Abstract

Molecular dynamics simulation is a powerful method for investigating the structural stability, dynamics, and function of biopolymers at the atomic level. In recent years, it has become possible to perform simulations on time scales of the order of milliseconds using special hardware. However, it is necessary to derive the important factors contributing to structural change or function from the complicated movements of biopolymers obtained from long simulations. Although some analysis methods for protein systems have been developed using increasing simulation times, many of these methods are static in nature (i.e., no information on time). In recent years, dynamic analysis methods have been developed, such as the Markov state model and relaxation mode analysis (RMA), which was introduced based on spin and homopolymer systems. The RMA method approximately extracts slow relaxation modes and rates from trajectories and decomposes the structural fluctuations into slow relaxation modes, which characterize the slow relaxation dynamics of the system. Recently, this method has been applied to biomolecular systems. In this article, we review RMA and its improved versions for protein systems.

Highlights

  • Molecular dynamics simulation is widely used for protein research

  • We have reviewed the method and application of relaxation mode analysis (RMA), a dynamic analysis method for protein simulations

  • We summarized several new RMAs proposed, including RMA with multiple evolution times, principal component RMA (PCRMA), two-step RMA, and Markov-state RMA (MSRMA)

Read more

Summary

Introduction

Molecular dynamics simulation is widely used for protein research. In general, the focus of this research is to extract information on the physical properties of individual proteins. The point of RMA is that we consider the variational problem, which is equivalent to the eigenvalue problem of the time evolution operator, and choose an appropriate trial function to estimate the slow relaxation modes and rates in the system (see the “RMA” section). In RMA, the relaxation modes and rates are given as left eigenfunctions and eigenvalues of the time evolution operator of the master equation of the system, respectively. From this point of view, RMA is related to Markov state models. RMA can be used to effectively analyze long simulations at room temperature and is useful for investigating systems with large conformational changes, such as intrinsically disordered proteins and protein folding

Conclusions
Compliance with ethical standards
Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call