SABRE: Ligand/Structure-Based Virtual Screening Approach Using Consensus Molecular-Shape Pattern Recognition

We present an efficient and rational ligand/structure shape-based virtual screening approach combining our previous ligand shape-based similarity SABRE (shape-approach-based routines enhanced) and the 3D shape of the receptor binding site. Our approach exploits the pharmacological preferences of a number of known active ligands to take advantage of the structural diversities and chemical similarities, using a linear combination of weighted molecular shape density. Furthermore, the algorithm generates a consensus molecular-shape pattern recognition that is used to filter and place the candidate structure into the binding pocket. The descriptor pool used to construct the consensus molecular-shape pattern consists of four dimensional (4D) fingerprints generated from the distribution of conformer states available to a molecule and the 3D shapes of a set of active ligands computed using SABRE software. The virtual screening efficiency of SABRE was validated using the Database of Useful Decoys (DUD) and the filtered version (WOMBAT) of 10 DUD targets. The ligand/structure shape-based similarity SABRE algorithm outperforms several other widely used virtual screening methods which uses the data fusion of multiscreening tools (2D and 3D fingerprints) and demonstrates a superior early retrieval rate of active compounds (EF(0.1%) = 69.0% and EF(1%) = 98.7%) from a large size of ligand database (∼95,000 structures). Therefore, our developed similarity approach can be of particular use for identifying active compounds that are similar to reference molecules and predicting activity against other targets (chemogenomics). An academic license of the SABRE program is available on request.

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

[2]  Antti Poso,et al.  The Effect of Ligand-Based Tautomer and Protomer Prediction on Structure-Based Virtual Screening , 2009, J. Chem. Inf. Model..

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

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

[5]  Michael Nilges,et al.  Comparative Evaluation of 3D Virtual Ligand Screening Methods: Impact of the Molecular Alignment on Enrichment , 2010, J. Chem. Inf. Model..

[6]  Jianjun Bi,et al.  Knowledge-based virtual screening of HLA-A*0201-restricted CD8+ T-cell epitope peptides from herpes simplex virus genome. , 2011, Journal of theoretical biology.

[7]  Chang-Guo Zhan,et al.  Ligand-Based Virtual Screening Approach Using a New Scoring Function , 2012, J. Chem. Inf. Model..

[8]  Veerabahu Shanmugasundaram,et al.  Molecular similarity measures. , 2011, Methods in molecular biology.

[9]  Fredrik Svensson,et al.  Virtual Screening Data Fusion Using Both Structure- and Ligand-Based Methods , 2012, J. Chem. Inf. Model..

[10]  Yongbo Hu,et al.  Comparison of Several Molecular Docking Programs: Pose Prediction and Virtual Screening Accuracy , 2009, J. Chem. Inf. Model..

[11]  C. Lemmen,et al.  FLEXS: a method for fast flexible ligand superposition. , 1998, Journal of medicinal chemistry.

[12]  Jérôme Hert,et al.  New Methods for Ligand-Based Virtual Screening: Use of Data Fusion and Machine Learning to Enhance the Effectiveness of Similarity Searching , 2006, J. Chem. Inf. Model..

[13]  Lazaros Mavridis,et al.  Comprehensive Comparison of Ligand-Based Virtual Screening Tools Against the DUD Data set Reveals Limitations of Current 3D Methods , 2010, J. Chem. Inf. Model..

[14]  Chang-Guo Zhan,et al.  Probing the regiospecificity of enzyme-catalyzed steroid glycosylation. , 2012, Organic letters.

[15]  D. Diller,et al.  Kinases, homology models, and high throughput docking. , 2003, Journal of medicinal chemistry.

[16]  Amanda C. Schierz.  Virtual screening of bioassay data , 2009, J. Cheminformatics.

[17]  Yanli Wang,et al.  Structure-Based Virtual Screening for Drug Discovery: a Problem-Centric Review , 2012, The AAPS Journal.

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

[19]  Natasja Brooijmans,et al.  Molecular recognition and docking algorithms. , 2003, Annual review of biophysics and biomolecular structure.

[20]  Woody Sherman,et al.  Rapid Shape-Based Ligand Alignment and Virtual Screening Method Based on Atom/Feature-Pair Similarities and Volume Overlap Scoring , 2011, J. Chem. Inf. Model..

[21]  Minghao Zheng,et al.  Enhancing Molecular Shape Comparison by Weighted Gaussian Functions , 2013, J. Chem. Inf. Model..

[22]  Peter Willett,et al.  Similarity-based virtual screening using 2D fingerprints. , 2006, Drug discovery today.

[23]  Jacob D. Durrant,et al.  CrystalDock: A Novel Approach to Fragment-Based Drug Design , 2011, J. Chem. Inf. Model..

[24]  David J. Wild,et al.  Grand challenges for cheminformatics , 2009, J. Cheminformatics.

[25]  J. A. Grant,et al.  A fast method of molecular shape comparison: A simple application of a Gaussian description of molecular shape , 1996, J. Comput. Chem..

