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Capturing Surface Complementarity in Proteins using Unsupervised Learning and Robust Curvature Measure
  • Abhijit Gupta,
  • Arnab Mukherjee
Abhijit Gupta
Indian Institute of Science Education Research Pune Centre of Excellence in Epigenetics
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Arnab Mukherjee
Indian Institute of Science Education Research Pune Centre of Excellence in Epigenetics
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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.

Peer review status:Published

03 Nov 2021Submitted to PROTEINS: Structure, Function, and Bioinformatics
05 Nov 2021Assigned to Editor
05 Nov 2021Submission Checks Completed
10 Nov 2021Reviewer(s) Assigned
Published in SSRN Electronic Journal. 10.2139/ssrn.3784950