cells

Urologic Oncology: Seminars and Original Investigations

2012 30 6 871 878 10.1016/j.urolonc.2010.09.010 2-s2.0-84870541520 21396835 26

Lu H.

Liu P.

Pan Y.

Huang H.

Inhibition of cyclin-dependent kinase phosphorylation of FOXO1 and prostate cancer cell growth by a peptide derived from FOXO1

Neoplasia 2011 13 9 854 863 10.1593/neo.11594 2-s2.0-80053454440 21969818 PMC3182277 27

Siebert S.

Pratt A. G.

Stocken D. D.

Targeting the rheumatoid arthritis synovial fibroblast via cyclin dependent kinase inhibition: an early phase trial

Medicine (Baltimore) 2020 99 26 p. e20458 10.1097/MD.0000000000020458 32590730 PMC7328978 28

Zhou Q.

Targeting cyclin-dependent kinases in ovarian cancer

Cancer Investigation 2017 35 367 376 10.1080/07357907.2017.1283508 2-s2.0-85026296742 28406716 29

Jannetti S. A.

Zeglis B. M.

Zalutsky M. R.

Reiner T.

Poly(ADP-ribose)polymerase (PARP) inhibitors and radiation therapy

Frontiers in Pharmacology 2020 11 p. 170 10.3389/fphar.2020.00170 32194409 PMC7062869 30

Risdon E. N.

Chau C. H.

Price D. K.

Sartor O.

Figg W. D.

PARP inhibitors and prostate cancer: to infinity and beyond BRCA

The Oncologist 2021 26 1 e115 e129 10.1634/theoncologist.2020-0697 32790034 PMC7794174 31

Sutcu H. H.

Matta E.

Ishchenko A. A.

Role of PARP-catalyzed ADP-ribosylation in the crosstalk between DNA strand breaks and epigenetic regulation

Journal of Molecular Biology 2020 432 6 1769 1791 10.1016/j.jmb.2019.12.019 31866292 32

Harrision D.

Gravells P.

Thompson R.

Bryant H. E.

Poly(ADP-ribose) glycohydrolase (PARG) vs. poly(ADP-ribose) polymerase (PARP) – function in genome maintenance and relevance of inhibitors for anti-cancer therapy

Frontiers in Molecular Biosciences 2020 7 p. 191 10.3389/fmolb.2020.00191 33005627 PMC7485115 33

Liu H.

Tao H.

Wang H.

Doxycycline inhibits cancer stem cell-like properties via PAR1/FAK/PI3K/AKT pathway in pancreatic cancer

Frontiers in Oncology 2021 10 10.3389/fonc.2020.619317 33643917 PMC7905084 34

Jiang Y.

Zhuo X.

Fu X.

Wu Y.

Mao C.

Targeting PAR2 overcomes gefitinib resistance in non-small-cell lung cancer cells through inhibition of EGFR transactivation

Frontiers in Pharmacology 2021 12 10.3389/fphar.2021.625289 PMC8100583 33967759 35

Fan C.

Wang Y.

Huang H.

The tetramethylpyrazine derivative Statmp-151: a novel small molecule Stat3 inhibitor with promising activity against breast cancer

Frontiers in Pharmacology 2021 12 10.3389/fphar.2021.651976 33967793 PMC8099110 36

Tian W.-L.

He F.

Fu X.

High expression of heat shock protein 90 alpha and its significance in human acute leukemia cells

Gene 2014 542 2 122 128 10.1016/j.gene.2014.03.046 2-s2.0-84898780109 24680776 37

Shi Y.

Liu X.

Lou J.

Plasma levels of heat shock protein 90 alpha associated with lung cancer development and treatment responses

Clinical Cancer Research 2014 20 23 6016 6022 10.1158/1078-0432.CCR-14-0174 2-s2.0-84918515119 25316816 38

Patel K.

Wen J.

Magliocca K.

Heat shock protein 90 (HSP90) is overexpressed in p16-negative oropharyngeal squamous cell carcinoma, and its inhibition in vitro potentiates the effects of chemoradiation

Cancer Chemotherapy and Pharmacology 2014 74 5 1015 1022 10.1007/s00280-014-2584-8 2-s2.0-84919951690 25205430 39

McCarthy M.

Pick E.

Kluger Y.

HSP90 as a marker of progression in melanoma Annals of Oncology

2008 19 3 590 594 10.1093/annonc/mdm545 2-s2.0-40149102363 18037622 40

Žáčková M.

Moučková D.

Lopotová T.

Ondráčková Z.

Klamová H.

Moravcová J.

Hsp90—a potential prognostic marker in CML Blood Cells, Molecules, and Diseases

2013 50 184 189 10.1016/j.bcmd.2012.11.002 2-s2.0-84873259656 23190580 41

Csermely P.

Schnaider T.

Szanto I.

Possible nuclear functions of the major molecular chaperones of the eukaryotic cytoplasm, Hsp90

Current Science 1998 74 5 442 445 42 Junho C. V.

Azevedo C. A. da Cunha R. S.

Heat shock proteins: connectors between heart and kidney

Cells 2021 10 8 p. 1939 10.3390/cells10081939 34440708 PMC8391307 43

Shan Q.

Ma F.

Wei J.

Li H.

Ma H.

Sun P.

Physiological functions of heat shock proteins Current Protein & Peptide Science

2020 21 8 751 760 10.2174/1389203720666191111113726 31713482 44

Paladino L.

Vitale A.

Santonocito R.

Molecular chaperones and thyroid cancer International Journal of Molecular Sciences

2021 22 8 p. 4196 10.3390/ijms22084196 33919591 PMC8073690 45

Pearl L. H.

Prodromou C.

Workman P.

The Hsp90 molecular chaperone: an open and shut case for treatment

The Biochemical Journal 2008 410 3 439 453 10.1042/BJ20071640 2-s2.0-41149111451 18290764 46

Picard D.

Heat-shock protein 90, a chaperone for folding and regulation

Cellular and Molecular Life Sciences 2002 59 10 1640 1648 10.1007/PL00012491 2-s2.0-0036810352 12475174 PMC11337538 47

Grindle M. P.

Carter B.

Alao J. P.

Connors K.

Tehver R.

Kravats A. N.

Structural communication between the E. coli chaperones DnaK and Hsp90

International Journal of Molecular Sciences 2021 22 p. 2200 10.3390/ijms22042200 PMC7926864 33672263 48

Shirai Y.

Chow C. C.

Kambe G.

An overview of the recent development of anticancer agents targeting the HIF-1 transcription factor

Cancers 2021 13 p. 2813 10.3390/cancers13112813 PMC8200185 34200019 49

Lallier M.

Marchandet L.

Moukengue B.

Molecular chaperones in osteosarcoma: diagnosis and therapeutic issues

Cells 2021 10 p. 754 10.3390/cells10040754 PMC8067202 33808130 50

Abbasi M.

Sadeghi-Aliabadi H.

Hassanzadeh F.

Amanlou M.

Prediction of dual agents as an activator of mutant p53 and inhibitor of Hsp90 by docking, molecular dynamic simulation and virtual screening

Journal of Molecular Graphics & Modelling 2015 61 186 195 10.1016/j.jmgm.2015.08.001 2-s2.0-84939191282 26277488 51

Calderwood S. K.

Khaleque M. A.

Sawyer D. B.

Ciocca D. R.

Heat shock proteins in cancer: chaperones of tumorigenesis

Trends in Biochemical Sciences 2006 31 3 164 172 10.1016/j.tibs.2006.01.006 2-s2.0-33644835965 16483782 52

Fabio Falsone S.

Leptihn S.

Osterauer A.

Haslbeck M.

Buchner J.

Oncogenic mutations reduce the stability of Src kinase

Journal of Molecular Biology 2004 344 1 281 291 10.1016/j.jmb.2004.08.091 2-s2.0-7044227709 15504417 53

Salinas-Garcia M. C.

Plaza-Garrido M.

Camara-Artigas A.

The impact of oncogenic mutations of the viral Src kinase on the structure and stability of the SH3 domain

Acta Crystallographica Section D Structural Biology

2021 77 854 866 10.1107/S2059798321004344 34076598 PMC8171063 54

Kabakov A.

Yakimova A.

Matchuk O.

Molecular chaperones in cancer stem cells: determinants of stemness and potential targets for antitumor therapy

Cells 2020 9 p. 892 10.3390/cells9040892 PMC7226806 32268506 55

Burdon R. H.

Heat shock proteins in relation to medicine Molecular Aspects of Medicine

1993 14 2 83 165 10.1016/0098-2997(93)90020-E 2-s2.0-0027218599 7901728 56

Bickel D.

Gohlke H.

C-terminal modulators of heat shock protein of 90 kDa (HSP90): state of development and modes of action

Bioorganic & Medicinal Chemistry 2019 27 21, article 115080 10.1016/j.bmc.2019.115080 2-s2.0-85071911150 31519378 57

Prodromou C.

Roe S. M.

Piper P. W.

Pearl L. H.

A molecular clamp in the crystal structure of the N-terminal domain of the yeast Hsp90 chaperone

Nature Structural Biology 1997 4 477 482 10.1038/nsb0697-477 2-s2.0-0030901877 9187656 58

Nakamoto H.

Yokoyama Y.

Suzuki T.

A cyclic lipopeptide surfactin is a species-selective Hsp90 inhibitor that suppresses cyanobacterial growth

Journal of Biochemistry 2021 170 2 255 264 10.1093/JB/MVAB037 33768253 59

Ghahraman M. R.

Hosseini-Nave H.

Azizi O.

Shakibaie M. R.

Mollaie H. R.

Shakibaie S.

Stereochemical trajectories of a two-component regulatory system PmrA/B in a colistin-resistant Acinetobacter baumannii clinical isolate

Iranian Biomedical Journal 2021 25 p. 193 10.52547/ibj.25.3.193 PMC8183390 33653023 60

Jin Y.

Structural and Functional Studies of the Hsp90 Molecular Chaperone

2021 Brandeis University ProQuest Dissertations Publishing 61

Blundell K. L. I. M.

Pal M.

Roe S. M.

Pearl L. H.

Prodromou C.

The structure of FKBP38 in complex with the MEEVD tetratricopeptide binding-motif of Hsp90

PLoS One 2017 12 3, article e0173543 10.1371/journal.pone.0173543 2-s2.0-85014826938 28278223 PMC5344419 62

Ali M. M. U.

Roe S. M.

Vaughan C. K.

Crystal structure of an Hsp90-nucleotide-p23/Sba1 closed chaperone complex

Nature 2006 440 7087 1013 1017 10.1038/nature04716 2-s2.0-33646176246 16625188 PMC5703407 63

Magwenyane A. M.

Mhlongo N. N.

Lawal M. M.

