Algorithms and web servers for protein binding sites detection in drug discovery

Résumé fourni par la source

Drug discovery is a protracted and demanding process, which can be expedited during its early stages through novel mathematical approaches and modern computing. To tackle this crucial issue, we are developing fresh mathematical solutions aimed at detecting and characterizing protein binding sites, pivotal for new drug discovery. This paper introduces algorithms founded on graph theory which we have devised to scrutinize target biological proteins. These algorithms yield vital data, facilitating the optimization of initial phases in novel drug development. A particular emphasis lies in the creation of pioneering protein binding site prediction algorithms (ProBiS) and innovative web tools for modeling pharmaceutically intriguing molecules—ProBiS tools. These tools have matured into comprehensive graphical resources for the study of proteins in the proteome. ProBiS stands apart from other structural algorithms due to its ability to align proteins with disparate folds, all without prior knowledge of the binding sites. This unique capability enables the identification of analogous binding sites and the prediction of molecular ligands of diverse pharmaceutical relevance. These ligands could potentially progress into drug candidates for treating diseases. Notably, this prediction is based on data sourced from the complete Protein Data Bank (PDB) and the AlphaFold database, encompassing proteins not yet cataloged in the PDB. All ProBiS tools are made available without charge to the academic community through http://insilab.org and https://probis.nih.gov.

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