集体变量引导的小肽自由能面工程
Collective Variable-Guided Engineering of the Free-Energy Surface of a Small Peptide
AI总结：
本文提出了一种基于集体变量的计算方法，利用短分子动力学轨迹分析突变对小肽自由能面的影响，揭示突变对折叠状态稳定性的作用。
摘要：
对蛋白质和肽的自由能面（FES）进行工程化是控制构象集合及其对扰动响应的核心。然而，预测化学修饰如点突变如何重塑FES并改变构象平衡仍具挑战性，尤其是在数据稀缺的环境中。基于集体变量用于自由能面定制（CV-FEST）框架，我们开发了一种利用短且无偏分子动力学轨迹来引导突变分析的计算方法。使用含有10个残基的β发夹CLN025及其系统化的单点突变体库，我们应用谐波线性判别分析（HLDA）从构象数据中提取集体变量。我们发现，仅从短野生型轨迹中学习的HLDA特征向量能提供残基层面的见解，说明突变在特定位置对热力学稳定或破坏折叠状态的倾向。进一步分析表明，突变体中主导HLDA特征值的变化，即沿HLDA坐标构象集合分离度的变化，与突变引起的自由能差变化（反映在熔解温度中）有强相关性。与复制交换分子动力学模拟相比，这些发现表明了一种有前景且计算上可行的路径，用于指导生物分子自由能景观的工程化。
英文摘要：
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.
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