Unexpected similarity between HIV-1 reverse transcriptase and tumor necrosis factor revealed by binding site image processing
Rationalizing the identification of hidden similarities across the repertoire of druggable protein cavities remains a major hurdle to a true proteome-wide structure-based discovery of novel drug candidates. We recently described a new computational approach (ProCare), inspired by numerical image processing, to identify local similarities in fragment-based subpockets. During the validation of the method, we unexpectedly identified a possible similarity in the binding pockets of two unrelated targets, human tumor necrosis factor alpha (TNF-) and HIV-1 reverse transcriptase (HIV-1 RT). Microscale thermophoresis experiments confirmed the ProCare prediction as two of the three tested and FDA-approved HIV-1 RT inhibitors indeed bind to soluble human TNF- trimer. Interestingly, the herein disclosed similarity could be revealed neither by state-of-the-art binding sites comparison methods nor by ligand-based pairwise similarity searches, suggesting that the point cloud registration approach implemented in ProCare, is uniquely suited to identify local and unobvious similarities among totally unrelated targets. AUTHOR SUMMARYComputational comparison of binding sites in proteins can provide insights on potential unrelated proteins that may bind to similar ligands. However, accurate prediction of binding site similarity requires powerful methods, ideally able to detect even local similarities. We herewith applied a recently developed computer vision method to identify an unexpected binding site similarity between two totally unrelated proteins that was confirmed experimentally by in vitro biophysical binding assays. Considering more precisely local similarities can therefore efficiently guide drug discovery, notably to repurpose existing drug candidates.