Quantitative comparison of protein-protein interaction interface using physicochemical feature-based descriptors of surface patches

Front Mol Biosci. 2023 Feb 6:10:1110567. doi: 10.3389/fmolb.2023.1110567. eCollection 2023.


Driving mechanisms of many biological functions in a cell include physical interactions of proteins. As protein-protein interactions (PPIs) are also important in disease development, protein-protein interactions are highlighted in the pharmaceutical industry as possible therapeutic targets in recent years. To understand the variety of protein-protein interactions in a proteome, it is essential to establish a method that can identify similarity and dissimilarity between protein-protein interactions for inferring the binding of similar molecules, including drugs and other proteins. In this study, we developed a novel method, protein-protein interaction-Surfer, which compares and quantifies similarity of local surface regions of protein-protein interactions. protein-protein interaction-Surfer represents a protein-protein interaction surface with overlapping surface patches, each of which is described with a three-dimensional Zernike descriptor (3DZD), a compact mathematical representation of 3D function. 3DZD captures both the 3D shape and physicochemical properties of the protein surface. The performance of protein-protein interaction-Surfer was benchmarked on datasets of protein-protein interactions, where we were able to show that protein-protein interaction-Surfer finds similar potential drug binding regions that do not share sequence and structure similarity. protein-protein interaction-Surfer is available at https://kiharalab.org/ppi-surfer.

Keywords: 3D Zernike descriptor; PPI; PPI drugs; molecular surface; protein-protein interaction; protein-protein interaction (PPI).

Grants and funding

W-HS acknowledges support from the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2020R1F1A1075998, 2020R1A4A1016695, and 2022M3E5F3081268). DK acknowledges supports from the National Institutes of Health (R01GM123055, R01GM133840, and 3R01GM133840-02S1), the National Science Foundation (DMS1614777, CMMI1825941, MCB1925643, DBI2003635, DBI2146026, and IIS2211598) and the Purdue Institute of Drug Discovery. KI acknowledges support from JSPS KAKENHI Grant Numbers 18K11543 and 21H03551. The following grant is also acknowledged; Research Support Project for Life Science and Drug Discovery [Basis for Supporting Innovative Drug Discovery and Life Science Research (BINDS)] from AMED under Grant Number 22ama121029j0001 (to KI and TH).