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

Abstract“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.

[1]  A. Rollett,et al.  The Monte Carlo Method , 2004 .

[2]  M. Rarey,et al.  FlexX‐Scan: Fast, structure‐based virtual screening , 2004, Proteins.

[3]  Renxiao Wang,et al.  The PDBbind database: collection of binding affinities for protein-ligand complexes with known three-dimensional structures. , 2004, Journal of medicinal chemistry.

[4]  Y. Mu,et al.  Reduction of False Positives in Structure-Based Virtual Screening When Receptor Plasticity Is Considered , 2015, Molecules.

[5]  Dan Li,et al.  Comprehensive evaluation of ten docking programs on a diverse set of protein-ligand complexes: the prediction accuracy of sampling power and scoring power. , 2016, Physical chemistry chemical physics : PCCP.

[6]  Chee Keong Kwoh,et al.  QuickVina: Accelerating AutoDock Vina Using Gradient-Based Heuristics for Global Optimization , 2012, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[7]  Arthur J. Olson,et al.  AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading , 2009, J. Comput. Chem..

[8]  Jie Li,et al.  PDB-wide collection of binding data: current status of the PDBbind database , 2015, Bioinform..

[9]  Renxiao Wang,et al.  The PDBbind database: methodologies and updates. , 2005, Journal of medicinal chemistry.

[10]  Mark A. Murcko,et al.  Virtual screening : an overview , 1998 .

[11]  L. Kavraki,et al.  DINC: A new AutoDock-based protocol for docking large ligands , 2013, BMC Structural Biology.

[12]  Chee Keong Kwoh,et al.  Fast, accurate, and reliable molecular docking with QuickVina 2 , 2015, Bioinform..

[13]  D. van der Spoel,et al.  Efficient docking of peptides to proteins without prior knowledge of the binding site , 2002, Protein science : a publication of the Protein Society.

[14]  David S. Goodsell,et al.  AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility , 2009, J. Comput. Chem..

[15]  Chaok Seok,et al.  GalaxyDock: Protein-Ligand Docking with Flexible Protein Side-chains , 2012, J. Chem. Inf. Model..

[16]  David S. Goodsell,et al.  AutoDockFR: Advances in Protein-Ligand Docking with Explicitly Specified Binding Site Flexibility , 2015, PLoS Comput. Biol..

[17]  M F Sanner,et al.  Python: a programming language for software integration and development. , 1999, Journal of molecular graphics & modelling.