Understanding the Hsp90 N-terminal dynamics: structural and molecular insights into the therapeutic activities of anticancer inhibitors radicicol (RD) and radicicol derivative (NVP-YUA922)

Molecules 2020 25 p. 1785 10.3390/molecules25081785 PMC7221724 32295059 64

Didenko T.

Duarte A. M. S.

Karagöz G. E.

Rüdiger S. G. D.

Hsp90 structure and function studied by NMR spectroscopy

Biochimica et Biophysica Acta, Molecular Cell Research

2012 1823 3 636 647 10.1016/j.bbamcr.2011.11.009 2-s2.0-84857048019 22155720 65

Gupta S. D.

Hsp90 flexibility and development of its inhibitors for the treatment of cancer

Current Chemical Biology 2018 12 1 53 64 10.2174/2212796812666180405144003 2-s2.0-85050352043 66

Forsberg L. K.

The Development of Small Molecules That Modulate Molecular Chaperones Hsp90 and Hsp70

2017 University of Kansas 67 Delmotte P.

Delmotte-Plaquee J.

A new antifungal substance of fungal origin Nature

1953 171 p. 344 10.1038/171344a0 2-s2.0-0001251439 13036885 68

Schulte T. W.

Akinaga S.

Soga S.

Antibiotic radicicol binds to the N-terminal domain of Hsp90 and shares important biologic activities with geldanamycin

Cell Stress & Chaperones 1998 3 2 100 108 10.1379/1466-1268(1998)003&#x0003c;0100:ARBTTN&#x0003e;2.3.CO;2 9672245 PMC312953 69

Khandelwal A.

Crowley V. M.

Blagg B. S. J.

Natural product inspired N-terminal Hsp90 inhibitors: from bench to bedside?

Medicinal Research Reviews 2016 36 1 92 118 10.1002/med.21351 2-s2.0-84951569181 26010985 PMC4659773 70

Ngo L. T.

Okogun J. I.

Folk W. R.

21st century natural product research and drug development and traditional medicines

Natural Product Reports 2013 30 4 584 592 10.1039/c3np20120a 2-s2.0-84874974106 23450245 PMC3652390 71

Koehn F. E.

Carter G. T.

The evolving role of natural products in drug discovery

Nature Reviews Drug Discovery 2005 4 206 220 10.1038/nrd1657 2-s2.0-14944383798 15729362 72

Mazaheri M.

Moosavi-Movahedi A. A.

Biodiversity and drug discovery approach to natural medicine 2021 Springer, Cham University of Tehran Science and Humanities Series 10.1007/978-3-030-74326-0_4 73

Mishra S.

A New Generation of Isoform Selective Hsp90 Inhibitors: Targeting the Cytosolic Hsp90 Isoforms, [Ph.D. thesis]

2018 University of Kansas 74 Tian Z. Q.

Liu Y.

Zhang D.

Synthesis and biological activities of novel 17-aminogeldanamycin derivatives

Bioorganic & Medicinal Chemistry 2004 12 20 5317 5329 10.1016/j.bmc.2004.07.053 2-s2.0-4544337503 15388159 75

Mielczarek-Lewandowska A.

Sztiller-Sikorska M.

Osrodek M.

Czyz M.

Hartman M. L.

17-Aminogeldanamycin selectively diminishes IRE1 α -XBP1s pathway activity and cooperatively induces apoptosis with MEK1/2 and BRAFV600E inhibitors in melanoma cells of different genetic subtypes

Apoptosis 2019 24 596 611 10.1007/s10495-019-01542-y 2-s2.0-85064613206 30989459 PMC6598962 76

Waza M.

Adachi H.

Katsuno M.

Minamiyama M.

Tanaka F.

Sobue G.

Alleviating neurodegeneration by an anticancer agent: an Hsp90 inhibitor (17-AAG)

Annals of the New York Academy of Sciences 2006 1086 1 21 34 10.1196/annals.1377.012 2-s2.0-34447540087 17185503 77

Sudhandiran G.

Thomas D.

Dineshbabu V.

Krishnan S.

Theranostic implications of heat shock proteins in idiopathic pulmonary fibrosis

Heat Shock Protein 90 in Human Diseases and Disorders

2019 493 506 10.1007/978-3-030-23158-3_22 78 Bizzarri M.

Giuliani A.

Monti N.

Verna R.

Pensotti A.

Cucina A.

Rediscovery of natural compounds acting via multitarget recognition and noncanonical pharmacodynamical actions

Drug Discovery Today 2020 25 5 920 927 10.1016/j.drudis.2020.02.010 32156546 79

Hall J. A.

Kusuma B. R.

Brandt G. E. L.

Blagg B. S. J.

Cruentaren a binds F1F0 ATP synthase to modulate the Hsp90 protein folding machinery

ACS Chemical Biology 2014 9 4 976 985 10.1021/cb400906e 2-s2.0-84899079603 24450340 PMC4090037 80

Rowlands M. G.

Newbatt Y. M.

Prodromou C.

Pearl L. H.

Workman P.

Aherne W.

High-throughput screening assay for inhibitors of heat-shock protein 90 ATPase activity

Analytical Biochemistry 2004 327 2 176 183 10.1016/j.ab.2003.10.038 2-s2.0-1642503079 15051534 81

Zhou L.

Ren R.

Yang L.

Sudden increase in human infection with avian influenza A(H7N9) virus in China, September-December 2016

Western Pacific Surveillance and Response Journal 2017 8 1 6 14 10.5365/wpsar.2017.8.1.001 2-s2.0-85014481790 28409054 PMC5375094 82

Hu Y.

Zhang X. J.

Yang X. T.

Tang Y. Y.

Hu L. Y.

Zhu D.

Screening technique for heat shock protein 90 inhibitors from natural products

Heat Shock Protein 90 in Human Diseases and Disorders

2019 Springer, Cham 411 439 10.1007/978-3-030-23158-3_19 83

Caldas-Lopes E.

Cerchietti L.

Ahn J. H.

Hsp90 inhibitor PU-H71, a multimodal inhibitor of malignancy, induces complete responses in triple-negative breast cancer models

Proceedings of the National Academy of Sciences 2009 106 20 8368 8373 10.1073/pnas.0903392106 2-s2.0-66249138886 19416831 PMC2688867 84

Anwar M. M.

Shalaby M.

Embaby A. M.

Saeed H.

Agwa M. M.

Hussein A.

Prodigiosin/PU-H71 as a novel potential combined therapy for triple negative breast cancer (TNBC): preclinical insights

Scientific Reports 2020 10, article 14706 10.1038/s41598-020-71157-w PMC7477571 32895397 85

Trendowski M.

PU-H71: an improvement on nature’s solutions to oncogenic Hsp90 addiction

Pharmacological Research 2015 99 202 216 10.1016/j.phrs.2015.06.007 2-s2.0-84937550485 26117427 86

Ramirez D.

Berry L.

Domalaon R.

Brizuela M.

Schweizer F.

Dilipid ultrashort tetrabasic peptidomimetics potentiate novobiocin and rifampicin against multidrug-resistant gram-negative bacteria

ACS Infectious Diseases 2020 6 6 1413 1426 10.1021/acsinfecdis.0c00017 32357292 87

Birar V. C.

Gelis I.

Zuo A.

Blagg B. S. J.

Synthesis of paramagnetic ligands that target the C-terminal binding site of Hsp90

Bioorganic & Medicinal Chemistry Letters 2020 30 16 p. 127303 10.1016/j.bmcl.2020.127303 32631523 PMC8745370 88

McConnell J. R.

Dyson H. J.

McAlpine S. R.

Using NMR to identify binding regions for N and C-terminal Hsp90 inhibitors using Hsp90 domains

RSC Medicinal Chemistry 2021 12 3 410 415 10.1039/D0MD00387E 33898992 PMC8044635 89

Zuehlke A. D.

Moses M. A.

Neckers L.

Heat shock protein 90: its inhibition and function

Philosophical Transactions of the Royal Society, B: Biological Sciences

2018 373 1738 p. 20160527 10.1098/rstb.2016.0527 2-s2.0-85037054667 29203712 PMC5717527 90

Yoshikawa C.

Ishida H.

Ohashi N.

Itoh T.

Synthesis of a coumarin-based PPAR γ fluorescence probe for competitive binding assay

International Journal of Molecular Sciences 2021 22, article 4034 10.3390/ijms22084034 PMC8070791 33919837 91

Centenera M. M.

Selth L. A.

Ebrahimie E.

Butler L. M.

Tilley W. D.

New opportunities for targeting the androgen receptor in prostate cancer

Cold Spring Harbor Perspectives in Medicine 2018 8 12 p. a030478 10.1101/cshperspect.a030478 2-s2.0-85044995760 29530945 PMC6280715 92

Donnelly A.

Blagg B.

Novobiocin and additional inhibitors of the Hsp90 C-terminal nucleotide- binding pocket

Current Medicinal Chemistry 2008 15 26 2702 2717 10.2174/092986708786242895 2-s2.0-61649098007 18991631 PMC2729083 93

Lin C.-C.

Design and Synthesis of Macrocyclic Peptides with Anti-Cancer Potency

2013 San Diego State University ProQuest Dissertations Publishing 94

Liu Y.

Zhang G.

Wang H.

Novel cyclic pentapeptide H-15 induces differentiation and inhibits proliferation in murine melanoma B16 cells

Oncology Letters 2016 11 2 1251 1255 10.3892/ol.2015.4025 2-s2.0-84954350250 26893727 PMC4734122 95

Zhang G.

Liu S.

Liu Y.

A novel cyclic pentapeptide, H-10, inhibits B16 cancer cell growth and induces cell apoptosis

Oncology Letters 2014 8 1 248 252 10.3892/ol.2014.2121 2-s2.0-84901241592 24959255 PMC4063637 96

Wang X.

Yao X.

Fan S.

A LY-15, a novel cyclic pentapeptide that inhibits B16 cell proliferation and migration and induces cell apoptosis

Oncology Letters 2018 15 4 5887 5892 10.3892/ol.2018.8023 2-s2.0-85042655409 29552219 PMC5840655 97

Yun C. W.

Kim H. J.

Lim J. H.

Lee S. H.

Heat shock proteins: agents of cancer development and therapeutic targets in anti-cancer therapy

Cell 2020 9 1 p. 60 10.3390/cells9010060 PMC7017199 31878360 98

Morra G.

Neves M. A. C.

Plescia C. J.

Dynamics-based discovery of allosteric inhibitors: selection of new ligands for the C-terminal domain of Hsp90

Journal of Chemical Theory and Computation 2010 6 9 2978 2989 10.1021/ct100334n 2-s2.0-77956566842 26616092 PMC7575213 99

Brandt G. E. L.

Blagg B. S. J.

