Paper
Guiding Peptide Kinetics via Collective-Variable Tuning of Free-Energy Barriers
Authors
Alexander Zhilkin, Muralika Medaparambath, Dan Mendels
Abstract
While recent advances in AI have transformed protein structure prediction, protein function is also often strongly influenced by the thermodynamic and kinetic features encoded in its underlying free-energy surface. Here, we propose a framework to rationally modify these surfaces in order to control conformational transition rates, built on the Collective Variables for Free Energy Surface Tailoring (CV-FEST) framework, and validate it 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 restricted to the metastable states, requiring only limited sampling within each state. Notably, the HLDA CV derived solely from the wild-type system already provides residue-level scores that predict whether mutations at specific positions are likely to accelerate or slow conformational transitions. Furthermore, we find that the leading HLDA eigenvalue associated with the derived CV, a quantitative measure of the one-dimensional statistical separation between folded and unfolded ensembles, correlates strongly with conformational transition rates across mutations. Together, these results show that kinetic effects of point mutations can be inferred from minimal local sampling, providing a practical route to the rational engineering of conformational transition rates without exhaustive simulations or large training datasets.
Metadata
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Raw Data (Debug)
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"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2602.19936v1</id>\n <title>Guiding Peptide Kinetics via Collective-Variable Tuning of Free-Energy Barriers</title>\n <updated>2026-02-23T15:09:31Z</updated>\n <link href='https://arxiv.org/abs/2602.19936v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2602.19936v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>While recent advances in AI have transformed protein structure prediction, protein function is also often strongly influenced by the thermodynamic and kinetic features encoded in its underlying free-energy surface. Here, we propose a framework to rationally modify these surfaces in order to control conformational transition rates, built on the Collective Variables for Free Energy Surface Tailoring (CV-FEST) framework, and validate it 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 restricted to the metastable states, requiring only limited sampling within each state. Notably, the HLDA CV derived solely from the wild-type system already provides residue-level scores that predict whether mutations at specific positions are likely to accelerate or slow conformational transitions. Furthermore, we find that the leading HLDA eigenvalue associated with the derived CV, a quantitative measure of the one-dimensional statistical separation between folded and unfolded ensembles, correlates strongly with conformational transition rates across mutations. Together, these results show that kinetic effects of point mutations can be inferred from minimal local sampling, providing a practical route to the rational engineering of conformational transition rates without exhaustive simulations or large training datasets.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='physics.bio-ph'/>\n <published>2026-02-23T15:09:31Z</published>\n <arxiv:primary_category term='physics.bio-ph'/>\n <author>\n <name>Alexander Zhilkin</name>\n </author>\n <author>\n <name>Muralika Medaparambath</name>\n </author>\n <author>\n <name>Dan Mendels</name>\n </author>\n </entry>"
}