Protein-Ligand Blind Docking Using QuickVina-W With Inter-Process Spatio-Temporal Integration

By Nafisa Hassan, Amr Alhossary, Yuguang Mu, Chee Keong Kwoh. 2017

312 citations recorded by the source corpus.

“Virtual Screening” is a common step of in silico drug design, where researchers screen a large library of small molecules (ligands) for interesting hits, in a process known as “Docking”. However, docking is a computationally intensive and time-consuming process, usually restricted to small size binding sites (pockets) and small number of interacting residues. When the target site is not known (blind docking), researchers split the docking box into multiple boxes, or repeat the search several times using different seeds, and then merge the results manually. Otherwise, the search time becomes impractically long. In this research, we studied the relation between the search progression and Average Sum of Proximity relative Frequencies (ASoF) of searching threads, which is closely related to the search speed and accuracy. A new inter-process spatio-temporal integration method is employed in Quick Vina 2, resulting in a new docking tool, QuickVina-W, a suitable tool for “blind docking”, (not limited in search space size or number of residues). QuickVina-W is faster than Quick Vina 2, yet better than AutoDock Vina. It should allow researchers to screen huge ligand libraries virtually, in practically short time and with high accuracy without the need to define a target pocket beforehand.

Subfields: Computational Theory and Mathematics, Molecular Biology

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Citations in the corpus

DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking (2022, methodology): “QuickVina-W [15] extends the speed-optimized QuickVina 2 [25] for blind docking.”
EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction (2022, methodology): “, millions) is generated via thorough sampling of possible binding locations and poses (Hassan et al., 2017); then, scoring and ranking steps are used to retrieve the most promising instances; finally, an energy-based fine-tuning method is employed to best fit the ligand in the respective pocket loc”
CB-Dock2: improved protein–ligand blind docking by integrating cavity detection, docking and homologous template fitting (2022, background): “A powerful approach for this purpose is protein–ligand blind docking, which identifies the binding regions of a protein, and simultaneously predicts the binding pose of a molecule (3,4).”
CB-Dock: a web server for cavity detection-guided protein–ligand blind docking (2019, methodology): “For example, popular software SwissDock [30, 31], QuickVina-W [15] and BSP-SLIM [32] provide particularly valuable services for blind docking.”
Do Deep Learning Models Really Outperform Traditional Approaches in Molecular Docking? (2023, methodology): “Molecular docking, given a ligand molecule and a ligand binding site (called ”pocket”) on a protein, predicting the binding mode of the protein-ligand complex, is a widely used technique in drug design, and several molecular docking methods [1, 2, 3, 4] are proposed in the last decades.”