PPI-hotspot for Detecting Protein-protein Interaction Hot Spots from the Free Protein Structure
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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-hotspot, 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 protein structure, and (ii) the interface residues predicted by AlphaFold-Multimer. PPI-hotspot yielded better performance than FTMap and SPOTONE, a webserver for predicting PPI-hot spots given the protein sequence. When combined with the AlphaFold-Multimer-predicted interface residues, PPI-hotspot yielded better performance than either method alone. Furthermore, we experimentally verified several PPI-hotspot-predicted PPI-hot spots of eukaryotic elongation factor 2. Notably, PPI-hotspot can reveal PPI-hot spots not obvious from complex structures, including those in contact with binding partners. PPI-hotspot serves as a valuable tool for understanding PPI mechanisms and aiding drug design. It is available as a web server (https://ppihotspotid.limlab.dnsalias.org/) and open-source code (https://github.com/wrigjz/ppihotspotid/).
Using protein language models for protein interaction hot spot prediction with limited data.
Sargsyan K, Lim C BMC Bioinformatics. 2024; 25(1):115.
PMID: 38493120 PMC: 10943781. DOI: 10.1186/s12859-024-05737-2.