Alternate strategies of Hsp90 modulation for the treatment of cancer and other diseases

Current Topics in Medicinal Chemistry 2009 9 15 1447 1461 10.2174/156802609789895683 2-s2.0-74249108175 19860731 PMC2853258 100

Penkler D. L.

Atilgan C.

Tastan Bishop Ö.

Allosteric modulation of human Hsp90 α conformational dynamics

Journal of Chemical Information and Modeling 2018 58 2 383 404 10.1021/acs.jcim.7b00630 2-s2.0-85042693849 29378140 101

Astl L.

Stetz G.

Verkhivker G. M.

Allosteric mechanism of the Hsp90 chaperone interactions with cochaperones and client proteins by modulating communication spines of coupled regulatory switches: integrative atomistic modeling of Hsp90 signaling in dynamic interaction networks

Journal of Chemical Information and Modeling 2020 60 7 3616 3631 10.1021/acs.jcim.0c00380 32519853 102

Stetz G.

Astl L.

Verkhivker G. M.

Exploring mechanisms of communication switching in the Hsp90-Cdc37 regulatory complexes with client kinases through allosteric coupling of phosphorylation sites: perturbation-based modeling and hierarchical community analysis of residue interaction networks

Journal of Chemical Theory and Computation 2020 16 7 4706 4725 10.1021/acs.jctc.0c00280 32492340 103

Stetz G.

Tse A.

Verkhivker G. M.

Dissecting structure-encoded determinants of allosteric cross-talk between post-translational modification sites in the Hsp90 chaperones

Scientific Reports 2018 8 1 10.1038/s41598-018-25329-4 2-s2.0-85046435971 PMC5932063 29720613 104

Kumar R.

Moche M.

Winblad B.

Pavlov P. F.

Combined x-ray crystallography and computational modeling approach to investigate the Hsp90 C-terminal peptide binding to FKBP51

Scientific Reports 2017 7 1 1 17 10.1038/s41598-017-14731-z 2-s2.0-85032498276 29079741 PMC5660230 105

Morra G.

Potestio R.

Micheletti C.

Colombo G.

Corresponding functional dynamics across the Hsp90 chaperone family: insights from a multiscale analysis of MD simulations

PLoS Computational Biology 2012 8 3, article e1002433 10.1371/journal.pcbi.1002433 2-s2.0-84861005184 22457611 PMC3310708 106

Azizian H.

Bahrami H.

Pasalar P.

Amanlou M.

Molecular modeling of Helicobacter pylori arginase and the inhibitor coordination interactions

Journal of Molecular Graphics & Modelling 2010 28 7 626 635 10.1016/j.jmgm.2009.12.007 2-s2.0-76549123580 20080052 107

Tripuraneni N. S.

Azam M. A.

A combination of pharmacophore modeling, atom-based 3D-QSAR, molecular docking and molecular dynamics simulation studies on PDE4 enzyme inhibitors

Journal of Biomolecular Structure & Dynamics 2016 34 11 2481 2492 10.1080/07391102.2015.1119732 2-s2.0-84963605329 26587754 108

Pourbasheer E.

Bazl R.

Amanlou M.

Molecular docking and 3D-QSAR studies on the MAPKAP-K2 inhibitors

Medicinal Chemistry Research 2014 23 5 2252 2263 10.1007/s00044-013-0820-0 2-s2.0-84898812368 109

Oyebamiji A. K.

Mutiu O. A.

Amao F. A.

Dataset on in-silico investigation on triazole derivatives via molecular modelling approach: a potential glioblastoma inhibitors

Data in Brief 2021 34, article 106703 10.1016/j.dib.2020.106703 PMC7797363 33457478 110

Ballante F.

Caroli A.

Wickersham R. B.

Ragno R.

Hsp90 inhibitors, part 1: definition of 3-D QSAutogrid/R models as a tool for virtual screening

Journal of Chemical Information and Modeling 2014 54 3 956 969 10.1021/ci400759t 2-s2.0-84896965995 24564321 111

Caroli A.

Ballante F.

Wickersham R. B.

Corelli F.

Ragno R.

Hsp90 inhibitors, part 2: combining ligand-based and structure-based approaches for virtual screening application

Journal of Chemical Information and Modeling 2014 54 970 977 10.1021/ci400760a 2-s2.0-84896952871 24555544 PMC3985681 112

Ugbaja S.

Lawal I.

Kumalo H.

Targets M. L.-C. D.

Alzheimer’s disease and β -secretase inhibition: an update with a focus on computer-aided inhibitor design

Current Drug Targets 2022 23 3 266 285 10.2174/1389450122666210809100050 34370634 113

Kesharwani A.

Chaurasia D. K.

Katara P.

Repurposing of FDA approved drugs and their validation against potential drug targets for Salmonella enterica through molecular dynamics simulation

Journal of Biomolecular Structure & Dynamics 2021 1 17 10.1080/07391102.2021.1880482 33525976 114

Roy K.

Singh S.

Diversity, A. S.-M. & 2011, undefined. Integration-mediated prediction enrichment of quantitative model for Hsp90 inhibitors as anti-cancer agents: 3D-QSAR study

Molecular Diversity 2011 15 477 489 10.1007/s11030-010-9269-y 2-s2.0-80051556999 20740314 115

Kumar S.

Kaushik A.

Narasimhan B.

Molecular docking, synthesis and biological significance of pyrimidine analogues as prospective antimicrobial and antiproliferative agents

BMC Chemistry 2019 13 1 17 31384832 10.1186/s13065-019-0601-z PMC6661814 116

Cai Z.

Luo F.

Wang Y.

Li E.

Huang Y.

Protein pKa prediction with machine learning ACS Omega

2021 6 50 34823 34831 10.33774/CHEMRXIV-2021-7GK5L 34963965 PMC8697405 117

Watanabe Y.

Fukuyoshi S.

Kato K.

Investigation of substrate recognition for cytochrome P450 1A2 mediated by water molecules using docking and molecular dynamics simulations

Journal of Molecular Graphics & Modelling 2017 74 326 336 10.1016/j.jmgm.2017.04.006 2-s2.0-85018435496 28475969 118

Guterres H.

Park S.-J.

Jiang W.

Im W.

Ligand-binding-site refinement to generate reliable Holo protein structure conformations from Apo structures

Journal of Chemical Information and Modeling 2021 61 535 546 10.1021/acs.jcim.0c01354 33337877 PMC7856192 119

Sánchez L.

Avilés M. N. C.

Pérez A. L. D Luna B. E. G.

García R. A. V.

Pérez C. D.

Análisis estructural de la superóxido dismutasa ChrC de Ochrobactrum tritici

Visum Mundi 2019 3 173 179 120 Nagaraju M.

McGowan L. C.

Hamelberg D.

Cyclophilin a inhibition: targeting transition-state-bound enzyme conformations for structure-based drug design

Journal of Chemical Information and Modeling 2013 53 2 403 410 10.1021/ci300432w 2-s2.0-84874431865 23312027 121

Roughley S.

Wright L.

Brough P.

Massey A.

Hubbard R. E.

Hsp90 Inhibitors and Drugs from Fragment and Virtual Screening

2011 Springer 10.1007/128_2011_181 21647838 122 Olotu F.

Delving the Cancer World : Combinatorial In Silico Modeling and Investigations of Crucial Oncogenic Targets and Potential Therapeutic Approaches in Cancer Treatment

2019 University of KwaZulu-Natal 123 Athar M.

Lone M.

Khedkar V. M.

Jha P. C.

Pharmacophore model prediction, 3D-QSAR and molecular docking studies on vinyl sulfones targeting Nrf2-mediated gene transcription intended for anti-Parkinson drug design

Journal of Biomolecular Structure & Dynamics 2016 34 1282 1297 10.1080/07391102.2015.1077343 2-s2.0-84984991233 26222438 124

Long Q.

Lin T. Y.

Huang Y.

Image-guided photo-therapeutic nanoporphyrin synergized HSP90 inhibitor in patient-derived xenograft bladder cancer model

Nanomedicine: Nanotechnology, Biology and Medicine

2018 14 789 799 10.1016/j.nano.2017.12.014 2-s2.0-85041806343 29317342 PMC5898975 125

Nazar A.

Abbas G.

Azam S. S.

Deciphering the inhibition mechanism of under trial Hsp90 inhibitors and their analogues: a comparative molecular dynamics simulation

Journal of Chemical Information and Modeling 2020 60 8 3812 3830 10.1021/acs.jcim.9b01134 32659088 126

Gioia D.

Bertazzo M.

Recanatini M.

Masetti M.

Cavalli A.

Dynamic docking: a paradigm shift in computational drug discovery

Molecules 2017 22 p. 2029 10.3390/molecules22112029 2-s2.0-85036552248 PMC6150405 29165360 127

Roy S. S.

Kapoor M.

In silico identification and analysis of the binding site for aminocoumarin type inhibitors in the C-terminal domain of Hsp90

Journal of Molecular Graphics & Modelling 2018 84 215 235 10.1016/j.jmgm.2018.06.016 2-s2.0-85050145388 30031951 128

Visscher M.

Arkin M. R.

Dansen T. B.

Covalent targeting of acquired cysteines in cancer

Current Opinion in Chemical Biology 2016 30 61 67 10.1016/j.cbpa.2015.11.004 2-s2.0-84949008215 26629855 PMC4731306 129

De Cesco S.

Kurian J.

Dufresne C.

Mittermaier A. K.

Moitessier N.

Covalent inhibitors design and discovery European Journal of Medicinal Chemistry

2017 138 96 114 10.1016/j.ejmech.2017.06.019 2-s2.0-85021168826 28651155 130

Garg G.

Zhao H.

Blagg B. S. J.

Design, synthesis and biological evaluation of alkylamino biphenylamides as Hsp90 C-terminal inhibitors

Bioorganic & Medicinal Chemistry 2017 25 2 451 457 10.1016/j.bmc.2016.11.030 2-s2.0-85007251504 27914946 PMC5214847 131

Al-Sha’er M. A.

Mansi I.

Khanfar M.

Abudayyh A.

Discovery of new heat shock protein 90 inhibitors using virtual co-crystallized pharmacophore generation

Journal of Enzyme Inhibition and Medicinal Chemistry

2016 31 64 77 10.1080/14756366.2016.1218485 2-s2.0-84984614729 27569779 132

Baby S. T.

Sharma S.

Enaganti S.

Cherian P. R.

Molecular docking and pharmacophore studies of heterocyclic compounds as heat shock protein 90 (Hsp90) inhibitors

Bioinformation 2016 12 3 149 155 10.6026/97320630012149 28232775 PMC5289218 133

Vettoretti G.

Moroni E.

Sattin S.

Molecular dynamics simulations reveal the mechanisms of allosteric activation of Hsp90 by designed ligands

Scientific Reports 2016 6, article 23830 10.1038/srep23830 2-s2.0-84962822408 PMC4817115 27032695 134

Abbasi M.

