The hydrophobicity of proteins and similar surfaces, which display chemical heterogeneity at the nanoscale, drives countless aqueous interactions and assemblies. However, predicting how surface chemical patterning influences hydrophobicity remains a challenge. Here, we address this challenge by using molecular simulations and machine learning to characterize and model the hydrophobicity of a diverse library of patterned surfaces, spanning a wide range of sizes, shapes, and chemical compositions. We find that simple models, based only on polar content, are inaccurate, whereas complex neural network models are accurate but challenging to interpret. However, by systematically incorporating chemical correlations between surface groups into our models, we are able to construct a series of minimal models of hydrophobicity, which are both accurate and interpretable. Our models highlight that the number of proximal polar groups is a key determinant of hydrophobicity and that polar neighbors enhance hydrophobicity. Although our minimal models are trained on particular patch size and shape, their interpretability enables us to generalize them to rectangular patches of all shapes and sizes. We also demonstrate how our models can be used to predict hot-spot locations with the largest marginal contributions to hydrophobicity and to design chemical patterns that have a fixed polar content but vary widely in their hydrophobicity. Our data-driven models and the principles they furnish for modulating hydrophobicity could facilitate the design of novel materials and engineered proteins with stronger interactions or enhanced solubilities.

中文翻译：

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了解纳米级化学图案化与疏水性之间的关系

蛋白质和类似表面的疏水性在纳米尺度上显示出化学异质性，驱动着无数水相相互作用和组装。然而，预测表面化学图案化如何影响疏水性仍然是一个挑战。在这里，我们通过使用分子模拟和机器学习来表征和模拟各种不同尺寸、形状和化学成分的图案化表面库的疏水性来应对这一挑战。我们发现仅基于极性内容的简单模型是不准确的，而复杂的神经网络模型是准确的但难以解释。然而，通过系统地将表面基团之间的化学相关性纳入我们的模型，我们能够构建一系列疏水性的最小模型，既准确又可解释。我们的模型强调，近端极性基团的数量是疏水性的关键决定因素，而极性邻居增强了疏水性。尽管我们的最小模型是针对特定的补丁大小和形状进行训练的，但它们的可解释性使我们能够将它们推广到所有形状和大小的矩形补丁。我们还展示了我们的模型如何用于预测对疏水性具有最大边际贡献的热点位置，以及如何设计具有固定极性含量但疏水性差异很大的化学模式。我们的数据驱动模型及其为调节疏水性提供的原理可以促进具有更强相互作用或增强溶解度的新型材料和工程蛋白质的设计。我们的模型强调，近端极性基团的数量是疏水性的关键决定因素，而极性邻居增强了疏水性。尽管我们的最小模型是针对特定的补丁大小和形状进行训练的，但它们的可解释性使我们能够将它们推广到所有形状和大小的矩形补丁。我们还展示了我们的模型如何用于预测对疏水性具有最大边际贡献的热点位置，以及如何设计具有固定极性含量但疏水性差异很大的化学模式。我们的数据驱动模型及其为调节疏水性提供的原理可以促进具有更强相互作用或增强溶解度的新型材料和工程蛋白质的设计。我们的模型强调，近端极性基团的数量是疏水性的关键决定因素，而极性邻居增强了疏水性。尽管我们的最小模型是针对特定的补丁大小和形状进行训练的，但它们的可解释性使我们能够将它们推广到所有形状和大小的矩形补丁。我们还展示了我们的模型如何用于预测对疏水性具有最大边际贡献的热点位置，以及如何设计具有固定极性含量但疏水性差异很大的化学模式。我们的数据驱动模型及其为调节疏水性提供的原理可以促进具有更强相互作用或增强溶解度的新型材料和工程蛋白质的设计。