Comparative Analysis of Pharmacophore Screening Tools

The pharmacophore concept is of central importance in computer-aided drug design (CADD) mainly because of its successful application in medicinal chemistry and, in particular, high-throughput virtual screening (HTVS). The simplicity of the pharmacophore definition enables the complexity of molecular interactions between ligand and receptor to be reduced to a handful set of features. With many pharmacophore screening softwares available, it is of the utmost interest to explore the behavior of these tools when applied to different biological systems. In this work, we present a comparative analysis of eight pharmacophore screening algorithms (Catalyst, Unity, LigandScout, Phase, Pharao, MOE, Pharmer, and POT) for their use in typical HTVS campaigns against four different biological targets by using default settings. The results herein presented show how the performance of each pharmacophore screening tool might be specifically related to factors such as the characteristics of the binding pocket, the use of specific pharmacophore features, and the use of these techniques in specific steps/contexts of the drug discovery pipeline. Algorithms with rmsd-based scoring functions are able to predict more compound poses correctly as overlay-based scoring functions. However, the ratio of correctly predicted compound poses versus incorrectly predicted poses is better for overlay-based scoring functions that also ensure better performances in compound library enrichments. While the ensemble of these observations can be used to choose the most appropriate class of algorithm for specific virtual screening projects, we remarked that pharmacophore algorithms are often equally good, and in this respect, we also analyzed how pharmacophore algorithms can be combined together in order to increase the success of hit compound identification. This study provides a valuable benchmark set for further developments in the field of pharmacophore search algorithms, e.g., by using pose predictions and compound library enrichment criteria.

[1]  Liu Shui,et al.  Urokinase Inhibitor Design Based on Pharmacophore Model Derived from Diverse Classes of Inhibitors , 2006 .

[2]  G. Schneider,et al.  Fuzzy pharmacophore models from molecular alignments for correlation-vector-based virtual screening. , 2004, Journal of medicinal chemistry.

[3]  William L. Jorgensen,et al.  Journal of Chemical Information and Modeling , 2005, J. Chem. Inf. Model..

[4]  Gert Thijs,et al.  Pharao: pharmacophore alignment and optimization. , 2008, Journal of molecular graphics & modelling.

[5]  Alberto Del Rio,et al.  Use of large multiconformational databases with structure-based pharmacophore models for fast screening of commercial compound collections , 2011, J. Cheminformatics.

[6]  Cathy H. Wu,et al.  UniProt: the Universal Protein knowledgebase , 2004, Nucleic Acids Res..

[7]  Alexandre Varnek,et al.  Chemoinformatics approaches to virtual screening , 2008 .

[8]  Marc C. Nicklaus,et al.  Combining docking with pharmacophore filtering for improved virtual screening , 2009, J. Cheminformatics.

[9]  Richard A. Lewis,et al.  Three-dimensional pharmacophore methods in drug discovery. , 2010, Journal of medicinal chemistry.

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

[11]  Pedro J Ballester,et al.  Prospective virtual screening with Ultrafast Shape Recognition: the identification of novel inhibitors of arylamine N-acetyltransferases , 2010, Journal of The Royal Society Interface.

[12]  Ting Ran,et al.  Structure-based and shape-complemented pharmacophore modeling for the discovery of novel checkpoint kinase 1 inhibitors , 2010, Journal of molecular modeling.

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

[14]  Wendy A. Warr,et al.  ChEMBL. An interview with John Overington, team leader, chemogenomics at the European Bioinformatics Institute Outstation of the European Molecular Biology Laboratory (EMBL-EBI) , 2009, J. Comput. Aided Mol. Des..

[15]  Eric J. Martin,et al.  Conformational Sampling of Bioactive Molecules: A Comparative Study , 2007, J. Chem. Inf. Model..

[16]  Woody Sherman,et al.  ConfGen: A Conformational Search Method for Efficient Generation of Bioactive Conformers , 2010, J. Chem. Inf. Model..

[17]  Patricia Rodriguez-Tomé,et al.  MMsINC: a large-scale chemoinformatics database , 2008, Nucleic Acids Res..

[18]  Wei-liang Zhu,et al.  Pharmacophore-based virtual screening versus docking-based virtual screening: a benchmark comparison against eight targets , 2009, Acta Pharmacologica Sinica.

[19]  Yu-Quan Wei,et al.  Towards more accurate pharmacophore modeling: Multicomplex-based comprehensive pharmacophore map and most-frequent-feature pharmacophore model of CDK2. , 2008, Journal of molecular graphics & modelling.

