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 including memory (i.e., non-Markovian effects) 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.

Submillisecond Atomistic Molecular Dynamics Simulations Reveal Hydrogen Bond-Driven Diffusion of a Guest Peptide in Protein–RNA Condensate

“…Next, we applied our recently developed IGME method , to compute the rates for the transition between the bound and dissociated states. The IGME method is based on the generalized master equation framework, − and it has been shown to be an efficient and robust way to estimate the rates for transitions between various conformational states from an ensemble of MD simulations . Specifically, the two-state transition probability matrices (TPMs) were calculated for all three systems with maximum likelihood estimation to enforce the detailed balance requirement .…”

Sub-millisecond Atomistic Simulations Reveal Hydrogen Bond-Driven Diffusion of a Guest Peptide in Protein-RNA Condensate

“…The IGME method is based on the generalized master equation framework [40][41][42] , and it has been shown to be an efficient and robust way to estimate the rates for transitions between various conformational states from an ensemble of MD simulations 24 . Specifically, the two-state transition probability matrices (TPM) were calculated for all three systems with maximum likelihood estimation to enforce the detailed balance requirement 43 .…”

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

“…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 .…”

“…Although some of these procedures are quite advanced and enable, e.g., an accurate estimation of transition rates even from biased simulation data 10 , the manual selection of the collective variables is typically laborious and can often cause the resulting models to fail the tests for Markovianity. While MSMs are extremely valuable tools, they possess certain limitations, such as the assumption of Markovianity, constraints on state representation granularity, reliance on extensive sampling, and relatively rapid relaxation dynamics [11][12][13][14] . Several alternative methodologies exist to address these shortcomings.…”