Structures of proteins and protein–protein complexes are determined by the same physical principles and thus share a number of similarities. At the same time, there could be differences because in order to function, proteins interact with other molecules, undergo conformations changes, and so forth, which might impose different restraints on the tertiary versus quaternary structures. This study focuses on structural properties of protein–protein interfaces in comparison with the protein core, based on the wealth of currently available structural data and new structure‐based approaches. The results showed that physicochemical characteristics, such as amino acid composition, residue–residue contact preferences, and hydrophilicity/hydrophobicity distributions, are similar in protein core and protein–protein interfaces. On the other hand, characteristics that reflect the evolutionary pressure, such as structural composition and packing, are largely different. The results provide important insight into fundamental properties of protein structure and function. At the same time, the results contribute to better understanding of the ways to dock proteins. Recent progress in predicting structures of individual proteins follows the advancement of deep learning techniques and new approaches to residue coevolution data. Protein core could potentially provide large amounts of data for application of the deep learning to docking. However, our results showed that the core motifs are significantly different from those at protein–protein interfaces, and thus may not be directly useful for docking. At the same time, such difference may help to overcome a major obstacle in application of the coevolutionary data to docking—discrimination of the intramolecular information not directly relevant to docking.

中文翻译：

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蛋白质核心和蛋白质-蛋白质界面的结构基序不同

蛋白质和蛋白质-蛋白质复合物的结构由相同的物理原理决定，因此具有许多相似之处。同时，可能存在差异，因为为了发挥作用，蛋白质与其他分子相互作用，经历构象变化等，这可能对三级和四级结构施加不同的限制。本研究基于当前可用的大量结构数据和基于结构的新方法，重点关注蛋白质-蛋白质界面与蛋白质核心的结构特性。结果表明，蛋白质核心和蛋白质-蛋白质界面的物理化学特征，如氨基酸组成、残基-残基接触偏好和亲水性/疏水性分布是相似的。另一方面，反映进化压力的特征，如结构组成和堆积，有很大不同。结果为蛋白质结构和功能的基本特性提供了重要的见解。同时，这些结果有助于更好地理解对接蛋白质的方法。随着深度学习技术的进步和残基协同进化数据的新方法的进步，预测单个蛋白质结构的最新进展。蛋白质核心可能为深度学习在对接中的应用提供大量数据。然而，我们的结果表明，核心基序与蛋白质-蛋白质界面处的核心基序显着不同，因此可能不能直接用于对接。同时，