Computational Modeling of Protein Dynamics: Bridging Experimental and Theoretical Perspectives

Abstract

Computational modeling has emerged as a powerful tool for investigating the intricate dynamics of proteins, complementing experimental techniques and providing insights into their functional mechanisms. This review delves into the state-of-the-art computational methods employed to simulate protein dynamics, including molecular dynamics (MD) simulations, normal mode analysis (NMA), and coarse-grained modeling. MD simulations offer atomic-level resolution, enabling the exploration of protein conformational changes, ligand binding, and enzyme catalysis. NMA, on the other hand, provides a simplified yet informative picture of protein flexibility by analyzing collective vibrational motions. Coarse-grained models, which reduce the complexity of the system by grouping atoms into larger beads, allow for efficient simulation of large-scale protein motions and long-time dynamics. By integrating these computational approaches with experimental data, researchers can gain a deeper understanding of protein function and design strategies for therapeutic interventions.