Memory unlocks the future of biomolecular dynamics: Transformative tools to uncover physical insights accurately and efficiently

Abstract

Conformational changes underpin function and encode complex biomolecular mechanisms. Gaining atomic-level detail of how such changes occur has the potential to reveal these mechanisms and is of critical importance in identifying drug targets, facilitating rational drug design, and enabling bioengineering applications. While the past two decades have brought Markov State Model techniques to the point where practitioners can regularly use them to glimpse the long-time dynamics of slow conformations in complex systems, many systems are still beyond their reach. In this perspective, we discuss how memory can reduce the computational cost to predict the long-time dynamics in these complex systems by orders of magnitude and with greater accuracy and resolution than state-of-the-art Markov State Models. We illustrate how memory lies at the heart of successful and promising techniques, ranging from the Fokker-Planck and generalized Langevin equations to deep learning recurrent neural networks and generalized master equations. We delineate how these techniques work, identify insights that they can offer in biomolecular systems, and discuss their advantages and disadvantages in practical settings. We show how generalized master equations can enable the investigation of, for example, the gate-opening process in RNA polymerase II and demonstrate how our recent advances tame the deleterious influence of statistical underconvergence of the molecular dynamics simulations used to parameterize these techniques. This represents a significant leap forward that will enable our memory-based techniques to interrogate systems that are currently beyond the reach of even the best Markov State Models. We conclude by discussing some current challenges and future prospects for how exploiting memory will open the door to many exciting opportunities.

CoVAMPnet: Comparative Markov State Analysis for Studying Effects of Drug Candidates on Disordered Biomolecules

“…11−14 Several alternative methodologies exist to address these shortcomings. These include hidden Markov models (HMMs) to relax the Markovian assumption, 12 approaches incorporating memory effects such as the generalized master equation (GME) and the generalized Langevin equation (GLE) for more effective dynamic property assessment, 11 and methods rooted in deep learning. 15 A powerful framework based on deep learning is VAMPnet, a neural network that learns a probabilistic assignment of each simulation frame to individual states in an unsupervised manner by maximizing a variational score.…”

Analysis of Velocity Autocorrelation Functions from Molecular Dynamics Simulations of a Small Peptide by the Generalized Langevin Equation with a Power-Law Kernel

“…The process was analyzed in terms of fractional diffusion to account for the self-similarity of a continuous distribution of time scales. Although a wide variety of approaches has been used to investigate memory effects in protein dynamics and kinetics, 1 including conformational dynamics or folding, 45 the present study is essentially driven by the perspective of NMR spectroscopy, where one is primarily interested in characterizing the motions of bond or atomic motions through the correlation functions attached to them. In this case, the interpretation of spin relaxation requires sound physical models in order to disentangle dynamics from the statistical properties, including memory effects, and to provide plausible models of dynamics that can be used for the analysis of relaxation data, while avoiding overfitting.…”

Symmetry-adapted Markov state models of closing, opening, and desensitizing in α7 nicotinic acetylcholine receptors

“…To construct an MSM, one must determine an appropriate lag time T and the number of microstates so that the relaxation time within each microstate is shorter than T for the model to be valid. This requirement poses challenges when modeling large proteins [34, 35]. Modelling proteins as large as pLGICs often necessitates either a large number of microstates or a long lag time, which contradicts the advantages offered by the MSM framework.…”