Markov State Models to Study the Functional Dynamics of Proteins in the Wake of Machine Learning

Abstract

Markov state models (MSMs) based on molecular dynamics (MD) simulations are routinely employed to study protein folding, however, their application to functional conformational changes of biomolecules is still limited. In the past few years, the field of computational chemistry has experienced a surge of advancements stemming from machine learning algorithms, and MSMs have not been left out. Unlike global processes, such as protein folding, the application of MSMs to functional conformational changes is challenging because they mostly consist of localized structural transitions. Therefore, it is critical to properly select a subset of structural features that can describe the slowest dynamics of these functional conformational changes. To address this challenge, we recommend several automatic feature selection methods such as Spectral-OASIS. To identify states in MSMs, the chosen features can be subject to dimensionality reduction methods such as TICA or deep learning based VAMPNets to project MD conformations onto a few collective variables for subsequent clustering. Another challenge for the application of MSMs to the study of functional conformational changes is the ability to comprehend their biophysical mechanisms, as MSMs built for these processes often require a large number of states. We recommend the recently developed quasi-MSMs (qMSMs) to address this issue. Compared to MSMs, qMSMs encode the non-Markovian dynamics via the generalized master equation and can significantly reduce the number of states. As a result, qMSMs can be built with a handful of states to facilitate the interpretation of functional conformational changes. In the wake of machine learning, we believe that the rapid advancement in the MSM methodology will lead to their wider application in studying functional conformational changes of biomolecules.

Prediction of Aptamer–Small-Molecule Interactions Using Metastable States from Multiple Independent Molecular Dynamics Simulations

“…We note, however, that there are other ways to obtain these representative conformations. Metastable states can be obtained from Markov state models (MSM) from multiple short MD simulations as long-lived conformations in the dynamics of the system . This analysis can help characterize the conformational space of unbound aptamers and the arrangement of binding sites .…”

Identification of functional substates of KRas during GTP hydrolysis with enhanced sampling simulations

“…Motivated by such consideration, numerous molecular dynamics (MD) [37][38][39][40][41][42][43][44] and enhanced sampling simulations 33,[45][46][47] have been performed to investigate conformational properties of Ras [48][49][50] and to dissect their biological function. Lu et al 51 observed that KRasGTPÁMg 2+ had one active state and three inactive substates in solution.…”

Distinct Activation Mechanisms Regulate Subtype Selectivity of Cannabinoid Receptors

“…Markov state model was developed to connect the information from individual trajectories to by building transition network between distinct states. [78][79][80] In markovian processes, the future step of the process should only depend on present states, not on the past states. S23A and S23B).…”