Analysis and optimization of structure-based virtual screening protocols. 2. Examination of docked ligand orientation sampling methodology: mapping a pharmacophore for success.

An important element of any structure-based virtual screening (SVS) technique is the method used to orient the ligands in the target active site. This has been a somewhat overlooked issue in recent SVS validation studies, with the assumption being made that the performance of an algorithm for a given set of orientation sampling settings will be representative for the general behavior of said technique. Here, we analyze five different SVS targets using a variety of sampling paradigms within the DOCK, GOLD and PROMETHEUS programs over a data set of approximately 10,000 noise compounds, combined with data sets containing multiple active compounds. These sets have been broken down by chemotype, with chemotype hit rate used to provide a measure of enrichment with a potentially improved relevance to real world SVS experiments. The variability in enrichment results produced by different sampling paradigms is illustrated, as is the utility of using pharmacophores to constrain sampling to regions that reflect known structural biology. The difference in results when comparing chemotype with compound hit rates is also highlighted.

[1]  A. Good,et al.  Analysis and optimization of structure-based virtual screening protocols (1): exploration of ligand conformational sampling techniques. , 2003, Journal of molecular graphics & modelling.

[2]  Robin Taylor,et al.  A new test set for validating predictions of protein–ligand interaction , 2002, Proteins.

[3]  Daniel A. Gschwend,et al.  Molecular docking towards drug discovery , 1996, Journal of molecular recognition : JMR.

[4]  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.

[5]  E Keith Davies,et al.  The potential of Internet computing for drug discovery. , 2002, Drug discovery today.

[6]  J. Gasteiger,et al.  ITERATIVE PARTIAL EQUALIZATION OF ORBITAL ELECTRONEGATIVITY – A RAPID ACCESS TO ATOMIC CHARGES , 1980 .

[7]  I. Kuntz,et al.  Matching chemistry and shape in molecular docking. , 1993, Protein engineering.

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

[9]  D. Kostrewa,et al.  Novel inhibitors of DNA gyrase: 3D structure based biased needle screening, hit validation by biophysical methods, and 3D guided optimization. A promising alternative to random screening. , 2000, Journal of medicinal chemistry.

[10]  R. DesJarlais,et al.  Inhibition of human immunodeficiency virus-1 protease by a C2-symmetric phosphinate. Synthesis and crystallographic analysis. , 1993, Biochemistry.

[11]  Andrew C. Good,et al.  Pharmacophore Pattern Application in Virtual Screening. Library Design and QSAR , 2000 .

[12]  G Schreiber,et al.  Evaluation of direct and cooperative contributions towards the strength of buried hydrogen bonds and salt bridges. , 2000, Journal of molecular biology.

[13]  C L Brooks,et al.  Ligand-protein database: linking protein-ligand complex structures to binding data. , 2001, Journal of medicinal chemistry.

[14]  Ingo Muegge,et al.  Evaluation of docking/scoring approaches: A comparative study based on MMP3 inhibitors , 2000, J. Comput. Aided Mol. Des..

[15]  Christian Laurence,et al.  Observations on the strength of hydrogen bonding , 2000 .

[16]  C. Sander,et al.  Errors in protein structures , 1996, Nature.

[17]  U. Singh,et al.  A NEW FORCE FIELD FOR MOLECULAR MECHANICAL SIMULATION OF NUCLEIC ACIDS AND PROTEINS , 1984 .

[18]  Janet M. Thornton,et al.  BLEEP—potential of mean force describing protein–ligand interactions: II. Calculation of binding energies and comparison with experimental data , 1999 .

[19]  Hans-Joachim Böhm,et al.  The development of a simple empirical scoring function to estimate the binding constant for a protein-ligand complex of known three-dimensional structure , 1994, J. Comput. Aided Mol. Des..

[20]  M. L. Connolly Solvent-accessible surfaces of proteins and nucleic acids. , 1983, Science.

[21]  P J Goodford,et al.  Physicochemical-activity relationship in practice. 2. Rational selection of benzenoid substituents. , 1975, Journal of medicinal chemistry.

[22]  Y. Martin,et al.  A general and fast scoring function for protein-ligand interactions: a simplified potential approach. , 1999, Journal of medicinal chemistry.

[23]  Gerhard Klebe,et al.  Predicting binding modes, binding affinities and ‘hot spots’ for protein-ligand complexes using a knowledge-based scoring function , 2000 .

[24]  Tim D. J. Perkins,et al.  New Approach to Molecular Docking and Its Application to Virtual Screening of Chemical Databases , 2000, J. Chem. Inf. Comput. Sci..

[25]  Tudor I. Oprea,et al.  The Design of Leadlike Combinatorial Libraries. , 1999, Angewandte Chemie.

[26]  Janet M. Thornton,et al.  BLEEP—potential of mean force describing protein–ligand interactions: I. Generating potential , 1999 .

[27]  D. Rognan,et al.  Protein-based virtual screening of chemical databases. 1. Evaluation of different docking/scoring combinations. , 2000, Journal of medicinal chemistry.