A maximum-caliber approach to predicting perturbed folding kinetics due to mutations

TL;DR

This paper introduces a maximum-caliber method to predict how mutations affect protein folding kinetics by inferring transition rates in Markov State Models, demonstrating its effectiveness across various complex protein systems.

Contribution

The paper develops a robust, simple maximum-caliber approach for predicting mutation-induced changes in protein folding rates using Markov State Models.

Findings

Accurately predicts folding rate changes in diverse protein systems

Effective in systems with non-native interactions

Applicable to complex, real-world protein folding scenarios

Abstract

We present a maximum-caliber method for inferring transition rates of a Markov State Model (MSM) with perturbed equilibrium populations, given estimates of state populations and rates for an unperturbed MSM. It is similar in spirit to previous approaches but given the inclusion of prior information it is more robust and simple to implement. We examine its performance in simple biased diffusion models of kinetics, and then apply the method to predicting changes in folding rates for several highly non-trivial protein folding systems for which non-native interactions play a significant role, including (1) tryptophan variants of GB1 hairpin, (2) salt-bridge mutations of Fs peptide helix, and (3) MSMs built from ultra-long folding trajectories of FiP35 and GTT variants of WW domain. In all cases, the method correctly predicts changes in folding rates, suggesting the wide applicability of maximum-caliber approaches to efficiently predict how mutations perturb protein conformational dynamics.