Variability in docking success rates due to dataset preparation

The results of cognate docking with the prepared Astex dataset provided by the organizers of the “Docking and Scoring: A Review of Docking Programs” session at the 241st ACS national meeting are presented. The MOE software with the newly developed GBVI/WSA dG scoring function is used throughout the study. For 80 % of the Astex targets, the MOE docker produces a top-scoring pose within 2 Å of the X-ray structure. For 91 % of the targets a pose within 2 Å of the X-ray structure is produced in the top 30 poses. Docking failures, defined as cases where the top scoring pose is greater than 2 Å from the experimental structure, are shown to be largely due to the absence of bound waters in the source dataset, highlighting the need to include these and other crucial information in future standardized sets. Docking success is shown to depend heavily on data preparation. A “dataset preparation” error of 0.5 kcal/mol is shown to cause fluctuations of over 20 % in docking success rates.

[1]  Anna Tramontano,et al.  Critical assessment of methods of protein structure prediction—Round VII , 2007, Proteins.

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

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

[4]  Stephen Hanessian,et al.  Docking of aminoglycosides to hydrated and flexible RNA. , 2006, Journal of medicinal chemistry.

[5]  J. Guthrie,et al.  A blind challenge for computational solvation free energies: introduction and overview. , 2009, The journal of physical chemistry. B.

[6]  B. Rost,et al.  Critical assessment of methods of protein structure prediction—Round VIII , 2009, Proteins.

[7]  Matthew L. Danielson,et al.  Computer-aided drug design platform using PyMOL , 2011, J. Comput. Aided Mol. Des..

[8]  Peter Kolb,et al.  Docking screens: right for the right reasons? , 2009, Current topics in medicinal chemistry.

[9]  N. Vermeulen,et al.  The role of water molecules in computational drug design. , 2010, Current topics in medicinal chemistry.

[10]  Zhi-Yuan Su,et al.  Predictions of Binding for Dopamine D2 Receptor Antagonists by the SIE Method , 2009, J. Chem. Inf. Model..

[11]  Richard D. Smith,et al.  CSAR Benchmark Exercise of 2010: Combined Evaluation Across All Submitted Scoring Functions , 2011, J. Chem. Inf. Model..

[12]  Xiaoqin Zou,et al.  Advances and Challenges in Protein-Ligand Docking , 2010, International journal of molecular sciences.

[13]  Christopher I. Bayly,et al.  Evaluating Virtual Screening Methods: Good and Bad Metrics for the "Early Recognition" Problem , 2007, J. Chem. Inf. Model..

[14]  Themis Lazaridis,et al.  Water at biomolecular binding interfaces. , 2007, Physical chemistry chemical physics : PCCP.

[15]  Ajay N. Jain,et al.  Recommendations for evaluation of computational methods , 2008, J. Comput. Aided Mol. Des..

[16]  Markus A. Lill,et al.  Solvent Interaction Energy Calculations on Molecular Dynamics Trajectories: Increasing the Efficiency Using Systematic Frame Selection , 2011, J. Chem. Inf. Model..

[17]  Dor Ben-Amotz,et al.  Unraveling water's entropic mysteries: a unified view of nonpolar, polar, and ionic hydration. , 2008, Accounts of chemical research.

[18]  Paul Labute,et al.  The generalized Born/volume integral implicit solvent model: Estimation of the free energy of hydration using London dispersion instead of atomic surface area , 2008, J. Comput. Chem..

[19]  L. Kuhn,et al.  Virtual screening with solvation and ligand-induced complementarity , 2000 .

[20]  Marwen Naïm,et al.  Molecular dynamics-solvated interaction energy studies of protein-protein interactions: the MP1-p14 scaffolding complex. , 2008, Journal of molecular biology.

[21]  Elizabeth Yuriev,et al.  Challenges and advances in computational docking: 2009 in review , 2011, Journal of molecular recognition : JMR.

[22]  C. E. Peishoff,et al.  A critical assessment of docking programs and scoring functions. , 2006, Journal of medicinal chemistry.

[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]  Allan Matte,et al.  Structural and Functional Analysis of Campylobacter jejuni PseG , 2009, The Journal of Biological Chemistry.

[25]  Richard D. Smith,et al.  CSAR Benchmark Exercise of 2010: Selection of the Protein–Ligand Complexes , 2011, J. Chem. Inf. Model..

[26]  Richard D. Taylor,et al.  Modeling water molecules in protein-ligand docking using GOLD. , 2005, Journal of medicinal chemistry.

[27]  Christopher R. Corbeil,et al.  Docking Ligands into Flexible and Solvated Macromolecules. 3. Impact of Input Ligand Conformation, Protein Flexibility, and Water Molecules on the Accuracy of Docking Programs , 2009, J. Chem. Inf. Model..

[28]  Agustina Rodriguez-Granillo,et al.  Interdomain interactions modulate collective dynamics of the metal-binding domains in the Wilson disease protein. , 2010, The journal of physical chemistry. B.

