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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

Corresponding Author:abhijit.gupta@students.iiserpune.ac.in

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Arnab Mukherjee
Indian Institute of Science Education Research Pune Centre of Excellence in Epigenetics
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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.
03 Nov 2021Submitted to PROTEINS: Structure, Function, and Bioinformatics
05 Nov 2021Submission Checks Completed
05 Nov 2021Assigned to Editor
10 Nov 2021Reviewer(s) Assigned
18 Dec 2021Review(s) Completed, Editorial Evaluation Pending
06 Jan 2022Editorial Decision: Revise Major
06 Mar 20221st Revision Received
06 Mar 2022Submission Checks Completed
06 Mar 2022Assigned to Editor
06 Mar 2022Reviewer(s) Assigned
01 Apr 2022Review(s) Completed, Editorial Evaluation Pending
01 Apr 2022Editorial Decision: Accept
Sep 2022Published in Proteins: Structure, Function, and Bioinformatics volume 90 issue 9 on pages 1669-1683. 10.1002/prot.26345