Building insightful, memory-enriched models to capture long-time biochemical processes from short-time simulations.

ABSTRACT: The ability to predict and understand complex molecular motions occurring over diverse timescales ranging from picoseconds to seconds and even hours in biological systems remains one of the largest challenges to chemical theory. Markov state models (MSMs), which provide a memoryless description of the transitions between different states of a biochemical system, have provided numerous important physically transparent insights into biological function. However, constructing these models often necessitates performing extremely long molecular simulations to converge the rates. Here, we show that by incorporating memory via the time-convolutionless generalized master equation (TCL-GME) one can build a theoretically transparent and physically intuitive memory-enriched model of biochemical proce

SUBMITTER: Dominic AJ 

PROVIDER: S-EPMC10041170 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature