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

Abstract

Abstract Experimental detection of residues critical for protein-protein interactions (PPI) is a time-consuming, 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-hotspotID, 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 show that PPI-hotspotID outperformed FTMap and SPOTONE, the only available webservers for predicting PPI hotspots given free protein structures and sequences, respectively. It also outperformed AlphaFold-Multimer in detecting PPI-hot spots using predicted interfaces. When combined with the AlphaFold-Multimer-predicted interface residues, PPI-HotspotID, yielded better performance than either method alone. Furthermore, we experimentally verified the PPI-hot spots of eukaryotic elongation factor 2 predicted by PPI-hotspotID. Notably, PPI-hotspotID 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-hotspotID 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 and the source code for PPI-hotspotID are available at https://ppihotspotid.limlab.dnsalias.org/ and https://github.com/wrigjz/ppihotspotid/, respectively.