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... more
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-h...