[26]  Gisbert Schneider,et al.  Virtual screening and fast automated docking methods. , 2002, Drug discovery today.

[27]  A. Hamza,et al.  Assessing the regioselectivity of OleD-catalyzed glycosylation with a diverse set of acceptors. , 2013, Journal of natural products.

[28]  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..

[29]  Milan Randic,et al.  Nonempirical approach to structure–activity studies† , 1984 .

[30]  Richard M. Jackson,et al.  LigMatch: A Multiple Structure-Based Ligand Matching Method for 3D Virtual Screening , 2009, J. Chem. Inf. Model..

[31]  Xi Chen,et al.  Fluorinated N,N-dialkylaminostilbenes repress colon cancer by targeting methionine S-adenosyltransferase 2A. , 2013, ACS chemical biology.

[32]  Paul G. Mezey,et al.  Topological shape analysis of chain molecules: An application of the GSTE principle , 1993 .

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

[34]  Reino Laatikainen,et al.  FLUFF-BALL, A Template-Based Grid-Independent Superposition and QSAR Technique: Validation Using a Benchmark Steroid Data Set , 2003, J. Chem. Inf. Comput. Sci..

[35]  P. Willett,et al.  Comparison of topological descriptors for similarity-based virtual screening using multiple bioactive reference structures. , 2004, Organic & biomolecular chemistry.

[36]  Andrew C. Good,et al.  High-throughput and Virtual Screening: Core Lead Discovery Technologies Move Towards Integration , 2000 .

[37]  et al.,et al.  In-silico predictive mutagenicity model generation using supervised learning approaches , 2012, Journal of Cheminformatics.

[38]  Rainer E. Martin,et al.  Ligand identification for G-protein-coupled receptors: a lead generation perspective. , 2004, Current opinion in chemical biology.

[39]  J. A. Grant,et al.  Gaussian docking functions. , 2003, Biopolymers.

[40]  J. Pin,et al.  Virtual screening workflow development guided by the "receiver operating characteristic" curve approach. Application to high-throughput docking on metabotropic glutamate receptor subtype 4. , 2005, Journal of medicinal chemistry.

[41]  R. Abagyan,et al.  Flexible ligand docking to multiple receptor conformations: a practical alternative. , 2008, Current opinion in structural biology.

[42]  Tudor I. Oprea,et al.  Optimization of CAMD techniques 3. Virtual screening enrichment studies: a help or hindrance in tool selection? , 2008, J. Comput. Aided Mol. Des..

[43]  Simona Distinto,et al.  How To Optimize Shape-Based Virtual Screening: Choosing the Right Query and Including Chemical Information , 2009, J. Chem. Inf. Model..

[44]  Henrik Boström,et al.  Improving structure-based virtual screening by multivariate analysis of scoring data. , 2003, Journal of medicinal chemistry.

[45]  Nian Wang,et al.  Discovery of novel SecA inhibitors of Candidatus Liberibacter asiaticus by structure based design. , 2011, Bioorganic & medicinal chemistry letters.

[46]  David J. Diller,et al.  Use of Catalyst Pharmacophore Models for Screening of Large Combinatorial Libraries , 2002, J. Chem. Inf. Comput. Sci..

[47]  Qiang Zhang,et al.  Scaffold hopping through virtual screening using 2D and 3D similarity descriptors: ranking, voting, and consensus scoring. , 2006, Journal of medicinal chemistry.

[48]  Dane Vasilescu,et al.  Molecular similarity in aminothiol radioprotectors: A Randić graph approach , 1987 .

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

[50]  D. Rouvray.  Predicting chemistry from topology. , 1986, Scientific American.

[51]  S. Sakkiah,et al.  Discovery of potential pancreatic cholesterol esterase inhibitors using pharmacophore modelling, virtual screening, and optimization studies , 2011, Journal of enzyme inhibition and medicinal chemistry.

[52]  Andreas Bender,et al.  Recognizing Pitfalls in Virtual Screening: A Critical Review , 2012, J. Chem. Inf. Model..

[53]  P. Labute,et al.  Flexible alignment of small molecules. , 2001, Journal of medicinal chemistry.

[54]  Mark S. Johnson,et al.  ShaEP: Molecular Overlay Based on Shape and Electrostatic Potential , 2009, J. Chem. Inf. Model..

[55]  Jeremy G. Vinter,et al.  FieldScreen: Virtual Screening Using Molecular Fields. Application to the DUD Data Set , 2008, J. Chem. Inf. Model..

[56]  M. Randic.  Characterization of molecular branching , 1975 .

[57]  J. A. Grant,et al.  A Gaussian Description of Molecular Shape , 1995 .

[58]  Jiwon Choi,et al.  Optimization of High Throughput Virtual Screening by Combining Shape-Matching and Docking Methods , 2008, J. Chem. Inf. Model..

[59]  Gustavo A. Arteca,et al.  A complete shape characterization for molecular charge densities represented by Gaussian‐type functions , 1991 .