Sadeghi-Aliabadi H.

Amanlou M.

Prediction of new Hsp90 inhibitors based on 3,4-isoxazolediamide scaffold using QSAR study, molecular docking and molecular dynamic simulation

DARU Journal of Pharmaceutical Sciences 2017 25, article 17 10.1186/s40199-017-0182-0 2-s2.0-85021626571 PMC5493083 28666484 135

Terracciano S.

Russo A.

Chini M. G.

Discovery of new molecular entities able to strongly interfere with Hsp90 C-terminal domain

Scientific Reports 2018 8, article 1709 10.1038/s41598-017-14902-y 2-s2.0-85041134440 PMC5786060 29374167 136

Taipale M.

Jarosz D. F.

Lindquist S.

HSP90 at the hub of protein homeostasis: emerging mechanistic insights

Nature Reviews Molecular Cell Biology 2010 11 7 515 528 10.1038/nrm2918 2-s2.0-77953916528 20531426 137

Whitesell L.

Lindquist S. L.

HSP90 and the chaperoning of cancer Nature Reviews Cancer

2005 5 10 761 772 10.1038/nrc1716 2-s2.0-25844519550 16175177 138

Bhatia S.

Diedrich D.

Frieg B.

Targeting HSP90 dimerization via the C terminus is effective in imatinib-resistant CML and lacks the heat shock response

Blood 2018 132 3 307 320 10.1182/blood-2017-10-810986 2-s2.0-85050083167 29724897 PMC6225350 139

Dai J.

Zhu M.

Qi X.

Fungal mycotoxin penisuloxazin A, a novel C-terminal Hsp90 inhibitor and characteristics of its analogues on Hsp90 function related to binding sites

Biochemical Pharmacology 2020 182 p. 114218 10.1016/j.bcp.2020.114218 32949584 140

Sepehri B.

Ghavami R.

Towards the in-silico design of new HSP90 inhibitors: molecular docking and 3D-QSAR CoMFA studies of tetrahydropyrido [4, 3-d] pyrimidine derivatives as HSP90 inhibitors

Medicinal Chemistry 2018 14 5 439 450 10.2174/1573406414666180321151029 2-s2.0-85049845661 29564982 141

Abbasi M.

Sadeghi-Aliabadi H.

Amanlou M.

3D-QSAR, molecular docking, and molecular dynamic simulations for prediction of new Hsp90 inhibitors based on isoxazole scaffold

Journal of Biomolecular Structure and Dynamics 2017 36 1463 1478 10.1080/07391102.2017.1326319 2-s2.0-85019540823 28482755 142

Mettu A.

Talla V.

Bajaj D. M.

Subhashini N. J. P.

Design, synthesis, and molecular docking studies of novel pyrazolyl 2-aminopyrimidine derivatives as HSP90 inhibitors

Archiv der Pharmazie 2019 352 10 p. 1900063 10.1002/ardp.201900063 2-s2.0-85070738590 31411362 143

Rampogu S.

Parate S.

Parameswaran S.

Natural compounds as potential Hsp90 inhibitors for breast cancer-pharmacophore guided molecular modelling studies

Computational Biology and Chemistry 2019 83 p. 107113 10.1016/j.compbiolchem.2019.107113 2-s2.0-85071659434 31493740 144

Olasupo S. B.

Uzairu A.

Shallangwa G. A.

Uba S.

Profiling the antidepressant properties of phenyl piperidine derivatives as inhibitors of serotonin transporter (SERT) via cheminformatics modeling, molecular docking and ADMET predictions

Scientific African 2020 9, article e00517 10.1016/j.sciaf.2020.e00517 145

Alam S.

Nasreen S.

Ahmad A.

Darokar M. P.

Khan F.

Detection of natural inhibitors against human liver cancer cell lines through QSAR, molecular docking and ADMET studies

Current Topics in Medicinal Chemistry 2021 21 686 695 10.2174/1568026620666201204155830 33280598 146

He Q.

Chu H.

Wang Y.

In silico design novel vibsanin B derivatives as inhibitor for heat shock protein 90 based on 3D-QSAR, molecular docking and molecular dynamics simulation

Journal of Biomolecular Structure & Dynamics 2020 38 14 4313 4324 10.1080/07391102.2019.1671900 31542999 147

Abdizadeh R.

Hadizadeh F.

Abdizadeh T.

QSAR analysis of coumarin-based benzamides as histone deacetylase inhibitors using CoMFA, CoMSIA and HQSAR methods

Journal of Molecular Structure 2020 1199 p. 126961 10.1016/j.molstruc.2019.126961 148

Vardhan S.

Sahoo S. K.

In silico ADMET and molecular docking study on searching potential inhibitors from limonoids and triterpenoids for COVID-19

Computers in Biology and Medicine 2020 124 p. 103936 10.1016/j.compbiomed.2020.103936 32738628 PMC7386496 149

Godoy-Castillo C.

Bravo-Acuña N.

Arriagada G.

Faunes F.

León R.

Soto-Delgado J.

Identification of the naphthoquinone derivative inhibitors binding site in heat shock protein 90: an induced-fit docking, molecular dynamics and 3D-QSAR study

Journal of Biomolecular Structure and Dynamics 2021 39 16 5977 5987 10.1080/07391102.2020.1803134 32799638 150

Tomašič T.

Durcik M.

Keegan B. M.

Discovery of novel Hsp90 C-terminal inhibitors using 3D-pharmacophores derived from molecular dynamics simulations

International Journal of Molecular Sciences 2020 21 p. 6898 10.3390/ijms21186898 PMC7555175 32962253 151

Mak O. W.

Sharma N.

Reynisson J.

Leung I. K. H.

Discovery of novel Hsp90 C-terminal domain inhibitors that disrupt co-chaperone binding

Bioorganic & Medicinal Chemistry Letters 2021 38 p. 127857 10.1016/j.bmcl.2021.127857 33609661 152

Shadrack D. M.

Swai H. S.

Hassanali A.

A computational study on the role of water and conformational fluctuations in Hsp90 in response to inhibitors

Journal of Molecular Graphics & Modelling 2020 96 p. 107510 10.1016/j.jmgm.2019.107510 31877402 153

Shadrack D. M.

Swai H. S.

Hassanali A.

A Computational Study on the Role of Solvents and Conformational Fluctuation of Macromolecules towards Drug Design, [Ph.D. thesis]

2020 The Nelson Mandela African Institution of Science and Technology 154

Tan Q. J.

Romero R. A.

Jenn D. C.

Target recognition with adaptive waveforms in cognitive radar using practical target RCS responses 2018 IEEE Radar Conference (RadarConf18) 2018 Oklahoma City, OK, USA 609 611 10.1109/RADAR.2018.8378628 2-s2.0-85049943517 155

Bekker G.-J.

Araki M.

Oshima K.

Okuno Y.

Kamiya N.

Exhaustive search of the configurational space of heat-shock protein 90 with its inhibitor by multicanonical molecular dynamics based dynamic docking

Journal of Computational Chemistry 2020 41 17 1606 1615 10.1002/jcc.26203 32267975 156

Cai W.

Wu J.

Sun Y.

Synthesis, evaluation, molecular dynamics simulation and targets identification of novel pyrazole-containing imide derivatives

Journal of Biomolecular Structure and Dynamics 2020 39 2176 2188 10.1080/07391102.2020.1745284 32189577 157

Dike P. P.

Bhowmick S.

Eldesoky G. E.

Wabaidur S. M.

Patil P. C.

Islam M. A.

In silico identification of small molecule modulators for disruption of Hsp90–Cdc37 protein–protein interaction interface for cancer therapeutic application

Journal of Biomolecular Structure and Dynamics 2020 40 5 2082 2098 10.1080/07391102.2020.1835714 33095103 158

Wang X.

Dong H.

Qin Q.

QSAR models on aminopyrazole-substituted resorcylate compounds as Hsp90 inhibitors

Journal of Computing Science and Engineering 2020 48 1146 1156 159

Tomašič T.

Zubrienė A.

Skok Ž.

Selective DNA gyrase inhibitors: multi-target in silico profiling with 3D-pharmacophores

Pharmaceuticals 2021 14 p. 789 10.3390/ph14080789 PMC8400042 34451886 Figure 1 Diagram representing the three domains of HSP90 protein (crystal structure of HSP90 dimer with PDB ID: 2CG9 while the red dashed cycle highlights an ATP-binding pocket) as adopted from open-source journals [ 62 , 63 ]. Figure 2 2D structures of some of the failed N-terminal HSP90 inhibitors at the clinical trials. Figure 3 2D structure of HSP90 natural inhibitor, geldanamycin (GA), and derivatives. Figure 4 Computational modeling and drug design methods employed for HSP90. Figure 5 2D structures of HSP90 inhibitors obtained and analysed via

📖 中文全文 Chinese Full Text

中文

# 计算机辅助药物发现方法开发热休克蛋白90(HSP90)抑制剂作为抗癌药物:近五年综述

## 摘要

癌症是由不同解剖部位细胞不受控制的异常生长引起的疾病。2018年,据预测全球癌症负担将上升至1810万新发病例和960万例死亡。抗癌化合物(通常称为化疗药物)在近期癌症研究中备受关注。这些药物通过多种生物过程靶向细胞生命周期的不同阶段。开发抗癌药物的最大障碍之一是传统化疗同时影响正常细胞和癌细胞,导致显著的副作用。近年来,新药开发方法的进步以及靶向原子间和分子间配体相互作用位点的预测已被证明是有益的。这促使了进一步研究和开发新型化学物种作为针对特定癌症类型的优选治疗化合物。鉴定对癌症具有高选择性和特异性的新药物分子是治疗和管理该疾病的先决条件。HSP90在癌症患者中过表达,且HSP90触发不稳定的有害激酶功能,从而促进致癌作用。因此,开发具有高选择性和特异性的强效HSP90抑制剂变得非常迫切。由于构象动态性,HSP90作为分子伴侣和辅伴侣的活性非常复杂,这可能是没有HSP90药物通过临床试验的原因之一。尽管如此,由于靶向N端三磷酸腺苷(ATP)口袋的竞争性抑制,HSP90调节似乎是优选策略。因此,本研究综述了开发HSP90抑制剂作为抗癌药物所采用的各种计算模型。我们建议对HSP90的三个不同结构域进行先进计算建模的广泛研究,以设计具有最小脱靶效应的强效有效抑制剂。

