Halfar Radek, Damborský Jiří, Marques Sérgio M, Martinovič Jan
IT4Innovations, VSB - Technical University of Ostrava, 70800, Ostrava, Czech Republic.
Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, 625 00, Brno, Czech Republic.
J Cheminform. 2025 Apr 28;17(1):61. doi: 10.1186/s13321-025-01005-4.
Protein-ligand docking is a computational method routinely used in many structural biology applications. It usually involves one receptor and one ligand. The docking of multiple ligands, however, can be important in several situations, such as the study of synergistic effects, substrate and product inhibition, or competitive binding. This can be a challenging and computationally demanding process. By integrating Particle Swarm Optimization into the established AutoDock Vina framework, we provided a powerful tool capable of accelerating drug discovery, and computational enzymology. Here we present Moldina (Multiple-Ligand Molecular Docking over AutoDock Vina), a new algorithm built upon AutoDock Vina. Through comprehensive testing against AutoDock Vina, the algorithm exhibited comparable accuracy in predicting ligand binding conformations while significantly reducing the computational time up to several hundred times. Moldina and the benchmark data are freely available at https://opencode.it4i.eu/permed/moldina-multiple-ligand-molecular-docking-over-autodock-vina and https://github.com/It4innovations/moldina-multiple-ligand-molecular-docking-over-autodock-vina .
蛋白质-配体对接是一种在许多结构生物学应用中经常使用的计算方法。它通常涉及一个受体和一个配体。然而,在几种情况下,多个配体的对接可能很重要,例如协同效应研究、底物和产物抑制或竞争性结合。这可能是一个具有挑战性且计算要求很高的过程。通过将粒子群优化集成到已建立的AutoDock Vina框架中,我们提供了一个强大的工具,能够加速药物发现和计算酶学。在此,我们展示了Moldina(基于AutoDock Vina的多配体分子对接),这是一种基于AutoDock Vina构建的新算法。通过对AutoDock Vina进行全面测试,该算法在预测配体结合构象时表现出相当的准确性,同时将计算时间显著减少了数百倍。Moldina和基准数据可在https://opencode.it4i.eu/permed/moldina-multiple-ligand-molecular-docking-over-autodock-vina和https://github.com/It4innovations/moldina-multiple-ligand-molecular-docking-over-autodock-vina上免费获取。