A novel interaction fingerprint derived from per atom score contributions: exhaustive evaluation of interaction fingerprint performance in docking based virtual screening

Protein ligand interaction fingerprints are a powerful approach for the analysis and assessment of docking poses to improve docking performance in virtual screening. In this study, a novel interaction fingerprint approach (PADIF, protein per atom score contributions derived interaction fingerprint) is presented which was specifically designed for utilising the GOLD scoring functions’ atom contributions together with a specific scoring scheme. This allows the incorporation of known protein–ligand complex structures for a target-specific scoring. Unlike many other methods, this approach uses weighting factors reflecting the relative frequency of a specific interaction in the references and penalizes destabilizing interactions. In addition, and for the first time, an exhaustive validation study was performed that assesses the performance of PADIF and two other interaction fingerprints in virtual screening. Here, PADIF shows superior results, and some rules of thumb for a successful use of interaction fingerprints could be identified.

[1]  Ruben Abagyan,et al.  Nuclear hormone receptor targeted virtual screening. , 2003, Journal of medicinal chemistry.

[2]  Didier Rognan,et al.  Encoding Protein-Ligand Interaction Patterns in Fingerprints and Graphs , 2013, J. Chem. Inf. Model..

[3]  L. Dardenne,et al.  Receptor–ligand molecular docking , 2013, Biophysical Reviews.

[4]  Karsten Klein,et al.  Scaffold Hunter: a comprehensive visual analytics framework for drug discovery , 2017, Journal of Cheminformatics.

[5]  Obdulia Rabal,et al.  APIF: A New Interaction Fingerprint Based on Atom Pairs and Its Application to Virtual Screening , 2009, J. Chem. Inf. Model..

[6]  Didier Rognan,et al.  Molecular Interaction Fingerprints , 2013 .

[7]  Thomas Stützle,et al.  Empirical Scoring Functions for Advanced Protein-Ligand Docking with PLANTS , 2009, J. Chem. Inf. Model..

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

[9]  Alessandra Nurisso,et al.  Molecular Docking Using the Molecular Lipophilicity Potential as Hydrophobic Descriptor: Impact on GOLD Docking Performance , 2012, J. Chem. Inf. Model..

[10]  Evangelia D Chrysina,et al.  Crystallographic studies on two bioisosteric analogues, N-acetyl-beta-D-glucopyranosylamine and N-trifluoroacetyl-beta-D-glucopyranosylamine, potent inhibitors of muscle glycogen phosphorylase. , 2005, Bioorganic & medicinal chemistry.

[11]  E. Lionta,et al.  Structure-Based Virtual Screening for Drug Discovery: Principles, Applications and Recent Advances , 2014, Current topics in medicinal chemistry.

[12]  Conrad C. Huang,et al.  UCSF Chimera—A visualization system for exploratory research and analysis , 2004, J. Comput. Chem..

[13]  Sangtae Kim,et al.  Position Specific Interaction Dependent Scoring Technique for Virtual Screening Based on Weighted Protein-Ligand Interaction Fingerprint Profiles , 2009, J. Chem. Inf. Model..

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

[15]  Xiaoqin Zou,et al.  Scoring functions and their evaluation methods for protein-ligand docking: recent advances and future directions. , 2010, Physical chemistry chemical physics : PCCP.

[16]  Thorsten Meinl,et al.  KNIME: The Konstanz Information Miner , 2007, GfKl.

[17]  Gerhard Klebe,et al.  Fconv: Format Conversion, Manipulation and Feature Computation of Molecular Data , 2011, Bioinform..

[18]  David G. Lloyd,et al.  Unbiasing Scoring Functions: A New Normalization and Rescoring Strategy , 2007, J. Chem. Inf. Model..

[19]  Zhihai Liu,et al.  Comparative Assessment of Scoring Functions on an Updated Benchmark: 2. Evaluation Methods and General Results , 2014, J. Chem. Inf. Model..

[20]  René Thomsen,et al.  MolDock: a new technique for high-accuracy molecular docking. , 2006, Journal of medicinal chemistry.

[21]  Yong Zhou,et al.  Computational methods using weighed-extreme learning machine to predict protein self-interactions with protein evolutionary information , 2017, Journal of Cheminformatics.

[22]  Peter Willett,et al.  Knowledge-Based Interaction Fingerprint Scoring: A Simple Method for Improving the Effectiveness of Fast Scoring Functions , 2006, J. Chem. Inf. Model..

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

[24]  David Rogers,et al.  Extended-Connectivity Fingerprints , 2010, J. Chem. Inf. Model..

[25]  Schmid,et al.  "Scaffold-Hopping" by Topological Pharmacophore Search: A Contribution to Virtual Screening. , 1999, Angewandte Chemie.

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

[27]  S. Hubbard,et al.  Structures of the tyrosine kinase domain of fibroblast growth factor receptor in complex with inhibitors. , 1997, Science.

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

[29]  Ricardo L. Mancera,et al.  Expanded Interaction Fingerprint Method for Analyzing Ligand Binding Modes in Docking and Structure-Based Drug Design , 2004, J. Chem. Inf. Model..

[30]  J. Bajorath,et al.  Recent Advances in Scaffold Hopping. , 2017, Journal of medicinal chemistry.

[31]  Chris G. Kruse,et al.  Assessment of scaffold hopping efficiency by use of molecular interaction fingerprints. , 2008, Journal of medicinal chemistry.

[32]  Dmitri B. Kireev,et al.  Structural Protein–Ligand Interaction Fingerprints (SPLIF) for Structure-Based Virtual Screening: Method and Benchmark Study , 2014, J. Chem. Inf. Model..

[33]  Gilles Marcou,et al.  Optimizing Fragment and Scaffold Docking by Use of Molecular Interaction Fingerprints , 2007, J. Chem. Inf. Model..

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

[35]  D. Moras,et al.  A 'specificity' pocket inferred from the crystal structures of the complexes of aldose reductase with the pharmaceutically important inhibitors tolrestat and sorbinil. , 1997, Structure.

[36]  Zhan Deng,et al.  Interaction profiles of protein kinase-inhibitor complexes and their application to virtual screening. , 2005, Journal of medicinal chemistry.

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

[38]  Z. Deng,et al.  Structural interaction fingerprint (SIFt): a novel method for analyzing three-dimensional protein-ligand binding interactions. , 2004, Journal of medicinal chemistry.

[39]  B. Turk.  Targeting proteases: successes, failures and future prospects , 2006, Nature Reviews Drug Discovery.