Literature DB >> 20853887

Comprehensive structural and functional characterization of the human kinome by protein structure modeling and ligand virtual screening.

Michal Brylinski1, Jeffrey Skolnick.   

Abstract

The growing interest in the identification of kinase inhibitors, promising therapeutics in the treatment of many diseases, has created a demand for the structural characterization of the entire human kinome. At the outset of the drug development process, the lead-finding stage, approaches that enrich the screening library with bioactive compounds are needed. Here, protein structure based methods can play an important role, but despite structural genomics efforts, it is unlikely that the three-dimensional structures of the entire kinome will be available soon. Therefore, at the proteome level, structure-based approaches must rely on predicted models, with a key issue being their utility in virtual ligand screening. In this study, we employ the recently developed FINDSITE/Q-Dock ligand homology modeling approach, which is well-suited for proteome-scale applications using predicted structures, to provide extensive structural and functional characterization of the human kinome. Specifically, we construct structure models for the human kinome; these are subsequently subject to virtual screening against a library of more than 2 million compounds. To rank the compounds, we employ a hierarchical approach that combines ligand- and structure-based filters. Modeling accuracy is carefully validated using available experimental data with particularly encouraging results found for the ability to identify, without prior knowledge, specific kinase inhibitors. More generally, the modeling procedure results in a large number of predicted molecular interactions between kinases and small ligands that should be of practical use in the development of novel inhibitors. The data set is freely available to the academic community via a user-friendly Web interface at http://cssb.biology.gatech.edu/kinomelhm/ as well as at the ZINC Web site ( http://zinc.docking.org/applications/2010Apr/Brylinski-2010.tar.gz ).

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Year:  2010        PMID: 20853887      PMCID: PMC2963673          DOI: 10.1021/ci100235n

Source DB:  PubMed          Journal:  J Chem Inf Model        ISSN: 1549-9596            Impact factor:   4.956


  110 in total

1.  The Protein Data Bank.

Authors:  H M Berman; J Westbrook; Z Feng; G Gilliland; T N Bhat; H Weissig; I N Shindyalov; P E Bourne
Journal:  Nucleic Acids Res       Date:  2000-01-01       Impact factor: 16.971

2.  LIGAND: database of chemical compounds and reactions in biological pathways.

Authors:  Susumu Goto; Yasushi Okuno; Masahiro Hattori; Takaaki Nishioka; Minoru Kanehisa
Journal:  Nucleic Acids Res       Date:  2002-01-01       Impact factor: 16.971

3.  Discovery of 5-[5-fluoro-2-oxo-1,2- dihydroindol-(3Z)-ylidenemethyl]-2,4- dimethyl-1H-pyrrole-3-carboxylic acid (2-diethylaminoethyl)amide, a novel tyrosine kinase inhibitor targeting vascular endothelial and platelet-derived growth factor receptor tyrosine kinase.

Authors:  Li Sun; Chris Liang; Sheri Shirazian; Yong Zhou; Todd Miller; Jean Cui; Juri Y Fukuda; Ji-Yu Chu; Asaad Nematalla; Xueyan Wang; Hui Chen; Anand Sistla; Tony C Luu; Flora Tang; James Wei; Cho Tang
Journal:  J Med Chem       Date:  2003-03-27       Impact factor: 7.446

4.  Assessing scoring functions for protein-ligand interactions.

Authors:  Philippe Ferrara; Holger Gohlke; Daniel J Price; Gerhard Klebe; Charles L Brooks
Journal:  J Med Chem       Date:  2004-06-03       Impact factor: 7.446

5.  A threading-based method (FINDSITE) for ligand-binding site prediction and functional annotation.

Authors:  Michal Brylinski; Jeffrey Skolnick
Journal:  Proc Natl Acad Sci U S A       Date:  2007-12-28       Impact factor: 11.205

6.  Development and experimental validation of a docking strategy for the generation of kinase-targeted libraries.

Authors:  Rafael Gozalbes; Laurence Simon; Nicolas Froloff; Eric Sartori; Claude Monteils; Romuald Baudelle
Journal:  J Med Chem       Date:  2008-05-15       Impact factor: 7.446

7.  Computation of 3D queries for ROCS based virtual screens.

Authors:  Gregory J Tawa; J Christian Baber; Christine Humblet
Journal:  J Comput Aided Mol Des       Date:  2009-09-26       Impact factor: 3.686

8.  Virtual screening for inhibitors of human aldose reductase.

Authors:  Oliver Kraemer; Isabelle Hazemann; Alberto D Podjarny; Gerhard Klebe
Journal:  Proteins       Date:  2004-06-01

9.  BAY 43-9006 exhibits broad spectrum oral antitumor activity and targets the RAF/MEK/ERK pathway and receptor tyrosine kinases involved in tumor progression and angiogenesis.