**关键词:** 热休克蛋白90;计算机辅助药物设计;分子对接;分子动力学模拟;癌症治疗

## 1. 引言

癌症是一组以异常细胞不受控制地发展、生长和扩散至正常边界之外为特征的疾病。根据世界卫生组织(WHO)的数据,癌症是全球第二大死亡原因,2018年造成960万例死亡,即每六例死亡中就有一例[1-3]。特定癌症的反复激增由多种因素引起,包括人口增长、老龄化以及可能与社会或经济相关的癌症决定因素的变化[4]。除了上述问题外,与生活方式和身体行为相关的因素,如营养、酒精使用和生理行为,都与癌症风险和负担相关[1,5]。癌症也可描述为不同解剖部位细胞不受控制的异常生长引起的一组疾病[6]。在新细胞产生而旧细胞死亡的自然、无害的细胞分裂循环中,癌症扰乱了细胞分裂,导致旧细胞持续存在而新细胞过早形成,从而产生可见为异常细胞增殖的肿瘤,引起肿胀。值得注意的是,某些恶性肿瘤(如白血病)不形成肿瘤,而是在血流中被发现[6]。

肿瘤分为两种:良性和恶性。良性肿瘤是非癌性的,但可以生长。然而,此类肿瘤不会侵袭性生长,是非侵袭性的。此外,良性肿瘤通过手术成功切除后不会复发。例如,如果发现良性肿瘤压迫大脑或其他重要器官,结果可能是致命的[7]。癌性生长被称为恶性肿瘤,可以侵袭性和不受控制地复制[8]。恶性肿瘤的另一个特征性特征是其转移能力,即侵入身体其他部位的过程[9]。癌症进展取决于外部和内部因素,包括环境因素。外部因素包括香烟/烟草产品、辐射和感染性物种。另一方面,基因突变、免疫系统激素异常和代谢突变是癌症的一些内部病因[10]。烟草使用每年全球造成700万人死亡,其中三分之二的使用者预计将死于该疾病。吸烟占全球癌症死亡人数的22%[11]。据预测,每周饮用一瓶葡萄酒会使不吸烟男性和女性的绝对终生癌症风险分别增加1.0%和1.4%[11]。每周一瓶葡萄酒的平均绝对癌症风险增加量相当于男性每周吸5支香烟或女性每周吸10支香烟[11]。发达国家超过20%的癌症死亡由感染引起,包括乙型和丙型肝炎病毒以及人乳头瘤病毒[12]。遗传异常被认为是5-10%癌症的原因。2018年,据预测全球癌症负担将上升至1810万新发病例和960万例死亡。在全球范围内,五分之一的男性和一分之六的女性在一生中会患癌症,八分之一的男性和一十一分之一的女性将死于该疾病。五年患病率(即癌症诊断后五年内存活的人数)预计全球为4380万人[13]。

大多数常见癌症由于突变的缓慢积累而数月甚至数年未被发现。DNA突变通常以每次细胞分裂每2000万个基因中发生1个的速率出现[6]。由于持续的研究压力,研究人员和制药行业面临着设计和开发用于治疗和管理各种致命癌症的高选择性和高效药物的巨大压力[1]。抗癌化合物(通常称为化疗药物)在近期癌症研究中备受关注[14]。这些药物通过多种生物过程靶向细胞生命周期多个阶段的细胞。近年来,新药开发方法的进步以及靶向原子间和分子间配体相互作用位点的预测已被证明是有益的。这促使了进一步研究和开发新型化学物种作为针对特定癌症种类的优选治疗化合物[15]。开发抗癌药物的最重大障碍之一是传统化疗同时影响正常细胞和癌细胞,导致显著的副作用[1]。因此,鉴定对癌症具有高选择性和特异性的新药物分子是治疗和管理该疾病的先决条件[1,15]。

在新型HSP90抑制剂设计和开发的近期实验数据,特别是计算数据方面存在文献空白。因此,本综述提供了过去五年(2016年至2021年)开发HSP90抑制剂作为抗癌药物所采用的各种计算方法的深入更新。

## 2. 药物发现的一些癌症治疗靶点概述

几十年来,癌症患者的治疗选择有限。这包括手术、小实体瘤的放疗以及血液癌和实体转移性肿瘤的化疗[16]。人蛋白酪氨酸激酶(PTKs)对人类癌症发展有重要贡献,已成为癌症预防的潜在靶点[17,18]。发现对癌细胞内靶向蛋白具有高选择性和特异性的小分子物种似乎是癌症治疗中的卓越策略。表皮生长因子受体(EGFR)的过表达已在大多数癌症中被检测到。因此,靶向EGFR(与表皮生长因子结合的蛋白)已成为最有效的癌症疗法之一。当EGFR过表达时,会发生快速细胞分裂[19]。这些EGFR蛋白在负责细胞存活和分裂的信号传输网络中发挥重要作用。多年来,发现EGFR酪氨酸激酶的小分子抑制剂吸引了制药行业更多的资源[20]。EGFR过表达与多种癌症的不良临床结果相关,包括头颈部癌、喉癌、食管癌、胃癌、胰腺癌、结肠癌、肾细胞癌、膀胱癌、乳腺癌、卵巢癌、宫颈癌、前列腺癌、非小细胞肺(NSCL)癌、乳头状甲状腺癌、黑色素瘤和胶质瘤[21]。近年来,已鉴定出多种EGFR酪氨酸激酶抑制剂(TKIs),包括卡奈替尼、厄洛替尼和吉非替尼。然而,由于对现有抑制剂观察到一些耐药性,目前仍在进行发现更有效和强效的EGFR抑制剂的研究[22]。

研究人员还靶向细胞周期蛋白依赖性蛋白激酶(CDKs)用于癌症治疗。CDKs是负责调节大多数通过细胞周期的传输机制表达的基本蛋白[23]。此外,CDKs对其他细胞周期机制(如神经元功能、代谢活动和基因转录)有巨大贡献[23]。细胞周期蛋白依赖性激酶(CDKs)是负责丝氨酸和苏氨酸残基上蛋白磷酸化的丝氨酸/苏氨酸激酶[24,25]。研究表明,在某些恶性肿瘤中观察到CDKs的过度活性或CDK抑制蛋白的功能失调,因此作为抗癌药物的有前景的靶点[26]。因此,开发和设计靶向CDK过表达的药物变得势在必行。一些CDK抑制剂,如seliciclib(一种作为抗癌治疗测试的细胞周期蛋白依赖性激酶(CDK)抑制剂),已在II期临床试验中[27,28]。

另一个癌症治疗靶点是聚(ADP-核糖)聚合酶(PARP),这是一种常见的核酶,是脱氧核糖核酸(DNA)损伤的标志物。DNA修复酶聚(ADP-核糖)聚合酶(PARP)在哺乳动物细胞核中表现出显著和丰富的表达。由于其结构和迷人的抑制特性,研究人员对PARP非常感兴趣[29]。美国食品药品监督管理局(FDA)已批准多种PARP抑制剂,靶向多种癌症类型[30]。这些PARP抑制剂还通过延迟单链断裂修复来发挥放射增敏剂的作用。这些抑制剂随后促进双链断裂产生,这一假设已在PARP抑制剂和电离辐射治疗的许多临床前组合模型中使用[30]。此外,该酶通过促进DNA、组蛋白和几种DNA修复酶的ADP-核糖基化来辅助DNA修复[31]。对于包括癌症在内的多种疾病,PARP一直是基于结构的药物设计工作的密集靶点。一些常见的PARP靶向抑制剂例子包括处于I期试验的iniparib,而BMN-673和olaparib处于II期临床试验[32]。

然而,虽然放疗和化疗等传统癌症治疗是有效的,但热休克蛋白90(HSP90)是一个有前景的癌症治疗靶点。胰腺癌干细胞(CSCs)参与促进胰腺癌侵袭和转移。CSCs受蛋白酶激活受体1(PAR1)影响,通过诱导Aspc-1细胞中的CSC样特性。因此,据报道强力霉素抑制PAR1,有效抑制胰腺癌细胞的CSC样特性和FAK/PI3K/AKT通路的激活,并增强5 FU的治疗效果[33]。研究了蛋白酶激活受体2(PAR2)在吉非替尼耐药中的作用,发现当非小细胞肺癌(NSCLC)细胞或肿瘤组织表现出吉非替尼耐药时,其表达显著增加。因此抑制PAR2,表明对吉非替尼耐药有逆转作用,即吉非替尼调节吉非替尼敏感和耐药NSCLC细胞中的EGFR反式激活、细胞活力、迁移和凋亡。研究表明,与单独使用吉非替尼相比,吉非替尼和PAR2(P2pal-185)的组合显著阻断ERK磷酸化和上皮-间充质转化(EMT)。PAR2被提议作为克服NSCLC中吉非替尼耐药的新靶点和通路[34]。

信号转导和转录激活因子3(Stat 3)被引入作为乳腺癌治疗的有前景的靶点。新型Stat 3抑制剂Statmp-151在乳腺癌细胞系MCF-7和MDA-MB-231以及小鼠乳腺癌细胞系4T1中进行了研究。结果表明,Statmp-151可能是治疗乳腺癌的潜在药物[35]。

### 2.1. 热休克蛋白概述

热休克蛋白(HSPs)是一组非常丰富、必需且进化保守的分子伴侣。它们在促进蛋白质变性(如缺氧、无氧、高温、药物和其他化合物)的刺激下维持细胞稳态。HSP分子量、亚组总结于表1。HSPs根据其分子量进行分类,HSP70是分子量为70 kDa的HSP亚组。HSP27、HSP40、HSP60、HSP70、HSP90和大HSF是基于分子量分类的主要分组。由于HSP成员数量增加且名称令人困惑,鉴定这些大型重叠蛋白质的需求很大,因为它们在某些情况下可能具有高度相似性,但在其他情况下差异很大。

HSPs被认为在促进癌症生长和扩散的分子通路中发挥关键作用。HSPs还可作为癌症诊断和疾病进展评估的生物标志物或癌症治疗的靶点,具有临床应用价值。HSPs可用作癌症治疗的治疗靶点,从而开发新的化疗药物。HSP70和HSP90是HSP家族中研究最深入的两个成员。GRP78(HSP70家族的成员)和HSP90是目前正在开发的大多数新癌症药物的靶点。其中一些药物已在临床试验中被测试,并证明在体外对癌细胞和体内动物异种移植模型中有效。目前尚不清楚为什么癌细胞需要与正常细胞不同量的HSPs,更多的知识可能导致发现治疗窗口,以开发对癌症更有效和毒性更低的HSP抑制剂。HSP抑制剂可能引起难以治疗的严重器官特异性毒性(肝脏或眼毒性)。通过鉴定癌细胞特异性的HSP功能,可能规避HSP抑制剂的器官特异性毒性。一些HSP抑制剂可能对癌症无效。为了控制细胞过程,HSP家族成员在信号网络中相互通讯和协调。当一种HSP被抑制时,其他HSPs可能过表达以补偿单一HSP抑制剂的抑制作用。例如,抑制HSP90导致HSP27和其他HSPs的过表达,导致热休克反应。可以进一步改进人类HSPs的命名和分类,以支持HSPs的功能描述和药物发现。

