A critical perspective on Markov state model treatments of protein–protein association using coarse-grained simulations

Abstract

Atomic-level information is essential to explain the specific interactions governing protein–protein recognition in terms of structure and dynamics. Of particular interest is a characterization of the time-dependent kinetic aspects of protein–protein association and dissociation. A powerful framework to characterize the dynamics of complex molecular systems is provided by Markov State Models (MSMs). The central idea is to construct a reduced stochastic model of the full system by defining a set of conformational featured microstates and determining the matrix of transition probabilities between them. While a MSM framework can sometimes be very effective, different combinations of input featurization and simulation methods can significantly affect the robustness and the quality of the information generated from MSMs in the context of protein association. Here, a systematic examination of a variety of MSMs methodologies is undertaken to clarify these issues. To circumvent the uncertainties caused by sampling issues, we use a simplified coarse-grained model of the barnase–barstar protein complex. A sensitivity analysis is proposed to identify the microstates of an MSM that contribute most to the error in conjunction with the transition-based reweighting analysis method for a more efficient and accurate MSM construction.

Committor-Consistent Variational String Method

“…The k on and k off values here are also close the MSM rates of 2.36 × 10 13 Å 3 s –1 and 2.74 × 10 7 s –1 , respectively, with two order parameters at an optimal lag time of 12 ns. It should be noted that k on is a second-order rate constant, as expected for a bimolecular association process consistent with previous analyses …”

Challenges and frontiers of computational modelling of biomolecular recognition

“…Computational methods have been developed to model biomolecular recognition and predict the binding free energies and/or kinetics rates, including the widely used molecular docking (Ciemny et al, 2018;Morris et al, 2009;Porter et al, 2017;Vakser, 2020;Wang & Zhu, 2016), Brownian Dynamics (Ermak & McCammon, 1978;Gabdoulline & Wade, 2001;Spaar et al, 2006;Votapka & Amaro, 2015;Wieczorek & Zielenkiewicz, 2008) and Molecular Dynamics (MD) simulations (Basdevant et al, 2013;He et al, 2021;Karplus & McCammon, 2002;Lamprakis et al, 2021;Pan et al, 2019). Molecular docking has been widely used for predicting the holo structures of protein-ligand (Wang & Zhu, 2016), protein-peptide (Ciemny et al, 2018) and protein-protein complexes (Vakser, 2020).…”

Docking-based long timescale simulation of cell-size protein systems at atomic resolution

“…The “minima hopping” paradigm has been widely used since the early days of molecular modeling for the sampling of the energy landscapes of biomolecules - such as conformational analysis of biopolymers, 37 rotamer libraries, 38 and refinement of protein-protein interfaces, 39 providing extraordinary savings of computing time by avoiding travel in low-probability areas of the landscape. Markov State Models (MSM), have been used to study protein folding, dynamics, 40 and association 41 by representing the energy landscape by a set of the energy minima and the probabilities of transition between them. In this study we use a similar idea, namely a Markov State Monte Carlo approach to sampling transitions between low energy states, to perform very long trajectory simulations of large systems of proteins at atomic resolution.…”