Authors:  Scott M Wilhelm; Christopher Carter; Liya Tang; Dean Wilkie; Angela McNabola; Hong Rong; Charles Chen; Xiaomei Zhang; Patrick Vincent; Mark McHugh; Yichen Cao; Jaleel Shujath; Susan Gawlak; Deepa Eveleigh; Bruce Rowley; Li Liu; Lila Adnane; Mark Lynch; Daniel Auclair; Ian Taylor; Rich Gedrich; Andrei Voznesensky; Bernd Riedl; Leonard E Post; Gideon Bollag; Pamela A Trail
Journal:  Cancer Res       Date:  2004-10-01       Impact factor: 13.312

Review 10.  Docking, virtual high throughput screening and in silico fragment-based drug design.

Authors:  Vincent Zoete; Aurélien Grosdidier; Olivier Michielin
Journal:  J Cell Mol Med       Date:  2009-01-21       Impact factor: 5.310

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  13 in total

Review 1.  Are predicted protein structures of any value for binding site prediction and virtual ligand screening?

Authors:  Jeffrey Skolnick; Hongyi Zhou; Mu Gao
Journal:  Curr Opin Struct Biol       Date:  2013-02-14       Impact factor: 6.809

Review 2.  Kinase Atlas: Druggability Analysis of Potential Allosteric Sites in Kinases.

Authors:  Christine Yueh; Justin Rettenmaier; Bing Xia; David R Hall; Andrey Alekseenko; Kathryn A Porter; Krister Barkovich; Gyorgy Keseru; Adrian Whitty; James A Wells; Sandor Vajda; Dima Kozakov
Journal:  J Med Chem       Date:  2019-07-05       Impact factor: 7.446

3.  Evaluation of model quality predictions in CASP9.

Authors:  Andriy Kryshtafovych; Krzysztof Fidelis; Anna Tramontano
Journal:  Proteins       Date:  2011-10-14

4.  Cross-reactivity virtual profiling of the human kinome by X-react(KIN): a chemical systems biology approach.

Authors:  Michal Brylinski; Jeffrey Skolnick
Journal:  Mol Pharm       Date:  2010-11-08       Impact factor: 4.939

Review 5.  Modelling three-dimensional protein structures for applications in drug design.

Authors:  Tobias Schmidt; Andreas Bergner; Torsten Schwede
Journal:  Drug Discov Today       Date:  2013-11-08       Impact factor: 7.851

6.  FINDSITEcomb2.0: A New Approach for Virtual Ligand Screening of Proteins and Virtual Target Screening of Biomolecules.

Authors:  Hongyi Zhou; Hongnan Cao; Jeffrey Skolnick
Journal:  J Chem Inf Model       Date:  2018-10-16       Impact factor: 4.956

7.  Kinome-wide activity modeling from diverse public high-quality data sets.

Authors:  Stephan C Schürer; Steven M Muskal
Journal:  J Chem Inf Model       Date:  2013-01-09       Impact factor: 4.956

8.  FINDSITE(comb): a threading/structure-based, proteomic-scale virtual ligand screening approach.

Authors:  Hongyi Zhou; Jeffrey Skolnick
Journal:  J Chem Inf Model       Date:  2012-12-28       Impact factor: 4.956

9.  FINDSITE(X): a structure-based, small molecule virtual screening approach with application to all identified human GPCRs.

Authors:  Hongyi Zhou; Jeffrey Skolnick
Journal:  Mol Pharm       Date:  2012-05-21       Impact factor: 4.939

10.  Prediction of ligand binding using an approach designed to accommodate diversity in protein-ligand interactions.

Authors:  Lorraine Marsh
Journal:  PLoS One       Date:  2011-08-10       Impact factor: 3.240

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