An Integrated Virtual Screening Approach for VEGFR-2 Inhibitors

In recent years, various virtual screening (VS) tools have been developed, and many successful screening campaigns have been showcased. However, whether by conventional molecular docking or pharmacophore screening, the selection of virtual hits is based on the ranking of compounds by scoring functions or fit values, which remains the bottleneck of VS due to insufficient accuracy. As the limitations of individual methods persist, a comprehensive comparison and integration of different methods may provide insights into selecting suitable methods for VS. Here, we evaluated the performance of molecular docking, fingerprint-based 2D similarity and multicomplex pharmacophore in an individual and a combined manner, through a retrospective VS study on VEGFR-2 inhibitors. An integrated two-layer workflow was developed and validated through VS of VEGFR-2 inhibitors against the DUD-E database, which demonstrated improved VS performance through a ligand-based method ECFP_4, followed by molecular docking, and then a strict multicomplex pharmacophore. Through a retrospective comparison with six published papers, this integrated approach outperformed 43 out of 45 methods, indicating a great effectiveness. This kind of integrated VS approach can be extended to other targets for the screening and discovery of inhibitors.

[1]  Charles L. Brooks,et al.  Detailed analysis of grid‐based molecular docking: A case study of CDOCKER—A CHARMm‐based MD docking algorithm , 2003, J. Comput. Chem..

[2]  D. Judd,et al.  Open Innovation in Drug Discovery research comes of age. , 2013, Drug discovery today.

[3]  Andreas Zell,et al.  Optimal assignment methods for ligand-based virtual screening , 2009, J. Cheminformatics.

[4]  Tingjun Hou,et al.  Development and Evaluation of an Integrated Virtual Screening Strategy by Combining Molecular Docking and Pharmacophore Searching Based on Multiple Protein Structures , 2013, J. Chem. Inf. Model..

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

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

[7]  Gregory L. Wilson,et al.  Integrating structure-based and ligand-based approaches for computational drug design. , 2011, Future medicinal chemistry.

[8]  C. Sotriffer,et al.  Docking compared to 3D-pharmacophores: the scoring function challenge , 2010 .

[9]  Rui Li,et al.  Multicomplex‐Based Pharmacophore‐Guided 3D‐QSAR Studies of N‐Substituted 2′‐(Aminoaryl)Benzothiazoles as Aurora‐A Inhibitors , 2012, Chemical biology & drug design.

[10]  Kavitha Bharatham,et al.  Pharmacophore identification and virtual screening for methionyl-tRNA synthetase inhibitors. , 2007, Journal of molecular graphics & modelling.

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

[12]  Gerhard Hessler,et al.  Fast similarity searching and screening hit analysis. , 2004, Drug discovery today. Technologies.

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

[14]  Björn Krüger,et al.  The holistic integration of virtual screening in drug discovery. , 2013, Drug discovery today.

[15]  Michael M. Mysinger,et al.  Directory of Useful Decoys, Enhanced (DUD-E): Better Ligands and Decoys for Better Benchmarking , 2012, Journal of medicinal chemistry.

[16]  Sarah DiGiulio.  Closing the Survivorship Care Gap: Talking to ASCO Survivorship Committee Chair-Elect Charles Shapiro , 2013 .

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

[18]  Sarah DiGiulio.  The 12-Hour Shift and Oncology Nurse Fatigue: The latest evidence about the harms of wearing schedules, and ongoing efforts to help nurses cope , 2013 .

[19]  Tingjun Hou,et al.  Recent development and application of virtual screening in drug discovery: an overview. , 2004, Current pharmaceutical design.

[20]  David W. Ritchie,et al.  Using Consensus-Shape Clustering To Identify Promiscuous Ligands and Protein Targets and To Choose the Right Query for Shape-Based Virtual Screening , 2011, J. Chem. Inf. Model..

[21]  James L. Melville,et al.  Better than Random? The Chemotype Enrichment Problem , 2009, J. Chem. Inf. Model..

[22]  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..

