Application of molecular dynamics simulation in biomedicine

Abstract

Molecular dynamics (MD) simulation has been widely used in the field of biomedicine to study the conformational transition of proteins caused by mutation or ligand binding/unbinding. It provides some perspectives those are difficult to find in traditional biochemical or pathological experiments, for example, detailed effects of mutations on protein structure and protein–protein/ligand interaction at the atomic level. In this review, a broad overview on conformation changes and drug discovery by MD simulation is given. We first discuss the preparation of protein structure for MD simulation, which is a key step that determines the accuracy of the simulation. Then, we summarize the applications of commonly used force fields and MD simulations in scientific research. Finally, enhanced sampling methods and common applications of these methods are introduced. In brief, MD simulation is a powerful tool and it can be used to guide experimental study. The combination of MD simulation and experimental techniques is an a priori means to solve the biomedical problems and give a deep understanding on the relationship between protein structure and function.

A Single Point Mutation Blocks the Entrance of Ligands to the Cannabinoid CB2 Receptor via the Lipid Bilayer

“…The above results, and the experimental evidence (see Introduction), led us to use the ligand bound to TMs 1 and 7 of CB 2 R to progressively sample the binding event. Because entry to the orthosteric binding site can have the largest energetic barrier of the process, , in some cases unsurmountable by unbiased MD simulations, we used well-tempered metadynamics . In this technique, a biasing potential is applied to permit the system to explore energetically inaccessible regions for unbiased MD simulations on reasonable time scales.…”

“…Because entry to the orthosteric binding site can have the largest energetic barrier of the process, 15 , 40 in some cases unsurmountable by unbiased MD simulations, we used well-tempered metadynamics. 69 In this technique, a biasing potential is applied to permit the system to explore energetically inaccessible regions for unbiased MD simulations on reasonable time scales. We used as a collective variable of the distance between the center of mass of the ligand in the initial conformation and the reference docking pose at the orthosteric binding site (see Materials and Methods ).…”

ACES: Optimized Alchemically Enhanced Sampling

“…These general ideas have been around for many years, but their fruition into robust practical methods for prediction of protein–ligand binding affinities has been met with challenges and remains a very active area of research and software development. It is in the details of how these requirements are achieved that distinguishes many of the different methods reported in the literature to date. − …”

Assessments of Variational Autoencoder in Protein Conformation Exploration

“…[12][13][14] Many enhanced sampling methods have been developed to address this issue, which in general fall into two categories: collective variable based (such as metadynamics 14,15 and variationally enhanced sampling 16,17 and collective variable free methods (such as replica exchange molecular dynamics 18,19 and integrated tempering sampling 20,21 ). 12,22,23 The deep-learning autoencoders models 24 present a powerful nonlinear dimensionality reduction technique to mine data-driven collective variables from MD trajectories [25][26][27][28][29][30][31][32] . This technique furnishes explicit and differentiable expressions for the highly abstract and differentiable collective variables, making it the ideal candidate for integration with enhanced sampling techniques to accelerate the exploration in the proteins configurational space.…”