Reliability analysis and optimization of the consensus docking approach for the development of virtual screening studies

Abstract Ligand-protein docking is one of the most common techniques used in virtual screening campaigns. Despite the large number of docking software available, there is still the need of improving the efficacy of docking-based virtual screenings. To date, only very few studies evaluated the possibility of combining the results of different docking methods to achieve higher success rates in virtual screening studies (consensus docking). In order to better understand the range of applicability of this approach, we carried out an extensive enriched database analysis using the DUD dataset. The consensus docking protocol was then refined by applying modifications concerning the calculation of pose consensus and the combination of docking methods included in the procedure. The results obtained suggest that this approach performs as well as the best available methods found in literature, confirming the idea that this procedure can be profitably used for the identification of new hit compounds.

[1]  Alina Bora,et al.  PLS-DA - Docking Optimized Combined Energetic Terms (PLSDA-DOCET) Protocol: A Brief Evaluation , 2011, J. Chem. Inf. Model..

[2]  C. David Andersson,et al.  Postprocessing of Docked Protein-Ligand Complexes Using Implicit Solvation Models , 2011, J. Chem. Inf. Model..

[3]  Luigi Cavallo,et al.  Ranking multiple docking solutions based on the conservation of inter‐residue contacts , 2013, Proteins.

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

[5]  Heloisa dos Santos Muniz,et al.  Ligand- and receptor-based docking with LiBELa , 2015, Journal of Computer-Aided Molecular Design.

[6]  Fedor N. Novikov,et al.  Lead Finder docking and virtual screening evaluation with Astex and DUD test sets , 2012, Journal of Computer-Aided Molecular Design.

[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]  Zhilong Xiu,et al.  Rescoring ligand docking poses. , 2010, Current opinion in drug discovery & development.

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

[10]  Hwangseo Park,et al.  Consensus Scoring Approach To Identify the Inhibitors of AMP-Activated Protein Kinase α2 with Virtual Screening , 2014, J. Chem. Inf. Model..

[11]  Reiji Teramoto,et al.  Structure-Based Virtual Screening with Supervised Consensus Scoring: Evaluation of Pose Prediction and Enrichment Factors , 2008, J. Chem. Inf. Model..

[12]  Tiziano Tuccinardi,et al.  Application of a FLAP-Consensus Docking Mixed Strategy for the Identification of New Fatty Acid Amide Hydrolase Inhibitors , 2015, J. Chem. Inf. Model..

[13]  Miklos Feher,et al.  Consensus scoring for protein-ligand interactions. , 2006, Drug discovery today.

[14]  Jessica Holien,et al.  Improvements, trends, and new ideas in molecular docking: 2012–2013 in review , 2015, Journal of molecular recognition : JMR.

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

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

[17]  Sebastian G. Rohrer,et al.  Maximum Unbiased Validation (MUV) Data Sets for Virtual Screening Based on PubChem Bioactivity Data , 2009, J. Chem. Inf. Model..

[18]  Jung-Hsin Lin,et al.  Scoring functions for prediction of protein-ligand interactions. , 2013, Current pharmaceutical design.

[19]  Oliver F. Lange,et al.  Improvement of Virtual Screening Results by Docking Data Feature Analysis , 2014, J. Chem. Inf. Model..

[20]  Sudipto Mukherjee,et al.  Implementation and evaluation of a docking‐rescoring method using molecular footprint comparisons , 2011, J. Comput. Chem..

[21]  J. Irwin,et al.  Docking and chemoinformatic screens for new ligands and targets. , 2009, Current opinion in biotechnology.

[22]  Tao Wang,et al.  Fragment-based drug discovery and molecular docking in drug design. , 2015, Current pharmaceutical biotechnology.

[23]  Kari Salokas,et al.  Ultrafast protein structure-based virtual screening with Panther , 2015, Journal of Computer-Aided Molecular Design.

[24]  H. Stark,et al.  Refined Docking as a Valuable Tool for Lead Optimization: Application to Histamine H3 Receptor Antagonists , 2008, Archiv der Pharmazie.

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

[26]  Flavio Rizzolio,et al.  Identification of New Fyn Kinase Inhibitors Using a FLAP-Based Approach , 2013, J. Chem. Inf. Model..

[27]  J. Ramanujam,et al.  GeauxDock: A novel approach for mixed‐resolution ligand docking using a descriptor‐based force field , 2015, J. Comput. Chem..

[28]  Lei Li,et al.  Improved protein-ligand binding affinity prediction by using a curvature-dependent surface-area model , 2014, Bioinform..

[29]  Gennady Poda,et al.  A New, Improved Hybrid Scoring Function for Molecular Docking and Scoring Based on AutoDock and AutoDock Vina , 2016, Chemical biology & drug design.

