DockBench: An Integrated Informatic Platform Bridging the Gap between the Robust Validation of Docking Protocols and Virtual Screening Simulations

Virtual screening (VS) is a computational methodology that streamlines the drug discovery process by reducing costs and required resources through the in silico identification of potential drug candidates. Structure-based VS (SBVS) exploits knowledge about the three-dimensional (3D) structure of protein targets and uses the docking methodology as search engine for novel hits. The success of a SBVS campaign strongly depends upon the accuracy of the docking protocol used to select the candidates from large chemical libraries. The identification of suitable protocols is therefore a crucial step in the setup of SBVS experiments. Carrying out extensive benchmark studies, however, is usually a tangled task that requires users’ proficiency in handling different file formats and philosophies at the basis of the plethora of existing software packages. We present here DockBench 1.0, a platform available free of charge that eases the pipeline by automating the entire procedure, from docking benchmark to VS setups. In its current implementation, DockBench 1.0 handles seven docking software packages and offers the possibility to test up to seventeen different protocols. The main features of our platform are presented here and the results of the benchmark study of human Checkpoint kinase 1 (hChk1) are discussed as validation test.

[1]  J. Breed,et al.  Discovery of checkpoint kinase inhibitor (S)-5-(3-fluorophenyl)-N-(piperidin-3-yl)-3-ureidothiophene-2-carboxamide (AZD7762) by structure-based design and optimization of thiophenecarboxamide ureas. , 2012, Journal of medicinal chemistry.

[2]  T. Fischmann,et al.  Discovery of pyrazolo[1,5-a]pyrimidine-based CHK1 inhibitors: a template-based approach--part 2. , 2011, Bioorganic & medicinal chemistry letters.

[3]  Richard D. Taylor,et al.  Improved protein–ligand docking using GOLD , 2003, Proteins.

[4]  Peter Willett,et al.  Comparison of Ranking Methods for Virtual Screening in Lead-Discovery Programs , 2003, J. Chem. Inf. Comput. Sci..

[5]  D Horvath,et al.  A virtual screening approach applied to the search for trypanothione reductase inhibitors. , 1997, Journal of medicinal chemistry.

[6]  Thomas A. Halgren.  Merck molecular force field. I. Basis, form, scope, parameterization, and performance of MMFF94 , 1996, J. Comput. Chem..

[7]  Chris Morley,et al.  Open Babel: An open chemical toolbox , 2011, J. Cheminformatics.

[8]  Junmei Wang,et al.  How well does a restrained electrostatic potential (RESP) model perform in calculating conformational energies of organic and biological molecules? , 2000, J. Comput. Chem..

[9]  Jon Read,et al.  Synthesis and evaluation of triazolones as checkpoint kinase 1 inhibitors. , 2012, Bioorganic & medicinal chemistry letters.

[10]  Gerald W. Shipps,et al.  Structure-based design and optimization of 2-aminothiazole-4-carboxamide as a new class of CHK1 inhibitors. , 2013, Bioorganic & medicinal chemistry letters.

[11]  Robin Taylor,et al.  Comparing protein–ligand docking programs is difficult , 2005, Proteins.

[12]  T. N. Bhat,et al.  The Protein Data Bank , 2000, Nucleic Acids Res..

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

[14]  J. Stewart.  Optimization of parameters for semiempirical methods I. Method , 1989 .

[15]  Douglas R. Houston,et al.  Consensus Docking: Improving the Reliability of Docking in a Virtual Screening Context , 2013, J. Chem. Inf. Model..

[16]  Hege S. Beard,et al.  Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. , 2004, Journal of medicinal chemistry.

[17]  James J P Stewart,et al.  Optimization of parameters for semiempirical methods IV: extension of MNDO, AM1, and PM3 to more main group elements , 2004, Journal of molecular modeling.

[18]  J. Stewart.  Optimization of parameters for semiempirical methods. III Extension of PM3 to Be, Mg, Zn, Ga, Ge, As, Se, Cd, In, Sn, Sb, Te, Hg, Tl, Pb, and Bi , 1991 .

[19]  J. Bajorath,et al.  Docking and scoring in virtual screening for drug discovery: methods and applications , 2004, Nature Reviews Drug Discovery.

[20]  Laura Sepp-Lorenzino,et al.  Pyridyl aminothiazoles as potent inhibitors of Chk1 with slow dissociation rates. , 2012, Bioorganic & medicinal chemistry letters.

[21]  Michael Reilly,et al.  Development of 6-substituted indolylquinolinones as potent Chek1 kinase inhibitors. , 2006, Bioorganic & medicinal chemistry letters.

[22]  Laura Sepp-Lorenzino,et al.  Development of thioquinazolinones, allosteric Chk1 kinase inhibitors. , 2009, Bioorganic & medicinal chemistry letters.

[23]  Matthew P. Repasky,et al.  Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. , 2004, Journal of medicinal chemistry.

[24]  P. Labute.  proteins STRUCTURE O FUNCTION O BIOINFORMATICS Protonate3D: Assignment of ionization , 2013 .

[25]  Michael J Bower,et al.  Structural basis for Chk1 inhibition by UCN-01. , 2002, The Journal of biological chemistry.

[26]  K. Wanner,et al.  Methods and Principles in Medicinal Chemistry , 2007 .

[27]  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..

[28]  Xavier Barril,et al.  rDock: A Fast, Versatile and Open Source Program for Docking Ligands to Proteins and Nucleic Acids , 2014, PLoS Comput. Biol..

[29]  Lawrence C Kuo,et al.  Optimization of a pyrazoloquinolinone class of Chk1 kinase inhibitors. , 2007, Bioorganic & medicinal chemistry letters.

[30]  T. Fischmann,et al.  Discovery of pyrazolo[1,5-a]pyrimidine-based CHK1 inhibitors: a template-based approach--part 1. , 2011, Bioorganic & medicinal chemistry letters.

[31]  J. Stewart.  Optimization of parameters for semiempirical methods II. Applications , 1989 .

[32]  Thomas Stützle,et al.  PLANTS: Application of Ant Colony Optimization to Structure-Based Drug Design , 2006, ANTS Workshop.

[33]  Gary Box,et al.  Structure-Guided Evolution of Potent and Selective CHK1 Inhibitors through Scaffold Morphing , 2011, Journal of medicinal chemistry.

[34]  Stefano Moro,et al.  Alternative Quality Assessment Strategy to Compare Performances of GPCR-Ligand Docking Protocols: The Human Adenosine A2A Receptor as a Case Study , 2014, J. Chem. Inf. Model..

[35]  Jiri Bartek,et al.  Chk1 and Chk2 kinases in checkpoint control and cancer. , 2003, Cancer cell.

[36]  David Parry,et al.  Design, synthesis and SAR of thienopyridines as potent CHK1 inhibitors. , 2010, Bioorganic & medicinal chemistry letters.

[37]  S. Elledge,et al.  Conservation of the Chk1 checkpoint pathway in mammals: linkage of DNA damage to Cdk regulation through Cdc25. , 1997, Science.

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

[39]  Markus Lill,et al.  Virtual screening in drug design. , 2013, Methods in molecular biology.

[40]  Ole Tange,et al.  GNU Parallel: The Command-Line Power Tool , 2011, login Usenix Mag..