Capturing surface complementarity in proteins using unsupervised learning and robust curvature measure

Abhijit Gupta, A. Mukherjee
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引用次数: 1

Abstract

The structure of a protein plays a pivotal role in determining its function. Often, the protein surface's shape and curvature dictate its nature of interaction with other proteins and biomolecules. However, marked by corrugations and roughness, a protein's surface representation poses significant challenges for its curvature‐based characterization. In the present study, we employ unsupervised machine learning to segment the protein surface into patches. To measure the surface curvature of a patch, we present an algebraic sphere fitting method that is fast, accurate, and robust. Moreover, we use local curvatures to show the existence of “shape complementarity” in protein–protein, antigen–antibody, and protein‐ligand interfaces. We believe that the current approach could help understand the relationship between protein structure and its biological function and can be used to find binding partners of a given protein.
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利用无监督学习和鲁棒曲率测量捕获蛋白质的表面互补性
蛋白质的结构在决定其功能方面起着关键作用。通常,蛋白质表面的形状和曲率决定了它与其他蛋白质和生物分子相互作用的性质。然而,以波纹和粗糙度为特征,蛋白质的表面表征对其基于曲率的表征提出了重大挑战。在本研究中,我们采用无监督机器学习将蛋白质表面分割成小块。为了测量一个斑块的表面曲率,我们提出了一种快速、准确和鲁棒的代数球体拟合方法。此外,我们使用局部曲率来证明在蛋白质-蛋白质、抗原-抗体和蛋白质-配体界面中存在“形状互补”。我们相信,目前的方法可以帮助理解蛋白质结构与其生物学功能之间的关系,并可用于寻找给定蛋白质的结合伙伴。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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