通过集体变量调制自由能屏障引导肽动力学
Guiding Peptide Kinetics via Collective-Variable Tuning of Free-Energy Barriers
AI总结：
本文提出一种高效框架，通过理性重塑自由能景观控制蛋白质构象动力学，利用HLDA集体变量预测突变对展开过渡速率的影响。
摘要：
尽管人工智能在蛋白质结构预测方面取得了进展，蛋白质功能也强烈受其底层自由能表面的热力学和动力学特征影响。本文提出了一种数据高效的框架，通过理性重塑自由能景观来工程蛋白质构象动力学。该方法基于集体变量自由能表面塑造（CV-FEST）框架，在Chignolin小蛋白的点突变上进行了验证。该方法依赖于基于短分子动力学轨迹的谐波线性判别分析（HLDA）集体变量（CVs），这些轨迹被限制在亚稳折叠和展开盆地内，仅需有限的局部采样而非详尽的稀有事件模拟。值得注意的是，仅从野生型系统中获得的HLDA CV提供了残基层面的评分，预测特定位置的突变是否可能加速或减慢展开过渡。此外，与所推导CV相关的主导HLDA特征值，作为折叠和展开集合之间一维统计分离的定量度量，与突变中的过渡速率显著相关。这些结果表明，突变依赖的动力学效应可以从最小的盆地内采样中推断出来，为通过集体变量设计、自由能表面工程和数据高效的分子模拟指导肽和蛋白质工程提供了一条实用途径。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
