Assessing the accuracy of binding pose prediction for kinase proteins and 7-azaindole inhibitors: a study with AutoDock4, Vina, DOCK 6, and GNINA 1.0.

ABSTRACT: Comparative benchmarking of molecular docking tools is vital for assessing the reliability of virtual screening and binding pose prediction in drug discovery. Our study evaluates the performance of four open-source docking programs: AutoDock4, AutoDock Vina, DOCK 6, and GNINA 1.0 in predicting binding poses of 7-azaindole derivative compounds across 70 kinase-ligand complexes from the RCSB Protein Data Bank. These compounds were selected due to the azaindole moiety's ability to mimic adenosine triphosphate (ATP), which is the natural substrate for kinases to form hydrogen bonds in kinase hinge regions. Docking was conducted using both rigid and flexible receptor conditions. Binding pose accuracy was quantified using atom-to-atom root mean square deviation (RMSD) and center-of-mass (CoM) RM

SUBMITTER: Tripathi A 

PROVIDER: S-EPMC12661494 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature