Parallel strategies for an inverse docking method

Molecular docking is a widely used computational technique that allows studying structure-based interactions complexes between biological objects at the molecular scale. The purpose of the current work is to develop a set of tools that allows performing inverse docking, i.e., to test at a large scale a chemical ligand on a large dataset of proteins, which has several applications on the field of drug research. We developed different strategies to parallelize/distribute the docking procedure, as a way to efficiently exploit the computational performance of multi-core and multi-machine (cluster) environments. The experiments conducted to compare these different strategies encourage the search for decomposing strategies as a way to improve the execution of inverse docking.

[1]  Csaba Hetényi,et al.  Toward prediction of functional protein pockets using blind docking and pocket search algorithms , 2011, Protein science : a publication of the Protein Society.

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

[3]  Thomas Lengauer,et al.  A fast flexible docking method using an incremental construction algorithm. , 1996, Journal of molecular biology.

[4]  R. Sanchez,et al.  Improving accuracy and efficiency of blind protein‐ligand docking by focusing on predicted binding sites , 2009, Proteins.

[5]  Aurélien Grosdidier,et al.  Blind docking of 260 protein–ligand complexes with EADock 2.0 , 2009, J. Comput. Chem..

[6]  David S. Goodsell,et al.  A semiempirical free energy force field with charge‐based desolvation , 2007, J. Comput. Chem..

[7]  Stefano Forli,et al.  Virtual screening with AutoDock: theory and practice , 2010, Expert opinion on drug discovery.

[8]  Thomas Stützle,et al.  Empirical Scoring Functions for Advanced Protein-Ligand Docking with PLANTS , 2009, J. Chem. Inf. Model..

[9]  R Abagyan,et al.  High-throughput docking for lead generation. , 2001, Current opinion in chemical biology.

[10]  F. Allen.  The Cambridge Structural Database: a quarter of a million crystal structures and rising. , 2002, Acta crystallographica. Section B, Structural science.

[11]  Brian K. Shoichet,et al.  ZINC - A Free Database of Commercially Available Compounds for Virtual Screening , 2005, J. Chem. Inf. Model..

[12]  Ben M. Webb,et al.  Comparative Protein Structure Modeling Using MODELLER , 2007, Current protocols in protein science.

[13]  G. Klebe.  Virtual ligand screening: strategies, perspectives and limitations , 2006, Drug Discovery Today.

[14]  B. Iorga,et al.  Acetylcholine nicotinic receptors: finding the putative binding site of allosteric modulators using the “blind docking” approach , 2006, Journal of molecular modeling.

[15]  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.

[16]  Matthew P. Repasky,et al.  Extra precision glide: docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexes. , 2006, Journal of medicinal chemistry.

[17]  Richard M. Jackson,et al.  LigMatch: A Multiple Structure-Based Ligand Matching Method for 3D Virtual Screening , 2009, J. Chem. Inf. Model..

[18]  John Moult,et al.  A decade of CASP: progress, bottlenecks and prognosis in protein structure prediction. , 2005, Current opinion in structural biology.

[19]  Luiz Angelo Steffenel,et al.  CONFIIT: a middleware for peer-to-peer computing , 2009, The Journal of Supercomputing.

[20]  Michael M. Mysinger,et al.  Automated Docking Screens: A Feasibility Study , 2009, Journal of medicinal chemistry.

[21]  Michael Nilges,et al.  Comparative Evaluation of 3D Virtual Ligand Screening Methods: Impact of the Molecular Alignment on Enrichment , 2010, J. Chem. Inf. Model..

[22]  David Baker,et al.  Blind docking of pharmaceutically relevant compounds using RosettaLigand , 2009, Protein science : a publication of the Protein Society.

[23]  Vincent Le Guilloux,et al.  Fpocket: An open source platform for ligand pocket detection , 2009, BMC Bioinformatics.

[24]  D. Goodsell,et al.  Automated docking to multiple target structures: Incorporation of protein mobility and structural water heterogeneity in AutoDock , 2002, Proteins.

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

[26]  Jaques Reifman,et al.  DOVIS: an implementation for high-throughput virtual screening using AutoDock , 2008, BMC Bioinformatics.

[27]  Steven J. M. Jones,et al.  A Computational Approach to Finding Novel Targets for Existing Drugs , 2011, PLoS Comput. Biol..

[28]  D. van der Spoel,et al.  Blind docking of drug‐sized compounds to proteins with up to a thousand residues , 2006, FEBS letters.