[20]  Thierry Langer,et al.  Molecule-pharmacophore superpositioning and pattern matching in computational drug design. , 2008, Drug discovery today.

[21]  T. Langer Pharmacophores in Drug Research , 2010, Molecular informatics.

[22]  Giuseppe Felice Mangiatordi,et al.  CoCoCo: a free suite of multiconformational chemical databases for high-throughput virtual screening purposes. , 2010, Molecular bioSystems.

[23]  Scott P. Brown,et al.  Large-scale application of high-throughput molecular mechanics with Poisson-Boltzmann surface area for routine physics-based scoring of protein-ligand complexes. , 2009, Journal of medicinal chemistry.

[24]  Olivier Sperandio,et al.  Free resources to assist structure-based virtual ligand screening experiments. , 2007, Current protein & peptide science.

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

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

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

[28]  J. Irwin,et al.  ZINC ? A Free Database of Commercially Available Compounds for Virtual Screening. , 2005 .

[29]  Andrew R. Leach,et al.  A comparison of the pharmacophore identification programs: Catalyst, DISCO and GASP , 2002, J. Comput. Aided Mol. Des..

[30]  Alberto Del Rio,et al.  Freely accessible databases of commercial compounds for high- throughput virtual screenings. , 2012, Current topics in medicinal chemistry.

[31]  M. Duffy,et al.  The urokinase‐type plasminogen activator system in cancer metastasis: A review , 1997, International journal of cancer.

[32]  C. John Blankley,et al.  Comparison of 2D Fingerprint Types and Hierarchy Level Selection Methods for Structural Grouping Using Ward's Clustering , 2000, J. Chem. Inf. Comput. Sci..

[33]  Klaus R. Liedl,et al.  One Concept, Three Implementations of 3D Pharmacophore-Based Virtual Screening: Distinct Coverage of Chemical Search Space , 2010, J. Chem. Inf. Model..

[34]  Sheng Zhang,et al.  PTP1B as a drug target: recent developments in PTP1B inhibitor discovery. , 2007, Drug discovery today.

[35]  Gisbert Schneider,et al.  Virtual screening: an endless staircase? , 2010, Nature Reviews Drug Discovery.

[36]  Matthias Rarey,et al.  Conformational Sampling for Large-Scale Virtual Screening: Accuracy versus Ensemble Size , 2009, J. Chem. Inf. Model..

[37]  Dimitris K. Agrafiotis,et al.  Stochastic proximity embedding , 2003, J. Comput. Chem..

[38]  Thierry Langer,et al.  LigandScout: 3-D Pharmacophores Derived from Protein-Bound Ligands and Their Use as Virtual Screening Filters , 2005, J. Chem. Inf. Model..

[39]  B. Kennedy,et al.  Increased insulin sensitivity and obesity resistance in mice lacking the protein tyrosine phosphatase-1B gene. , 1999, Science.

[40]  Giulio Rastelli,et al.  Structure-based design of potent aromatase inhibitors by high-throughput docking. , 2011, Journal of medicinal chemistry.

[41]  Chris de Graaf,et al.  Snooker: A Structure-Based Pharmacophore Generation Tool Applied to Class A GPCRs , 2011, J. Chem. Inf. Model..

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

[43]  D O Morgan,et al.  Cyclin-dependent kinases: engines, clocks, and microprocessors. , 1997, Annual review of cell and developmental biology.

[44]  A. Tafi,et al.  Pharmacophore modelling: a forty year old approach and its modern synergies. , 2011, Current medicinal chemistry.

[45]  Paul M. Selzer,et al.  The Impact of Tautomer Forms on Pharmacophore-Based Virtual Screening , 2006, J. Chem. Inf. Model..

[46]  Thomas E. Exner,et al.  Influence of Protonation, Tautomeric, and Stereoisomeric States on Protein-Ligand Docking Results , 2009, J. Chem. Inf. Model..

[47]  Huafeng Xu,et al.  A self-organizing principle for learning nonlinear manifolds , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[48]  David Ryan Koes,et al.  Pharmer: Efficient and Exact Pharmacophore Search , 2011, J. Chem. Inf. Model..

[49]  Hongma Sun,et al.  Pharmacophore-based virtual screening. , 2008, Current medicinal chemistry.

[50]  Simona Distinto,et al.  Evaluation of the performance of 3D virtual screening protocols: RMSD comparisons, enrichment assessments, and decoy selection—What can we learn from earlier mistakes? , 2008, J. Comput. Aided Mol. Des..