Predicting favorable protein-peptide binding events remains a central challenge in biophysics, with continued uncertainty surrounding how nonlocal effects shape the global energy landscape. Here, we introduce peripheral surface information (PSI) entropy, a quantitative measure of the statistical variability in apolar and charged non-interacting surface (NIS) proportions across conformational ensembles. Using energy-directed molecular docking via HADDOCK3 and explicit-solvent molecular dynamics simulations, it is demonstrated that favorable binding partners exhibit emergent, low-entropy N-states (discrete macrostates in NIS state space) indicative of preferential apolar/charged surface configurations. Across dozens of peptides and multiple receptor systems (WW, PDZ, and MDM2 domains), dominant N-states persisted under varied docking parameters and initial conditions. An experimental meta-ensemble of WW domains from 36 high-resolution structures confirmed the presence of dominant NIS modes independent of in silico methodology, suggesting an evolutionary selection pressure toward specific NIS fingerprints. These findings establish PSI entropy as a thermoinformatic descriptor that encodes favorable binding constraints into unique statistical signatures of the NIS.

翻译：预测有利的蛋白质-肽结合事件仍然是生物物理学中的核心挑战，关于非局部效应如何塑造全局能量景观的问题仍存在持续的不确定性。本文引入外周表面信息（PSI）熵，这是一种对构象集合中非极性与带电非相互作用表面（NIS）比例统计变异性的定量度量。通过基于HADDOCK3的能量导向分子对接和显式溶剂分子动力学模拟，研究表明有利的结合伴侣会表现出突现的低熵N态（NIS状态空间中的离散宏观态），这指示了优先的非极性/带电表面构型。在数十种肽段和多个受体系统（WW、PDZ和MDM2结构域）中，主导N态在不同的对接参数和初始条件下持续存在。来自36个高分辨率结构的WW结构域实验元集合证实了主导NIS模式的存在，且与计算方法无关，这表明进化选择压力倾向于特定的NIS指纹。这些发现确立了PSI熵作为一种热信息学描述符，能够将有利的结合约束编码为NIS的独特统计特征。