Search

Drug discovery is usually focused on a single protein target; in this process, existing compounds that bind to related proteins are often ignored. We describe ProBiS plugin, extension of our earlier ProBiS-ligands approach, which for a given protein structure allows prediction of its binding sites and, for each binding site, the ligands from similar binding sites in the Protein Data Bank. We developed a new database of precalculated binding site comparisons of about 290000 proteins to allow fast prediction of binding sites in existing proteins. The plugin enables advanced viewing of predicted binding sites, ligands' poses, and their interactions in three-dimensional graphics. Using the InhA query protein, an enoyl reductase enzyme in the Mycobacterium tuberculosis fatty acid biosynthesis pathway, we predicted its possible ligands and assessed their inhibitory activity experimentally. This resulted in three previously unrecognized inhibitors with novel scaffolds, demonstrating the plugin's utility in the early drug discovery process.

• Implemented K-CliqueWeight into the ProBiS-Dock algorithm, enhancing its capabilities for molecular docking. • Efficiently identifies the top N weighted k-cliques, addressing challenges in molecular docking and diverse problem domains. • Demonstrated superior performance of K-CliqueWeight, surpassing alternatives by several orders of magnitude. • Accessible to academia for free at http://insilab.org/kcliqueweight, facilitating widespread use. Molecular docking, a key process in drug discovery, is often used in the discovery of new bioactive compounds. In this technique, small molecules are systematically placed at a protein binding site to identify the ligands with the highest binding affinity. Here we have developed a new graph-theoretical algorithm called K-CliqueWeight. This algorithm efficiently identifies the top N highest weight k-cliques in different types of vertex-weighted graphs and can serve as a building block for various algorithms addressing different problems, including molecular docking. K-CliqueWeight and its variant K-CliqueDynWeight are extensions of our established and widely used maximum clique algorithm. Our new algorithm uses a novel approach to approximate graph coloring and provides efficient upper bounds on the size and weight of a k-clique within the branch-and-bound algorithm. It outperforms alternative methods and often shows a speedup of several orders of magnitude. Rigorous tests with general random graphs and those specifically designed for docking confirm its exceptional performance. K-CliqueWeight has been integrated into the existing ProBiS-Dock algorithm for molecular docking. The algorithm is freely available to the academic community at http://insilab.org/kcliqueweight. [Display omitted] [ABSTRACT FROM AUTHOR]