Guiding Peptide Kinetics via Collective-Variable Tuning of Free-Energy Barriers

TL;DR

This paper introduces a data-efficient method to engineer protein conformational kinetics by reshaping free-energy landscapes using collective variables derived from limited molecular dynamics data.

Contribution

It presents the CV-FEST framework that predicts mutation effects on protein kinetics from minimal sampling, enabling practical peptide and protein engineering.

Findings

HLDA-derived CVs predict mutation effects on unfolding rates

Leading HLDA eigenvalue correlates with transition rates

Minimal in-basin sampling suffices for kinetic inference

Abstract

While recent advances in AI have transformed protein structure prediction, protein function is also strongly influenced by the thermodynamic and kinetic features encoded in its underlying free-energy surface. Here, we propose a data-efficient framework for engineering protein conformational kinetics by rationally reshaping free-energy landscapes to control transition rates. Built on the Collective Variables for Free Energy Surface Tailoring (CV-FEST) framework, the approach is validated on point mutations of the miniprotein Chignolin. The framework relies on Harmonic Linear Discriminant Analysis (HLDA)-based collective variables (CVs) constructed from short molecular dynamics trajectories confined to metastable folded and unfolded basins, requiring only limited local sampling rather than exhaustive rare-event simulations. Notably, the HLDA CV derived solely from the wild-type system provides residue-level scores that predict whether mutations at specific positions are likely to accelerate or slow unfolding transitions. Furthermore, the leading HLDA eigenvalue associated with the derived CV, a quantitative measure of the one-dimensional statistical separation between folded and unfolded ensembles, is significantly correlated with transition rates across mutations. Together, these results suggest that mutation-dependent kinetic effects can be inferred from minimal in-basin sampling, providing a practical route for guiding peptide and protein engineering through collective-variable design, free-energy surface engineering, and data-efficient molecular simulation.