[30]  Joannis Apostolakis,et al.  GlamDock: Development and Validation of a New Docking Tool on Several Thousand Protein-Ligand Complexes , 2007, J. Chem. Inf. Model..

[31]  Peter Monecke,et al.  Accounting for conformational variability in protein-ligand docking with NMR-guided rescoring. , 2013, Journal of the American Chemical Society.

[32]  T. Tuccinardi.  Docking-based virtual screening: recent developments. , 2009, Combinatorial chemistry & high throughput screening.

[33]  Christine Humblet,et al.  Lead optimization via high-throughput molecular docking. , 2007, Current opinion in drug discovery & development.

[34]  Anthony Nicholls,et al.  Conformer Generation with OMEGA: Learning from the Data Set and the Analysis of Failures , 2012, J. Chem. Inf. Model..

[35]  Tanya Singh,et al.  AADS - An Automated Active Site Identification, Docking, and Scoring Protocol for Protein Targets Based on Physicochemical Descriptors , 2011, J. Chem. Inf. Model..

[36]  Reiji Teramoto,et al.  Supervised Consensus Scoring for Docking and Virtual Screening , 2007, J. Chem. Inf. Model..

[37]  Thomas Stützle,et al.  pharmACOphore: multiple flexible ligand alignment based on ant colony optimization , 2010, J. Cheminformatics.

[38]  Natalia Zaitseva,et al.  Accounting for Intraligand Interactions in Flexible Ligand Docking with a PMF-Based Scoring Function , 2015, J. Chem. Inf. Model..

[39]  Benjamin A. Ellingson,et al.  Conformer Generation with OMEGA: Algorithm and Validation Using High Quality Structures from the Protein Databank and Cambridge Structural Database , 2010, J. Chem. Inf. Model..

[40]  Kam Y. J. Zhang,et al.  Hierarchical virtual screening approaches in small molecule drug discovery , 2014, Methods.

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

[42]  Rafael Najmanovich,et al.  FlexAID: Revisiting Docking on Non-Native-Complex Structures , 2015, J. Chem. Inf. Model..

[43]  Walter Filgueira de Azevedo,et al.  Molecular docking algorithms. , 2008, Current drug targets.

[44]  Jürgen Bajorath,et al.  Analysis of structure-based virtual screening studies and characterization of identified active compounds. , 2012, Future medicinal chemistry.

[45]  Xiaoqin Zou,et al.  Scoring functions and their evaluation methods for protein-ligand docking: recent advances and future directions. , 2010, Physical chemistry chemical physics : PCCP.

[46]  Dmitri B. Kireev,et al.  Structural Protein–Ligand Interaction Fingerprints (SPLIF) for Structure-Based Virtual Screening: Method and Benchmark Study , 2014, J. Chem. Inf. Model..

[47]  Thomas Stützle,et al.  pharmACOphore: multiple flexible ligand alignment based on ant colony optimization , 2010, J. Cheminformatics.

[48]  Dragos Horvath,et al.  S4MPLE—Sampler for Multiple Protein-Ligand Entities: Methodology and Rigid-Site Docking Benchmarking , 2015, Molecules.

[49]  Ji-Bo Wang,et al.  Knowledge-Based Strategy to Improve Ligand Pose Prediction Accuracy for Lead Optimization , 2015, J. Chem. Inf. Model..

[50]  Lingling Jiang,et al.  Pharmacophore-Based Similarity Scoring for DOCK , 2014, The journal of physical chemistry. B.

[51]  Leonardo L. G. Ferreira,et al.  Molecular Docking and Structure-Based Drug Design Strategies , 2015, Molecules.

[52]  Tiziano Tuccinardi,et al.  Extensive Consensus Docking Evaluation for Ligand Pose Prediction and Virtual Screening Studies , 2014, J. Chem. Inf. Model..

[53]  M. Murcko,et al.  Consensus scoring: A method for obtaining improved hit rates from docking databases of three-dimensional structures into proteins. , 1999, Journal of medicinal chemistry.

[54]  J. Irwin,et al.  Benchmarking sets for molecular docking. , 2006, Journal of medicinal chemistry.

[55]  Markus H J Seifert,et al.  Targeted scoring functions for virtual screening. , 2009, Drug discovery today.

[56]  Pamela F. Jones,et al.  VORFFIP-Driven Dock: V-D2OCK, a Fast and Accurate Protein Docking Strategy , 2015, PloS one.

[57]  Steven W. Muchmore,et al.  POSIT: Flexible Shape-Guided Docking For Pose Prediction , 2015, J. Chem. Inf. Model..