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\begin{document}

\title{Capturing Surface Complementarity in Proteins using Unsupervised
Learning and Robust Curvature Measure}



\author[1]{Abhijit Gupta}%
\author[1]{Arnab Mukherjee}%
\affil[1]{Indian Institute of Science Education Research Pune Centre of Excellence in Epigenetics}%


\vspace{-1em}



  
  \date{May 22, 2024}


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\begin{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.%
\end{abstract}%



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\textbf{Hosted file}

\verb`SurfaceCurvature_MS_Proteins.docx`  available at \url{https://authorea.com/users/445509/articles/545010-capturing-surface-complementarity-in-proteins-using-unsupervised-learning-and-robust-curvature-measure}

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