HSP90过表达的肿瘤包括胰腺癌、卵巢癌、乳腺癌、肺癌、子宫内膜癌、口咽鳞状细胞癌和多发性骨髓瘤[36-38]。在肺癌、食管癌、膀胱癌、黑色素瘤和白血病中,HSP90的高表达已被证明是预后不良的预测因子[39,40]。HSP90是一个潜在的治疗靶点,可抑制肿瘤发展和进展,因为它在癌症生物学中发挥关键作用,许多HSP90抑制剂已在临床试验中被探索。作为抗癌药物,HSP90抑制剂具有多种优势,因为许多信号蛋白是HSP90客户蛋白,HSP90抑制剂可同时作用于多种信号通路。因此,与仅使用一种靶蛋白的治疗相比,抗HSP90治疗不太可能导致肿瘤细胞存活。HSP90是真核生物中最丰富的伴侣蛋白,约占细胞质蛋白的1%至2%[41]。在各种细胞内和细胞外应激情况下,HSP90支持新生成蛋白的正确折叠并帮助重折叠变性蛋白[42]。

热休克蛋白通常是在生命许多方面发挥关键作用的分子伴侣。它们参与错误折叠蛋白的重折叠,有助于维持细胞稳态。热休克因子(HSF)在环境应激反应中被激活并结合热休克元件(HSEs),增加HSP翻译,从而高水平合成HSP[43]。热休克蛋白90(HSP90)分子伴侣广泛存在于真核生物和原核生物中,在维持细胞稳定性方面发挥重要作用[44,45]。依赖ATP,HSP90参与靶蛋白的激活、正确折叠、组装、运输、构象维持和降解[46,47]。然而,这些靶蛋白包括许多升高或突变的致癌蛋白,包括p53和hTERT,其中一些与癌症特征相关[48,49]。这些在肿瘤发生、生长、侵袭和转移中的客户蛋白使HSP90成为有吸引力的癌症治疗靶点[50,51]。

在癌症患者中观察到HSP90的过表达,并且已观察到HSP90触发不稳定的有害激酶功能,从而促进致癌作用[52,53]。因此,开发并合成了高效抑制剂用于治疗HSP90相关癌症。因此,抑制剂与HSP90的化学结合导致客户蛋白分解,引起蛋白质错误折叠,从而阻止肿瘤发生并逃避耐药性的缺点[54]。在这种情况下,涉及多种致癌通路的癌蛋白被同时消除,产生组合性肿瘤攻击并显著提高癌症治愈率。因此,HSP90抑制剂在肿瘤治疗中具有积极的应用[43,55]。

## 3. HSP90的结构描述

HSP90蛋白以同源二聚体形式存在,其中单个单体包含三个结构域:N端结构域、中间结构域和C端结构域(图1)[56]。N端结构域是GHKL超家族的成员,构成HSP90的主要ATP酶结构域。N端结构域具有带变化大小(长度)的超荷连接部分,与旋转酶、拓扑异构酶和组氨酸激酶具有相似结构。它还构成与中间结构域连接的其他异构体和物种[56-59]。中间结构域在水解三磷酸腺苷(ATP)中发挥重要功能[60]。C端负责形成HSP90的重要二聚体界面。Met-Glu-Glu-Val-Asp(MEEVD)基序是具有四肽重复(TPR)结构域的辅亚组的子集的基本相互作用位点,也存在于C端[61]。

生物学研究表明,多肽是同源二聚体,其中每个单体由三个柔性相关的保守区域(N-、M-和C端结构域)组成。N端结构域包括核苷酸(ATP和ADP)和药物结合裂缝,通常称为"Bergerat折叠",在ATP结合时被氨基酸的分子"塞子"关闭,当二磷酸腺苷(ADP)连接时打开。中间结构域抗蛋白水解,设计用于结合ATP的客户蛋白、一些辅伴侣和π-磷酸[60]。当该特定蛋白片段不存在时,HSP90的ATP酶活性不可量化,蛋白质二聚化的位点是C端结构域,其中存在作为四肽重复(TPR)受体含有的辅伴侣的五肽基序(Met-Glu-Glu-Val-Asp或MEEVD)。在原子水平上,该区域的排列(其抽象不会显著干扰HSP90蛋白的功能)尚未被解析[64,65]。

## 4. 热休克蛋白90抑制剂的开发

负责细胞蛋白质调节的过程(称为稳态或蛋白质稳态)帮助细胞不断适应动态环境。HSP90作为分子伴侣帮助蛋白质适应和折叠,同时避免因应激导致的错误折叠和聚集[56]。由于构象动态性,HSP90作为伴侣和辅伴侣的活性非常复杂。由于靶向N端三磷酸腺苷口袋的竞争性抑制,HSP90调节似乎是优选的[56,66]。迄今为止,在19种进入临床试验的N端靶向HSP90抑制剂中,没有一种获得FDA批准。这是因为有害的健康影响,如热休克反应(HSR)诱导[63]。图2显示了一些在临床试验中失败的N端HSP90抑制剂的2D结构。

被称为"C端抑制剂"的化合物已被生产作为替代品,以利用多种方式控制HSP90功能,无论是作为基于天然产物的对应物还是通过合理设计[56]。操纵分子伴侣的一种方法是使用从新生霉素产生的抑制剂来靶向HSP90 C端。新生霉素构效关系导致了神经保护性或细胞毒性化学物质的发现。C端抑制剂是唯一能够区分促存活热休克反应和由客户蛋白降解引起的细胞毒性反应的抑制剂[66]。

多年来,研究人员探索了天然产物在HSP90抑制剂开发中的应用。Delmotte和Delmotte-Plaquee从monosporium border中提取并发现了根赤壳菌素(RD),将其用作大环内酯类抗生素[63,67]。1998年,Schulte等人将根赤壳菌素鉴定为与格尔德霉素(GA)竞争的HSP90抑制剂[68]。根赤壳菌素采用折叠(垂直而非平行)构象,使用大环和芳香环与格尔德霉素不同。然而,它在模拟与天冬氨酸93(Asp93)的构象相互作用方面类似于根赤壳菌素。然而,体内研究揭示了根赤壳菌素抗癌效力的一些缺陷,原因是其半衰期短和快速代谢反应[63,69]。因此,用GA抑制HSP90减少了癌细胞的生长和致癌蛋白的分解[70]。

晶体结构的分析确定了GA和RD在HSP90内的结合位点在N端ATP结合结构域中,并模拟了开放的ADP结合构象[71]。虽然GA和RD有效靶向并干扰HSP90活性,但由于其毒性和低稳定性,它们的临床应用未成功。

### 4.1. HSP90 N端抑制剂

GA和RD对HSP90的选择性是由于HSP90特殊的N端ATP结合Bergerat折叠几何口袋,包含在ATP酶的GHKL亚组中[72,73]。从这种选择性中出现了毒性较低且高度稳定的ATP结合口袋内抑制剂,以模拟GA和RD相互作用。最早进入临床试验的HSP90小分子抑制剂是GA的化学类似物(图3),17-烯丙基氨基-17-去甲氧基格尔德霉素(17-AAG)。该化合物用17-烷基氨基取代17-甲氧基以降低毒性[74,75]。虽然在I期临床试验中证明了抗癌效率,特别是与曲妥珠单抗联合用于HER-2阳性乳腺癌患者时,但17-AAG的开发因其水溶性差和专利问题而终止[76,77]。

用于临床测试的其他苯醌安莎霉素类似物包括17-DMAG、IPI-504和17-AG,它们的结构如图3所示。IPI-504类似物最有前景,进入了II期和III期临床试验。IPI-504是GA的还原醌变体,显示出增强的HSP90敏感性和降低的患者肝毒性。然而,IPI-504的进展因在临床试验中无效而停止[77]。目前没有苯醌安莎霉素化合物处于临床审查中。

根赤壳菌素模拟HSP90的ADP结合构象,与Asp93格尔德霉素类似地相互作用。相反,RD在结合时与GA取向不同,并且对ATP口袋具有更高程度的结合[71,78]。虽然不如GA结构显著,RD也采用折叠构型,具有基本垂直而非平行的大环和芳香环[78]。然而,由于体内快速代谢,RD不表现出抗肿瘤活性[69]。

通过利用RD-HSP90键合复合物的已建立构象决定簇,生产了几种合成类似物。这导致了KF25706的开发,这是一种稳定的代谢化合物,在多种人类癌细胞系和异种移植啮齿动物模型中证明了抗增殖效力[79]。尽管适合应用,但KF25706的复杂性使其难以扩大开发规模。

除了对HSP90抑制至关重要外,RD的间苯二酚部分的行为似乎类似于ATP的腺嘌呤环。几种抑制剂已使用间苯二酚环靶向该药效团,并正在进行临床评估。已证明STA-9090(ganetespib)是由Synta Pharmaceuticals Corp.开发的间苯二唑分子,与HSP90具有高度结合,并在低至0.01 μM的浓度下使其失活。此外,STA-9090显示出增强的肿瘤侵袭性和低毒性[79]。

Rowlands等人评估了56,000种化合物的集合,鉴定了CCT018159,一种包含根赤壳菌素间苯二酚锚定单元的分子[80-82]。CCT018159的进一步开发促成了NVP的形成,一种由诺华批准用于临床评估的间苯二唑异噁唑酰胺。间苯二酚的其他类似物包括KW-2478(Kyowa Hakko Kirin Pharma)和AT13387(Astex)。虽然苯醌安莎霉素和根赤壳菌素的类似物尚未获得临床应用许可,但所揭示的结合口袋反应已被利用,这些化合物有助于开发额外类别的HSP90抑制剂。

最早生产的全合成衍生物是基于嘌呤的化合物。这些化合物利用了GA和RD与HSP90结合后采用的折叠构型。全合成衍生物及其与HSP90相互作用的进一步研究促成了PUH71的合成。该化合物对癌细胞具有亲和力,仅在低浓度下抑制HSP90活性。PU-H71目前处于I期临床试验中,用于晚期恶性肿瘤患者[83-85]。

### 4.2. HSP90 C端抑制剂

使用核苷酸亲和切割在HSP90的C端内发现了第二个ATP结合口袋。发现的主要C端抑制剂是天然产物新生霉素[86,87]。新生霉素相互作用位点靠近C端二聚化结构域,以类似于ADP的弯曲位置结合。新生霉素与C端的相互作用导致靶蛋白分解,而其与HSP90的相互作用保持不稳定[88]。

新生霉素结构被用于合成化合物A4及其类似物。这些A4类似物是修饰的香豆素环系统,模拟腺嘌呤和鸟嘌呤,具有额外的战略性定位的氢键供体和受体,以适应口袋中的更高特异性[89]。产生的最强效的新生霉素类似物是KU-174,设计用于模拟ATP结合构象[90]。该化合物已在多种癌细胞系中证明效力,因为它分解客户蛋白而不引起热休克反应(HSR)[91]。

