arXiv subjects

Alexander Zhilkin

Publications and source records attributed to Alexander Zhilkin.

3 recordsLinked to original sources

arXiv2026

Collective Variable-Guided Engineering of the Free-Energy Surface of a Small Peptide

Engineering the free-energy surfaces (FES) of proteins and peptides is central to controlling conformational ensembles and their responses to perturbations. However, predicting how chemical modifications such as point mutations reshape the FES and shift conformational equilibria remains challenging, particularly in data-scarce settings. Building on the Collective Variables for Free Energy Surface Tailoring (CV-FEST) framework, we develop a computational approach that leverages short, unbiased molecular dynamics trajectories to guide mutation analysis. Using the ten-residue beta-hairpin CLN025 and a systematic library of its single-point mutants, we apply Harmonic Linear Discriminant Analysis (HLDA) to extract collective variables from the conformational data. We find that the HLDA eigenvector learned solely from short wild-type trajectories provides residue-level insight into the propensity of mutations at specific positions to thermodynamically stabilize or destabilize the folded state. Extending this analysis, we show that shifts in the leading HLDA eigenvalue across mutants, a measure of changes in separability between the conformational ensembles along the HLDA coordinate, correlate strongly with mutation-induced changes in the free-energy difference between states, as reflected in melting temperatures. Benchmarked against Replica Exchange Molecular Dynamics simulations, these findings suggest a promising and computationally affordable route toward guiding the engineering of biomolecular free-energy landscapes.

physics.bio-ph↗

arXiv2026

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

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.

physics.bio-ph↗

arXiv2025

Scaling Up Bayesian DAG Sampling

Bayesian inference of Bayesian network structures is often performed by sampling directed acyclic graphs along an appropriately constructed Markov chain. We present two techniques to improve sampling. First, we give an efficient implementation of basic moves, which add, delete, or reverse a single arc. Second, we expedite summing over parent sets, an expensive task required for more sophisticated moves: we devise a preprocessing method to prune possible parent sets so as to approximately preserve the sums. Our empirical study shows that our techniques can yield substantial efficiency gains compared to previous methods.

cs.LG↗