Molecular dynamics simulations of proteins: an in-depth review of computational strategies, structural insights, and their role in medicinal chemistry and drug development.

Molecular dynamics (MD) simulations have emerged as a powerful and extensively employed tool in biomedical research, offering insights into intricate biomolecular processes such as structural flexibility and molecular interactions, and playing a pivotal role in the development of therapeutic approaches. Although MD techniques are applied to a variety of biomolecules including DNA, RNA, proteins, and their assemblies, this review focuses specifically on the role of MD in elucidating protein behavior and their interactions with inhibitors across different disease contexts. The selection of an appropriate force field is essential, as it greatly influences the reliability of simulation outcomes. Widely adopted MD software packages such as GROMACS, DESMOND, and AMBER leverage rigorously tested force fields and have shown consistent performance across diverse biological applications. Despite current successes, challenges remain in narrowing the gap between computational models and actual cellular conditions. The integration of machine learning and deep learning technologies is expected to accelerate progress in this evolving field.

Background:

Molecular dynamics (MD) simulations have emerged as a powerful and extensively employed tool in biomedical research, offering insights into intricate biomolecular processes such as structural flexibility and molecular interactions, and playing a pivotal role in the development of therapeutic approaches. Although MD techniques are applied to a variety of biomolecules including DNA, RNA, proteins, and their assemblies, this review focuses specifically on the role of MD in elucidating protein behavior and their interactions with inhibitors across different disease contexts.

Methods:

N/A - Review article

Results:

The selection of an appropriate force field is essential, as it greatly influences the reliability of simulation outcomes. Widely adopted MD software packages such as GROMACS, DESMOND, and AMBER leverage rigorously tested force fields and have shown consistent performance across diverse biological applications.

Data Summary:

No quantitative results or key statistics are provided in the abstract.

Conclusions:

Despite current successes, challenges remain in narrowing the gap between computational models and actual cellular conditions. The integration of machine learning and deep learning technologies is expected to accelerate progress in this evolving field.

Practical Significance:

MD simulations play a pivotal role in the development of therapeutic approaches, particularly by elucidating protein behavior and their interactions with inhibitors across different disease contexts.