铂类化疗药物顺铂和微管稳定剂紫杉醇是其他C端抑制剂[92]。目前,没有FDA批准的C端HSP90抑制剂,这对制药行业和科学家来说似乎是一个很大的问题。因此,需要更多资源投入这一研究领域。有趣的是,N端靶向剂的一个挑战通过这些化合物在不诱导HSR的情况下抑制HSP90功能的能力得到解决,使C端抑制剂成为未来研究和探索的有兴趣的化合物。

### 4.3. 中间结构域抑制剂

Sansalvamide A(San A)是一种从海洋真菌Fusarium物种中分离的环状五肽[93]。Sansalvamide A与HSP90的中间结构域N端片段结合,发挥变构干扰C端结合辅伴侣和客户蛋白接触的能力[93]。有趣的是,Di-Sansalvamide A(Di-San A),San A的二聚化版本,被发现结合HSP90的C-中间结构域,表明Di-San A物理上阻止了C端结合客户蛋白的结合[93]。

三种源自San A的化合物H-10、H-15和LY-15已在黑色素瘤细胞中作为HSP90的可能抑制剂进行了研究。这些试剂以浓度和时间依赖性方式抑制黑色素瘤细胞系生长。此外,LY-15和H-10诱导了与半胱天冬酶-3和半胱天冬酶-9激活相关的凋亡相关线粒体通路,但不激活半胱天冬酶-8[94-96]。

### 4.4. 一些处于临床试验中的HSP90抑制剂

虽然目前处于临床试验中的化合物具有广泛的结构,但仔细观察揭示它们通常可以根据其与GA、RD或嘌呤支架的相似性进行分类。只有SNX-5422不属于这些分类中的任何一种(图2)。

正如在药物发现中常见的那样,天然产物在先导化合物发现中发挥重要作用。在HSP90的情况下,SNX-5422、RD和ATP的活性化合物在小分子HSP90抑制剂的开发中发挥了重要作用。这些试剂中没有一种可作为治疗药物被接受,但它们都作为良好的先导分子或起点,用于目前处于临床试验中的大多数药物(表2)。

## 5. 分子反应性、变构动力学和变构设计

HSP90在与癌症相关的众多代谢通路中发挥重要作用。药物发现部门已进行了许多靶向HSP90的试验。此外,已发现几种HSP90抑制剂,但由于毒性问题而失败。因此,研究人员引入了变构扰动作为替代策略,以药理学方式诱导HSP90 ATP酶活性和闭合动力学,同时调节肿瘤细胞死亡[98-100]。此外,几种先进的计算方法已与实验方法结合使用,以提供变构配体识别机制的原子级见解及其体内和体外过程,从HSP90的完全未结合状态开始[101-103]。

Stetz等人[103]使用分子模拟和其他反应实验研究的组合,通过结构分析和系数来破坏HSP90构象,以表征翻译后修饰(PTM)焦点位点的实际功能。研究结果表明,在HSP90构象中,少数保守的PTM充当变构动力学和通讯的全局生物介质,而最大柔性的PTM位点充当变构结构变化的伴随物和传感器。

2020年,Stetz等人[102]在盒式剪接过程中检查了HSP90与Cdc37磷酸化位点之间变构相互作用的机制。为了量化磷酸化位点和激酶精确转换开关的异质后果,研究人员使用了进化测定、粗分子模拟与基于噪声的完全协作建模以及未结合和确定的HSP90和Cdc37系统的分析组合。研究结果表明,向HSP90传递信号的激酶精确磷酸化开关部分根据当前特应性倾向改变其调节特性。

为了观察HSP90形式内的传输分子,Astl等人[101]采用了一种集成笔记本电脑版本,结合进化和系数评估、实验蛋白质连接和结构建模、分子模拟、强度评估和社区建模。他们设计了一种网络链机制,以确保依从性开关之间的一致性,并确定了促进相互作用和与伴侣长期对话的重要监管检查点。该研究的发现进一步揭示了HSP90伴侣的过敏定律,以及对话的基本机制模型和HSP90与功能周期中结合伴侣的版本。

根据大规模生物物理研究和分子模拟,动态位点内紧密的ATP酶反应与HSP90的全局结构和构象动力学相关。这些发现提供了HSP90中蛋白质动力学和催化偶联的机制模型,以及扩展偶联效应如何影响酶活性的测试。

计算建模和计算机辅助药物设计(CADD)极大地促进了药物的成功开发,特别是在当代制药和药物行业中[104]。将计算机辅助药物设计(CADD)整合到HSP90的开发中,有助于增强选择性药物靶向,减少毒性和脱靶效应[1]。计算方法对生物分子结构和功能的研究有显著贡献[105]。这是由于当今存在大量的治疗受体X射线结构。

传统上,设计新型药物通常是一个繁琐、昂贵且漫长的过程。然而,CADD方法(图4)建设性地增强了药物开发中涉及的多任务过程,如同源建模、分析相互作用蛋白、预测结合位点、开发和验证药效团、分子对接和分子动力学模拟[1]。其中一些CADD方法简述如下。

### 5.1. 同源建模

同源建模在建模HSP90酶的未确定结构方面非常有帮助。然而,一些已确定的实验结构已被存储并保存在蛋白质数据库(PDB)中[106,107]。以前,HSP90的原子和分子间性质的研究受到限制[63]。最近,研究人员探索了分子建模方法来设计HSP90的三维模型,这为其结构和机械动力学性质提供了实质性理解[108]。

### 5.2. QSAR

定量构效关系(QSAR)方法用于估计理化参数、化学化合物结构与其生物分子性质之间的相关性[109]。QSAR已成功用于制药和药物行业设计新的强效药物。作为计算机辅助药物设计方法,它帮助设计了强效HSP90药物和抑制剂[107,110,111]。

### 5.3. 分子对接

这种CADD技术已被用于预测和评估配体-受体结合姿态与受体活性位点的模式。该过程包括对接,随后对各个姿态进行评分,并应用它们来确定和计算结合自由能[112]。这种对接技术在计算药物发现和建模中至关重要,例如设计HSP90选择性抑制[113,114]。

分子对接扩展到用于解析小受体配体(大分子)结合机制的方法[105,115]。它通常在基于结构的理性药物设计中进行,以分类精确的小分子配体构象并近似配体和蛋白质之间的相互作用频率[116,117]。在整个对接过程中利用不同受体和配体的笛卡尔坐标来预测配体对所得配体-受体复合物的适当构象。使用分子力学来测量配体-受体结合能。配体和相应的受体基于分子锁和关键方法动态相互作用[118,119]。将不同配体和受体的结合能与该酶抑制剂的生物活性进行比较[120]。

### 5.4. 虚拟筛选

虚拟筛选涉及利用CADD从大量化合物库或库中获得活性先导分子(化合物)。该方法包括分析通过X射线晶体学或核磁共振(NMR)实验获得的三维化合物结构[112]。虚拟筛选已被应用于设计具有高选择性的新HSP90药物抑制[121]。

### 5.5. 药效团开发和验证

当蛋白质的结构性质未解决时,药效团建模已被用于设计先导化合物[122]。该技术已被用于发现具有所需选择性抑制的化合物[122,123]。该技术已在CADD、虚拟筛选和药效团开发的概念中,这些最近已被用于药物重定位。这种方法有助于开发针对癌症治疗的HSP90抑制[124]。

### 5.6. 分子动力学(MD)模拟

该技术在不同环境和条件下研究配体-蛋白质复合物的动态机制[125]。分子动力学模拟对于理解蛋白质和其他生物分子化合物的结构构象和治疗目的非常有价值[126]。在HSP90抑制剂的开发中采用了分子动力学模拟的工具[126]。MD模拟结合能评估和其他后分析已被用于验证HSP90抑制剂-受体复合物的效力和效力[127]。

迄今为止,研究人员尚未广泛探索采用CADD设计特定和选择性共价热休克蛋白90抑制剂的前景潜力[128,129]。

### 5.7. 计算研究(2016-2021年)

计算建模和计算机辅助药物设计(CADD)方法的应用已被广泛用于发现用于癌症治疗的HSP90高特异性抑制剂[130]。

2016年,Mahmoud等人通过广泛采用CADD技术(如药效团建模、分子对接、QSAR和虚拟共晶药效团)发现了新的HSP90抑制剂[131]。该研究鉴定了24个显示HSP90抑制潜力的命中化合物,其中15个具有较低的微摩尔IC50[131]。

2016年Baby等人的另一项研究中,作者采用药效团开发和分子对接的组合技术来鉴定HSP90拮抗剂化合物。从杂环分子中利用GOLD 3.1选择鉴定的HSP90拮抗剂化合物[122,132]。根据研究结果,两种抑制剂Q1G和T21(图5(a)和5(b))具有强结合亲和力和抑制作用。Q1G与氨基酸残基Asp93、Ser52和Tyr139产生氢键网络,而T21与Tyr139和Asp93产生氢键相互作用,表明这些是HSP90抑制的重要残基[122,132]。两个氢键供体、两个氢键受体和两个疏水特性构成了最佳药效团模型[122,132]。

此外,Vettoretti等人使用MD模拟在构象研究中发现了HSP90变构激活的分子机制。该研究还强调了变构调节对HSP90的结构后果,以及其在活性状态下的动力学特征,为开发新的功能调节剂提供了有用信息[133]。

2017年,Abbasi等人通过定量构效关系、分子对接和随后MD模拟的组合计算技术从3,4-异噁唑二酰胺支架预测新型HSP90抑制剂[134]。从结果可以看出HSP90抑制分子的大小、形状、对称性和分支的重要性。对接研究表明,间苯二酚环中的2个羟基对复合物亲和力很重要且必要。三个基团的取向与不同R基团的取代相关。分子动力学(MD)模拟结果比较了一种新化合物和一种最佳合成化合物,其中新化合物(图5(c)-5(e))以比最佳合成化合物更低的结合能稳定在活性位点。

同样,Garg等人验证了构效关系(SAR)方法在开发新型C端HSP90抑制剂中的有效性。进一步评估了这些新型HSP90抑制剂的生物学性质[130]。

同样在2017年,Kumar等人应用化合物库的虚拟筛选来鉴定可能抑制与乳腺癌相关的致癌HSP90相互作用组的分子的强效分子。该研究鉴定了5种活性先导化合物,具有可观的结合能,范围为-8.7 kcal/mol至-10.7 kcal/mol[104]。

