Literature DB >> 18083776

Quantitative systems-level determinants of human genes targeted by successful drugs.

Lixia Yao1, Andrey Rzhetsky.   

Abstract

What makes a successful drug target? A target molecule with an appropriate (druggable) tertiary structure is a necessary but not the sufficient condition for success. Here we analyzed specific properties of human genes and proteins targeted by 919 FDA-approved drugs and identified several quantitative measures that distinguish them from other genes and proteins at a highly significant level. Compared to an average gene and its encoded protein(s), successful drug targets are more highly connected (but far from being the most highly connected), have higher betweenness values, lower entropies of tissue expression, and lower ratios of nonsynonymous to synonymous single-nucleotide polymorphisms. Furthermore, we have identified human tissues that are significantly over- or undertargeted relative to the full spectrum of genes that are active in each tissue. Our study provides quantitative guidelines that could aid in the computational screening of new drug targets in human cells.

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Year:  2007        PMID: 18083776      PMCID: PMC2203618          DOI: 10.1101/gr.6888208

Source DB:  PubMed          Journal:  Genome Res        ISSN: 1088-9051            Impact factor:   9.043


  26 in total

1.  dbSNP: the NCBI database of genetic variation.

Authors:  S T Sherry; M H Ward; M Kholodov; J Baker; L Phan; E M Smigielski; K Sirotkin
Journal:  Nucleic Acids Res       Date:  2001-01-01       Impact factor: 16.971

2.  Growing scale-free networks with tunable clustering.

Authors:  Petter Holme; Beom Jun Kim
Journal:  Phys Rev E Stat Nonlin Soft Matter Phys       Date:  2002-01-11

Review 3.  Stategic trends in the drug industry.

Authors:  Jürgen Drews
Journal:  Drug Discov Today       Date:  2003-05-01       Impact factor: 7.851

4.  GeneWays: a system for extracting, analyzing, visualizing, and integrating molecular pathway data.

Authors:  Andrey Rzhetsky; Ivan Iossifov; Tomohiro Koike; Michael Krauthammer; Pauline Kra; Mitzi Morris; Hong Yu; Pablo Ariel Duboué; Wubin Weng; W John Wilbur; Vasileios Hatzivassiloglou; Carol Friedman
Journal:  J Biomed Inform       Date:  2004-02       Impact factor: 6.317

Review 5.  The target discovery process.

Authors:  Ursula Egner; Jörn Krätzschmar; Bertolt Kreft; Hans-Dieter Pohlenz; Martin Schneider
Journal:  Chembiochem       Date:  2005-03       Impact factor: 3.164

6.  Towards a proteome-scale map of the human protein-protein interaction network.

Authors:  Jean-François Rual; Kavitha Venkatesan; Tong Hao; Tomoko Hirozane-Kishikawa; Amélie Dricot; Ning Li; Gabriel F Berriz; Francis D Gibbons; Matija Dreze; Nono Ayivi-Guedehoussou; Niels Klitgord; Christophe Simon; Mike Boxem; Stuart Milstein; Jennifer Rosenberg; Debra S Goldberg; Lan V Zhang; Sharyl L Wong; Giovanni Franklin; Siming Li; Joanna S Albala; Janghoo Lim; Carlene Fraughton; Estelle Llamosas; Sebiha Cevik; Camille Bex; Philippe Lamesch; Robert S Sikorski; Jean Vandenhaute; Huda Y Zoghbi; Alex Smolyar; Stephanie Bosak; Reynaldo Sequerra; Lynn Doucette-Stamm; Michael E Cusick; David E Hill; Frederick P Roth; Marc Vidal
Journal:  Nature       Date:  2005-09-28       Impact factor: 49.962

Review 7.  Case histories, magic bullets and the state of drug discovery.

Authors:  Jürgen Drews
Journal:  Nat Rev Drug Discov       Date:  2006-04-13       Impact factor: 84.694

8.  The meaning and use of the area under a receiver operating characteristic (ROC) curve.

Authors:  J A Hanley; B J McNeil
Journal:  Radiology       Date:  1982-04       Impact factor: 11.105

9.  Evolutionary rate in the protein interaction network.

Authors:  Hunter B Fraser; Aaron E Hirsh; Lars M Steinmetz; Curt Scharfe; Marcus W Feldman
Journal:  Science       Date:  2002-04-26       Impact factor: 47.728

10.  DrugBank: a comprehensive resource for in silico drug discovery and exploration.

Authors:  David S Wishart; Craig Knox; An Chi Guo; Savita Shrivastava; Murtaza Hassanali; Paul Stothard; Zhan Chang; Jennifer Woolsey
Journal:  Nucleic Acids Res       Date:  2006-01-01       Impact factor: 16.971

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  26 in total

Review 1.  Novel opportunities for computational biology and sociology in drug discovery.

Authors:  Lixia Yao; James A Evans; Andrey Rzhetsky
Journal:  Trends Biotechnol       Date:  2010-04       Impact factor: 19.536

Review 2.  Approaches to target tractability assessment - a practical perspective.

Authors:  Kristin K Brown; Michael M Hann; Ami S Lakdawala; Rita Santos; Pamela J Thomas; Kieran Todd
Journal:  Medchemcomm       Date:  2018-02-14       Impact factor: 3.597

3.  Protein annotation from protein interaction networks and Gene Ontology.

Authors:  Cao D Nguyen; Katheleen J Gardiner; Krzysztof J Cios
Journal:  J Biomed Inform       Date:  2011-05-06       Impact factor: 6.317

4.  Drug target-gene signatures that predict teratogenicity are enriched for developmentally related genes.

Authors:  Asher D Schachter; Isaac S Kohane
Journal:  Reprod Toxicol       Date:  2010-11-27       Impact factor: 3.143

5.  Semi-supervised drug-protein interaction prediction from heterogeneous biological spaces.

Authors:  Zheng Xia; Ling-Yun Wu; Xiaobo Zhou; Stephen T C Wong
Journal:  BMC Syst Biol       Date:  2010-09-13

6.  Reconstruction and flux-balance analysis of the Plasmodium falciparum metabolic network.

Authors:  Germán Plata; Tzu-Lin Hsiao; Kellen L Olszewski; Manuel Llinás; Dennis Vitkup
Journal:  Mol Syst Biol       Date:  2010-09-07       Impact factor: 11.429

7.  Effects of protein interaction data integration, representation and reliability on the use of network properties for drug target prediction.

Authors:  Antonio Mora; Ian M Donaldson
Journal:  BMC Bioinformatics       Date:  2012-11-12       Impact factor: 3.169

8.  Structure of protein interaction networks and their implications on drug design.

Authors:  Takeshi Hase; Hiroshi Tanaka; Yasuhiro Suzuki; So Nakagawa; Hiroaki Kitano
Journal:  PLoS Comput Biol       Date:  2009-10-30       Impact factor: 4.475

9.  Assessing the druggability of protein-protein interactions by a supervised machine-learning method.

Authors:  Nobuyoshi Sugaya; Kazuyoshi Ikeda
Journal:  BMC Bioinformatics       Date:  2009-08-25       Impact factor: 3.169

10.  Characterizing the network of drugs and their affected metabolic subpathways.

Authors:  Chunquan Li; Desi Shang; Yan Wang; Jing Li; Junwei Han; Shuyuan Wang; Qianlan Yao; Yingying Wang; Yunpeng Zhang; Chunlong Zhang; Yanjun Xu; Wei Jiang; Xia Li
Journal:  PLoS One       Date:  2012-10-24       Impact factor: 3.240

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