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Dinesh Kumar Sriramulu,이선구 한국생물공학회 2023 KSBB Journal Vol.38 No.1
Molecular docking method helps predict the protein- ligand binding conformation, but its prediction accuracy is still limited and varies depending on the target ligand's structure and physicochemical properties. Therefore, understanding the ligand-dependent prediction accuracy is crucial in efficiently using the docking tool. In this study, we investigated how the prediction accuracy of AutoDock, a popular molecular docking tool, is affected by the aromaticity of the target ligand structure, ligand torsion number, and ligand hydrophobicity. The ligands with an aromatic ring showed substantially lower prediction accuracy than those without an aromatic ring. The ligands with lower torsion number showed higher prediction accuracy in the ligands with and without an aromatic ring. The hydrophobicity of ligands did not significantly influence the prediction accuracy in the case of ligands with an aromatic ring structure. However, the ligands without ring structure showed a clear difference in prediction accuracy depending on their hydrophobicity. These results are expected to be employed as a reference in molecular docking studies using AutoDock.
Dinesh Kumar Sriramulu,Sangwook Wu,이선구 한국공업화학회 2020 Journal of Industrial and Engineering Chemistry Vol.83 No.-
Molecular docking simulation is a useful tool in the prediction of protein-ligand binding affinity on alarge scale and has great potential in various applicationfields such as virtual screening of potentialdrug molecules. However, the reliability of molecular docking is still weak in the estimation of ligand-binding free energy, which limits the applicability of molecular docking simulation. Ligand torsionnumber is related to theflexibility of ligand and generally incorporated as a crucial variable in thethermodynamic function of binding free energy. In this study, we investigated how the ligand torsionnumber has influence on the binding affinity prediction of AutoDock, a popular molecular dockingsimulation tool. The pKd values of various protein-ligands were estimated by using the binding freeenergy function of AutoDock and compared with their experimental pKd values. The torsion numberdependent comparison showed that the predicted binding affinities were mostly underestimated inthe complexes of higher torsion numbers, whereas the underestimated and overestimated cases wererelatively balanced at relatively lower torsion numbers. A new weight factor for torsion-free energyterm of binding energy function was determined and introduced to make correction to theunderestimation of binding affinity of ligands with high torsion numbers. It is expected that the torsionnumber dependent deviation pattern of AutoDock and its correction strategy are useful in the large-scale validation of protein-ligand binding affinity.