[29]  Paul N. Mortenson,et al.  Diverse, high-quality test set for the validation of protein-ligand docking performance. , 2007, Journal of medicinal chemistry.

[30]  K Fidelis,et al.  A large‐scale experiment to assess protein structure prediction methods , 1995, Proteins.

[31]  Christopher R. Corbeil,et al.  Towards the development of universal, fast and highly accurate docking/scoring methods: a long way to go , 2008, British journal of pharmacology.

[32]  Marvin Waldman,et al.  Lions and tigers and bears, oh my! Three barriers to progress in computer-aided molecular design , 2011, Journal of Computer-Aided Molecular Design.

[33]  S. Wodak,et al.  Assessment of blind predictions of protein–protein interactions: Current status of docking methods , 2003, Proteins.

[34]  Miklos Feher,et al.  Reducing Docking Score Variations Arising from Input Differences , 2010, J. Chem. Inf. Model..

[35]  Christopher R. Corbeil,et al.  Docking Ligands into Flexible and Solvated Macromolecules, 1. Development and Validation of FITTED 1.0 , 2007, J. Chem. Inf. Model..

[36]  Imran Siddiqi,et al.  Solvated Interaction Energy (SIE) for Scoring Protein-Ligand Binding Affinities, 1. Exploring the Parameter Space , 2007, J. Chem. Inf. Model..

[37]  S. Wodak,et al.  Assessment of CAPRI predictions in rounds 3–5 shows progress in docking procedures , 2005, Proteins.

[38]  Marc F Lensink,et al.  Docking and scoring protein interactions: CAPRI 2009 , 2010, Proteins.

[39]  Christopher R. Corbeil,et al.  Modeling Reality for Optimal Docking of Small Molecules to Biological Targets , 2009 .

[40]  S. Wodak,et al.  Hemoglobin interaction in sickle cell fibers. I: Theoretical approaches to the molecular contacts. , 1975, Proceedings of the National Academy of Sciences of the United States of America.

[41]  Toshio Furuya,et al.  Structure-activity relationship of novel DAPK inhibitors identified by structure-based virtual screening. , 2010, Bioorganic & medicinal chemistry.

[42]  Miklos Feher,et al.  Effect of Input Differences on the Results of Docking Calculations , 2009, J. Chem. Inf. Model..

[43]  P Willett,et al.  Development and validation of a genetic algorithm for flexible docking. , 1997, Journal of molecular biology.

[44]  Niu Huang,et al.  Exploiting ordered waters in molecular docking. , 2008, Journal of medicinal chemistry.

[45]  A. Tramontano,et al.  Critical assessment of methods of protein structure prediction (CASP)—round IX , 2011, Proteins.

[46]  S. Wodak,et al.  Docking and scoring protein complexes: CAPRI 3rd Edition , 2007, Proteins.

[47]  Ajay N. Jain.  Surflex-Dock 2.1: Robust performance from ligand energetic modeling, ring flexibility, and knowledge-based search , 2007, J. Comput. Aided Mol. Des..

[48]  Yan Wang,et al.  Computational determination of binding structures and free energies of phosphodiesterase-2 with benzo[1,4]diazepin-2-one derivatives. , 2010, The journal of physical chemistry. B.

[49]  Ajay N. Jain.  Surflex: fully automatic flexible molecular docking using a molecular similarity-based search engine. , 2003, Journal of medicinal chemistry.

[50]  S. Bryant,et al.  Critical assessment of methods of protein structure prediction (CASP): Round II , 1997, Proteins.

[51]  Matthias Rarey,et al.  Modeling of metal interaction geometries for protein–ligand docking , 2007, Proteins.

[52]  I. Kuntz,et al.  Conformational analysis of flexible ligands in macromolecular receptor sites , 1992 .

[53]  David L Mobley,et al.  Predicting small-molecule solvation free energies: an informal blind test for computational chemistry. , 2008, Journal of medicinal chemistry.

[54]  Traian Sulea,et al.  Solvated Interaction Energy (SIE) for Scoring Protein-Ligand Binding Affinities. 2. Benchmark in the CSAR-2010 Scoring Exercise , 2011, J. Chem. Inf. Model..

[55]  Renxiao Wang,et al.  Comparative evaluation of 11 scoring functions for molecular docking. , 2003, Journal of medicinal chemistry.

[56]  T Lengauer,et al.  The particle concept: placing discrete water molecules during protein‐ligand docking predictions , 1999, Proteins.

[57]  Anthony Nicholls,et al.  The SAMPL2 blind prediction challenge: introduction and overview , 2010, J. Comput. Aided Mol. Des..

[58]  Nicolas Moitessier,et al.  Docking Ligands into Flexible and Solvated Macromolecules. 4. Are Popular Scoring Functions Accurate for this Class of Proteins? , 2009, J. Chem. Inf. Model..

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

[60]  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.

[61]  Herbert Edelsbrunner,et al.  Weighted alpha shapes , 1992 .