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

Source: [Guiding Peptide Kinetics via Collective-Variable Tuning of Free-Energy Barriers](<https://www.sota2.com/research/paper/699d30ae6017c4460ffab675>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-04-30T05:00:04.703Z

Authors: Alexander Zhilkin, Muralika Medaparambath, Dan Mendels

Published: 2026-02-23T15:09:31.000Z

## 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.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2602.19936>)

[PDF](<https://arxiv.org/pdf/2602.19936.pdf>)