The Wako-Saitô-Muñoz-Eaton Model for Predicting Protein Folding and Dynamics

Abstract

Despite the recent advances in the prediction of protein structures by deep neutral networks, the elucidation of protein-folding mechanisms remains challenging. A promising theory for describing protein folding is a coarse-grained statistical mechanical model called the Wako–Saitô–Muñoz–Eaton (WSME) model. The model can calculate the free-energy landscapes of proteins based on a three-dimensional structure with low computational complexity, thereby providing a comprehensive understanding of the folding pathways and the structure and stability of the intermediates and transition states involved in the folding reaction. In this review, we summarize previous and recent studies on protein folding and dynamics performed using the WSME model and discuss future challenges and prospects. The WSME model successfully predicted the folding mechanisms of small single-domain proteins and the effects of amino-acid substitutions on protein stability and folding in a manner that was consistent with experimental results. Furthermore, extended versions of the WSME model were applied to predict the folding mechanisms of multi-domain proteins and the conformational changes associated with protein function. Thus, the WSME model may contribute significantly to solving the protein-folding problem and is expected to be useful for predicting protein folding, stability, and dynamics in basic research and in industrial and medical applications.

Ab initio protein structure prediction: the necessary presence of external force field as it is delivered by Hsp40 chaperone

“…Recently proposed models like Wako-Saitô-Muñoz-Eaton (WSME) model succesfully predicts the structure of domains [ 53 , 54 ]. However, according to FOD-M-based analysis, domains are mostly characterised by very low K parameter [ 26 ].…”

A Kinetic Transition Network Model Reveals the Diversity of Protein Dimer Formation Mechanisms

“…The WSME model [21][22][23] is an Ising-like model for generating denatured ensembles of proteins. In this model, each conformational state of a protein is characterized by the folding state of the residues.…”

“…We can avoid this by assuming that a projection of the full dynamics is observed on the discrete states, resulting in a Projected Markov Model, which may be estimated using a Hidden Markov Model [18]. An alternative way to avoid the discretization problem is using ensembles of discrete states generated by an ensemble-based method such as COREX [19] or the Wako-Saito-Muñoz-Eaton model [20][21][22][23]. If we can define a reversible move set for the states of the ensemble generated by a particular method, we can calculate the probabilities of transitions by the Metropolis-Hastings criterion [24,25].…”

“…To demonstrate the applicability of the model, we applied our two-layer kinetic network model to a Wako-Saito-Muñoz-Eaton (WSME) representation [21][22][23] of several three-and two-state protein complexes. Using Transition Path Theory [26,27], we were able to characterize in a detailed manner the kinetics of the dimer formation of some homodimers that have not been previously investigated kinetically.…”