PPI-hotspotID: A Method for Detecting Protein-Protein Interaction Hot Spots from the Free Protein Structure

Abstract

Experimental detection of residues critical for protein-protein interactions (PPI) is a timeconsuming, costly, and labor-intensive process. Hence, high-throughput PPI-hot spot prediction methods have been developed, but they have been validated using relatively small datasets, which may compromise their predictive reliability. Here, we introduce PPI-hotspot ID , a novel method for identifying PPI-hot spots using the free protein structure, and validated it on the largest collection of experimentally confirmed PPI-hot spots to date. We explored the possibility of detecting PPI-hot spots using (i) FTMap in the PPI mode, which identifies hot spots on protein-protein interfaces from the free protein structure, and (ii) the interface residues predicted by AlphaFold-Multimer. PPI-hotspot ID yielded better performance than FTMap and SPOTONE, a webserver for predicting PPI-hotspots given the protein sequence. When combined with the AlphaFold-Multimer-predicted interface residues, PPI-Hotspot ID , also yielded better performance than either method alone. Furthermore, we experimentally verified several PPI-hot spots of eukaryotic elongation factor 2 predicted by PPI-hotspot ID . Notably, PPI-hotspot ID unveils PPI-hot spots that are not obvious from complex structures, which only reveal interface residues, thus overlooking PPI-hot spots in indirect contact with binding partners. Thus, PPI-hotspot ID serves as a valuable tool for understanding the mechanisms of PPIs and facilitating the design of novel drugs targeting these interactions. A freely accessible web server is available at https://ppihotspotid.limlab.dnsalias.org/ and the source code for PPI-hotspot ID at https://github.com/wrigjz/ppihotspotid/.