PT - JOURNAL ARTICLE AU - Tim Hempel AU - Mauricio J. del Razo AU - Christopher T. Lee AU - Bryn C. Taylor AU - Rommie E. Amaro AU - Frank NoƩ TI - Independent Markov Decomposition: Towards modeling kinetics of biomolecular complexes AID - 10.1101/2021.03.24.436806 DP - 2021 Jan 01 TA - bioRxiv PG - 2021.03.24.436806 4099 - http://biorxiv.org/content/early/2021/03/25/2021.03.24.436806.short 4100 - http://biorxiv.org/content/early/2021/03/25/2021.03.24.436806.full AB - In order to advance the mission of in silico cell biology, modeling the interactions of large and complex biological systems becomes increasingly relevant. The combination of molecular dynamics (MD) and Markov state models (MSMs) have enabled the construction of simplified models of molecular kinetics on long timescales. Despite its success, this approach is inherently limited by the size of the molecular system. With increasing size of macromolecular complexes, the number of independent or weakly coupled subsystems increases, and the number of global system states increase exponentially, making the sampling of all distinct global states unfeasible. In this work, we present a technique called Independent Markov Decomposition (IMD) that leverages weak coupling between subsystems in order to compute a global kinetic model without requiring to sample all combinatorial states of subsystems. We give a theoretical basis for IMD and propose an approach for finding and validating such a decomposition. Using empirical few-state MSMs of ion channel models that are well established in electrophysiology, we demonstrate that IMD can reproduce experimental conductance measurements with a major reduction in sampling compared with a standard MSM approach. We further show how to find the optimal partition of all-atom protein simulations into weakly coupled subunits.Significance Statement Molecular simulations of proteins are often interpreted using Markov state models (MSMs), in which each protein configuration is assigned to a global state. As we explore larger and more complex biological systems, the size of this global state space will face a combinatorial explosion, rendering it impossible to gather sufficient sampling data. In this work, we introduce an approach to decompose a system of interest into separable subsystems. We show that MSMs built for each subsystem can be later coupled to reproduce the behaviors of the global system. To aid in the choice of decomposition we also describe a score to quantify its goodness. This decomposition strategy has the promise to enable robust modeling of complex biomolecular systems.Competing Interest StatementThe authors have declared no competing interest.