[23]  Jianpeng Ma,et al.  Molecular docking study of the interactions between the thioesterase domain of human fatty acid synthase and its ligands , 2008, Proteins.

[24]  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..

[25]  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.

[26]  Konstantin V Balakin,et al.  VEGF/VEGFR signalling as a target for inhibiting angiogenesis , 2007, Expert opinion on investigational drugs.

[27]  Violeta I. Pérez-Nueno,et al.  Improving VEGFR-2 Docking-Based Screening by Pharmacophore Postfiltering and Similarity Search Postprocessing , 2011, J. Chem. Inf. Model..

[28]  Haichun Liu,et al.  De novo design of N-(pyridin-4-ylmethyl)aniline derivatives as KDR inhibitors: 3D-QSAR, molecular fragment replacement, protein-ligand interaction fingerprint, and ADMET prediction , 2012, Molecular Diversity.

[29]  Guixia Liu,et al.  Performance Evaluation of 2D Fingerprint and 3D Shape Similarity Methods in Virtual Screening , 2012, J. Chem. Inf. Model..

[30]  Istvan J. Enyedy,et al.  Can we use docking and scoring for hit-to-lead optimization? , 2008, J. Comput. Aided Mol. Des..

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

[32]  P E Bourne,et al.  The Protein Data Bank. , 2002, Nucleic acids research.

[33]  Krzysztof Józwiak,et al.  X-ray Crystallographic Structures as a Source of Ligand Alignment in 3D-QSAR , 2013, J. Chem. Inf. Model..

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

[35]  Jeannette Y. Wick,et al.  An overview of small-molecule inhibitors of VEGFR signaling , 2009, Nature Reviews Clinical Oncology.

[36]  Zhiqiang Bai,et al.  Development and strategies of VEGFR-2/KDR inhibitors. , 2012, Future medicinal chemistry.

[37]  A. Konagurthu,et al.  MUSTANG: A multiple structural alignment algorithm , 2006, Proteins.

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

[39]  Jürgen Bajorath,et al.  Molecular similarity analysis in virtual screening: foundations, limitations and novel approaches. , 2007, Drug discovery today.

[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. Friesner,et al.  Evaluation and Reparametrization of the OPLS-AA Force Field for Proteins via Comparison with Accurate Quantum Chemical Calculations on Peptides† , 2001 .

[42]  Ling Wang,et al.  Naphthamides as novel and potent vascular endothelial growth factor receptor tyrosine kinase inhibitors: design, synthesis, and evaluation. , 2008, Journal of medicinal chemistry.

[43]  Xin Huang,et al.  Pyridyl-pyrimidine benzimidazole derivatives as potent, selective, and orally bioavailable inhibitors of Tie-2 kinase. , 2009, Bioorganic & medicinal chemistry letters.

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

[45]  Stephen J Boyer,et al.  Small molecule inhibitors of KDR (VEGFR-2) kinase: an overview of structure activity relationships. , 2002, Current topics in medicinal chemistry.

[46]  M. Radi,et al.  Vascular endothelial growth factor (VEGF) receptors: drugs and new inhibitors. , 2012, Journal of medicinal chemistry.

[47]  Yutaka Maeda,et al.  Novel 4-amino-furo[2,3-d]pyrimidines as Tie-2 and VEGFR2 dual inhibitors. , 2005, Bioorganic & medicinal chemistry letters.

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

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

[50]  Mark A. Murcko,et al.  Virtual screening : an overview , 1998 .

[51]  Malgorzata N. Drwal,et al.  Combination of ligand- and structure-based methods in virtual screening. , 2013, Drug discovery today. Technologies.

[52]  Jürgen Bajorath,et al.  Integration of virtual and high-throughput screening , 2002, Nature Reviews Drug Discovery.

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

[54]  Dennis M. Krüger,et al.  Comparison of Structure‐ and Ligand‐Based Virtual Screening Protocols Considering Hit List Complementarity and Enrichment Factors , 2010, ChemMedChem.

[55]  Sheng-Yong Yang,et al.  Pharmacophore modeling and applications in drug discovery: challenges and recent advances. , 2010, Drug discovery today.

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