2018年,Terracciano等人发现了两种新型强效C端HSP90抑制剂[135]。这两种新型分子物种诱导癌细胞死亡,同时显著下调HSP90。这些新型HSP90抑制剂显示出干扰HSP90 C端区域的高能力,这是由于传统N端结构域抑制剂的成功率低,提供了替代抑制方法[135-139]。这些发现是通过采用特殊优化、分子对接和随后的分子动力学模拟实现的[135]。

Sepehri和Ghavami通过分子对接和3D-QSAR CoMFA研究了四氢吡啶并[4,3-d]嘧啶衍生物(图5(f))作为HSP90抑制剂[140]。根据提取的等高线图或CoMFA模型,获得了三种抑制剂并对接到HSP90的N端结构域结合位点。这些化合物获得了必要的结合能[140]。

此外,Abbasi等人也在2018年通过3D-QSAR、分子对接和分子动力学基于异噁唑支架(图5(g))预测了新的HSP90抑制剂[141]。使用比较分子场分析(CoMFA)和比较分子相似性指数分析(CoMSIA),建立了静电和立体等高线图,并建立了疏水性和氢键,同时产生了供体和受体。因此,预测了新的化合物。使用分子对接和分子动力学(MD)研究和评估了预测化合物在HSP90结合位点的结合模式,发现其在结合位点稳定[141]。

2019年,Mettu等人设计并合成了新型吡唑基2-氨基嘧啶衍生物(图5(h))作为HSP90抑制剂,使用分子对接研究进行评估[142]。研究表明,合成的分子保留了与HSP90的所有基本结合相互作用[142]。

2019年的另一项研究中,Rampogu等人专注于天然化合物作为乳腺癌HSP90的可能抑制剂——药效团引导的分子建模[143]。评估了3210种天然植物化学物质的数据库,以在彻底确认模型药效团后检索潜在抑制剂。筛选后,检索到135种植物化学化合物,并通过类药性因素进一步分类,包括Lipinski五规则、ADMET性质和基于分子对接的评分[144,145]。从对接研究中获得的三种植物化学分子显示出优于所研究临床治疗药物的性质。此外,命中化合物和参考化合物的对接分数分别为48.27(格尔德霉素)、40.90(根赤壳菌素)、73.04(Hit1)、72.92(Hit2)和68.12(Hit3)(图5(i)和5(j)),通过分子动力学验证其结合稳定性[143]。

2020年,Nazar等人应用了计算机方法、分子对接、分子动力学模拟和结合自由能计算来理解HSP90抑制机制,以鉴定新型癌症治疗药物[125]。基于GOLD适应度分数和与对接的HSP90抑制剂及其类似物的取向,它们被指定为最优分子。观察到这些抑制剂与HSP90活性位点的相互作用显著。顶级对接分子(图5(k)和5(l))的分子动力学(MD)确保了抑制与HSP90活性位点之间的强结合相互作用。结果产生了对设计靶向癌症的HSP90的新见解[125]。

在另一份报告中,Nazar等人通过药效团建模设计了新的热休克蛋白(HSP90)抑制剂,并利用虚拟筛选工作流程来确定分子的关键结构(ZINC02819805)(图5(m))[125]。通过化合物ZINC02819805的优化设计了吡唑并吡喃并嘧啶衍生物。通过分子动力学模拟研究了设计化合物之一与HSP90的关键相互作用,并显示出稳定性[125]。

He等人(2020年)进行了计算机研究,以设计新的vibsanin B衍生物(图5(n)和5(o))和基于3D-QSAR、分子对接和分子动力学模拟的HSP90抑制剂[146]。从CoMFA和CoMSIA获得了抑制剂结构信息的指导信息[147]。通过分子对接和动力学评估了抑制剂在结合位点中的稳定性,这也表明许多关键残基对活性有显著贡献。该研究中大多数虚拟设计的化合物呈现出合理的ADMET特征,为HSP90的结构修饰提供了理论支持[147,148]。

Godoy-Castillo等人通过诱导契合对接、分子动力学和3D-QSAR研究鉴定了HSP90中萘醌衍生物抑制剂的结合位点[149]。分子对接和动力学模拟带来了对结合模式和各自蛋白质-抑制剂相互作用的理解。结果为基于醌支架(图5(p))的新型分子的合理修饰提供了基础,以创建具有高抗肿瘤活性的高效力HSP90抑制剂[149]。

Tomašič等人还使用从分子动力学模拟获得的3D药效团发现了新的HSP90 C端抑制剂[150]。通过从独特方法衍生的药效团模型和虚拟筛选确定了合适的结合位点,该方法允许从分子动力学轨迹中衍生和分析配体-蛋白质相互作用。从虚拟筛选检索的化合物中,对两种化合物进行了生物学测试。一种提供有前景性质的独特支架的化合物,用于未来的合成优化和分子开发,需要评估HSP90 C端结构域作为开发抗癌药物的关注焦点[151]。

Shadrack等人对HSP90中水和构象波动在抑制剂反应中的作用进行了计算研究[152]。作者建议对接实践用于FDA批准的HSP90药物的重定位。描述了apo、holo和受体集合(松弛复合物)结构、水和HSP90构象修饰的作用[152,153]。据报道,当在晶体结构上执行时,对接能比RCS集合对水的包含更敏感。结果可作为开发HSP90抑制剂的可能基础[154]。

Bekker等人使用基于多规范分子动力学的动态对接来彻底研究抑制剂与HSP90 N端结构域结合的配置空间[155]。该研究中的动态对接方法有效预测了固有结合位点,同时彻底测试了广泛的配置空间,对结合时的蛋白质结构施加了改变影响[155]。

Cai等人合成了新的含吡唑的酰亚胺衍生物(图5(q)),并使用分子动力学模拟进行评估[156]。在药效团和分子对接的辅助下,HSP90被建议作为这些化合物的可能药物靶点。通过分子动力学评估了这些化合物的稳定性[156]。

Magwenyane等人通过DFT、分子对接和MD研究了根赤壳菌素(RD)和NVP-YUA922(NVP)(图6)通过抑制癌症的治疗性质的结构和分子见解,以了解HSP90 N端动力学[63]。密度泛函理论(DFT)计算预测NVP具有-23.3 kcal/mol的溶剂化自由能的高有利性和75.5 kcal/mol的最高稳定性能,用于主要原子离域。分子动力学(MD)评估显示,与RD(NT-RD)相比,NVP与HSP90结合(NT-NVP)时具有高稳定性。HSP90蛋白对NT-NVP的结合亲和力大于NT-RD,其中结合中突出的关键残基是Gly 97、Asp 93和Thr 184。其中的发现作为对HSP90动力学的有益见解,将有助于构建用于癌症治疗的新型强效抑制剂[63]。

Dike等人应用了计算机方法来鉴定小分子调节剂,以破坏用于癌症治疗的HSP90-Cdc37蛋白质-蛋白质相互作用界面[157]。从超过60,000种化合物的集合中发现了四种分子。分子动力学(MD)模拟阐明了所有四种分子保持在界面上并对HSP90-Cdc37有强亲和力。因此,建议的分子可能对成功抑制HSP90-Cdc37界面至关重要[157]。

2021年,Mak等人通过使用虚拟筛选和内在蛋白质荧光猝灭结合测定发现了两种类药新型HSP90 CTD抑制剂,为利用分子伴侣抑制剂的新型治疗药物的未来开发做准备[151]。

在另一项研究中,Rezvani等人通过分子对接、MD模拟和密度泛函理论的计算机技术鉴定了两种新的HSP90抑制剂[1]。使用Zinc15结构查询来定位处于不同临床试验阶段的HSP90抑制剂中的相关化合物(78%)。使用预定相似性截止值获得了29种小分子并对接到HSP90-NTDs的集合中。使用分子对接和分子间结合研究发现,氢键、疏水相互作用和盐桥是复合物形成的决定性力量。由于其构象略有不同,化合物19和20被HSP90的结合口袋有效容纳。Asn51和Phe138被鉴定为与19和20稳定相互作用的重要残基[1]。

Wang等人采用定量构效关系(QSAR)技术研究了氨基吡唑取代的间苯二酚化合物作为HSP90抑制剂[158]。新型HSP90抑制剂,氨基吡唑取代的间苯二酚化合物,具有广泛的HSP90抑制作用,被创建用于开发新的抗菌药物。使用定量构效关系技术预测了新型HSP90抑制剂的真菌选择性。最佳线性模型的相关系数R为0.89和0.11,并产生了两个非线性模型[158]。

在另一项研究中,Tomašič等人对选择性DNA旋转酶和HSP90抑制剂应用了三维药效团分析[159]。作者为GyrB、人拓扑异构酶IIα(TopoII)和HSP90 N端结构域(NTD)设计了选择性三维药效团系统,用作命中扩展和先导优化的起点。使用其脱靶药效团建模,他们能够预测GyrB抑制剂的选择性靶向结合。选定化合物1和2对HSP90和TopoII的体外研究证实了这些发现。针对大肠杆菌DNA旋转酶和人TopoII的体外研究验证了化合物3和4的选择性HSP90 NTD抑制的预测,这也通过针对大肠杆菌DNA旋转酶和人TopoII的体外测定得到证实。经证实,设计基于三维化学参数的药效团模型是预测已知和新型HSP90和GyrB抑制剂活性和选择性的有用工具[159]。

Rezvan等人使用Zinc15结构询问揭示了处于不同临床试验阶段的HSP90抑制剂中的类似化合物(≥78%)。使用预定相似性截止值将小分子对接到HSP90 NTDs的集合中。发现两种构象非常不同的化合物在HSP90的结合口袋中耐受良好。Asn51和Phe138被鉴定为与化合物稳定相互作用的重要残基。尽管所提出的化合物的基本作用机制未知且仍有待研究,但这项工作指出了未来结构导向优化朝向HSP90-NTD强效抑制剂的关键结构特征[1]。

## 6. 结论

近年来,开发致癌HSP90高选择性抑制剂发生了范式转变。这对于克服阻碍已有药物临床批准后批准的障碍很重要,尽管它们在多种临床前和临床研究中显示出有效性。传统上,设计新型药物通常是一个繁琐、昂贵且漫长的过程。然而,CADD方法建设性地增强了药物开发中涉及的多任务过程,如同源建模、分析相互作用蛋白、预测结合位点、开发和验证药效团、分子对接和分子动力学。

现代CADD技术在HSP90研究中的应用产生了结构和分子见解,有助于鉴定和改善具有增强选择性和活性的新型HSP90抑制剂。尽管迄今取得了进展,但仍需要一种新的计算机技术与实验验证相结合的动力,以获得高选择性的结果。我们还建议对HSP90的三个不同结构域进行先进计算建模的广泛研究,以设计具有最小脱靶效应的强效有效抑制剂。

---

**利益冲突:** 作者声明没有利益冲突。

**致谢:** 作者感谢南非国家研究基金会和夸祖鲁-纳塔尔大学健康科学学院的支持。