Capturing Surface Complementarity in Proteins using Unsupervised
Learning and Robust Curvature Measure
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.