We develop new methodological solutions for prediction and study of protein binding sites on the PDB scale, based on graph theoretical approaches, combined with molecular dynamics simulations. A special focus is on the development of new algorithms for the prediction of protein binding sites (ProBiS) and new web tools for modeling of pharmaceutically interesting molecules - ProBiS Tools (algorithm, database, web server). ProBiS differs from other structural alignment algorithms, in that it can align proteins having different overall 3D shapes, or folds, if they only share similar binding sites. ProBiS enables detection of similar binding sites, and can predict molecules of pharmaceutical interest that bind to the protein, which could be developed further into drugs to treat a disease. We develop ProBiS-Fold (Figure 1), a free-web based server (http://insilab.org) for binding site detection for structural human proteome in drug discovery based on our recently developed ProBiS-Dock database (http://probis-dock-database.insilab.org), an extension of the ProBiS-ligands (http://probis.cmm.ki.si) and the ProBiS-CHARMMing web interface for prediction and optimization of ligands in protein binding sites (http://probis.nih.gov), as well on a recent addition, the GenProBiS web server (http://genprobis.insilab.org). ProBiS-Fold an extension of the ProBiS-Dock Database with protein models from the recently developed AlphaFold database which provides open access to protein structure predictions for the human proteome and 20 other key organisms (DeepMind, Google, https://alphafold.ebi.ac.uk) is thus opening up completely new possibilities for drug research on virtually the entire human proteome as well as on proteomes of other species.

ProBiS-Fold: Binding site detection for structural human proteome in drug discovery

ACS Spring 2022

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