Shape guides colloidal nanoparticles to form complex assemblies, but its role in defining interfaces in biomolecular complexes is less clear. In this work, we isolate the role of shape in protein complexes by studying the reversible binding processes of 46 protein dimer pairs, and investigate when entropic effects from shape complementarity alone are sufficient to predict the native protein binding interface. We employ depletants using a generic, implicit depletion model to amplify the magnitude of the entropic forces arising from lock-and-key binding and isolate the effect of shape complementarity in protein dimerization. For 13% of the complexes studied here, protein shape is sufficient to predict native complexes as equilibrium assemblies. We elucidate the results by analyzing the importance of competing binding configurations and how it affects the assembly. A machine learning classifier, with a precision of 89.14% and a recall of 77.11%, is able to identify the cases where shape alone predicts the native protein interface.

中文翻译：

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互补形状在蛋白质二聚化中的作用

形状引导胶体纳米粒子形成复杂的组件，但它在定义生物分子复合物界面方面的作用尚不清楚。在这项工作中，我们通过研究 46 个蛋白质二聚体对的可逆结合过程来分离形状在蛋白质复合物中的作用，并研究仅来自形状互补性的熵效应何时足以预测天然蛋白质结合界面。我们使用一个通用的、隐式的耗尽模型来使用耗尽剂来放大锁键结合产生的熵力的大小，并隔离蛋白质二聚化中形状互补的影响。对于此处研究的 13% 的复合物，蛋白质形状足以将天然复合物预测为平衡组件。我们通过分析竞争结合配置的重要性及其如何影响组装来阐明结果。机器学习分类器的精度为 89.14%，召回率为 77.11%，能够识别仅靠形状预测天然蛋白质界面的情况。