Properties and identification of human protein drug targets

被引:210
作者
Bakheet, Tala M. [2 ]
Doig, Andrew J. [1 ]
机构
[1] Univ Manchester, Manchester Interdisciplinary Bioctr, Manchester M1 7DN, Lancs, England
[2] Univ Manchester, Fac Life Sci, Manchester M13 9PT, Lancs, England
关键词
GENE ONTOLOGY; PREDICTION; SEQUENCE; DISCOVERY; OPINION; MODEL;
D O I
10.1093/bioinformatics/btp002
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Motivation: We analysed 148 human drug target proteins and 3573 non-drug targets to identify differences in their properties and to predict new potential drug targets. Results: Drug targets are rare in organelles; they are more likely to be enzymes, particularly oxidoreductases, transferases or lyases and not ligases; they are involved in binding, signalling and communication; they are secreted; and have long lifetimes, shown by lack of PEST signals and the presence of N-glycosylation. This can be summarized into eight key properties that are desirable in a human drug target, namely: high hydrophobicity, high length, SignalP motif present, no PEST motif, more than two N-glycosylated amino acids, not more than one O-glycosylated Ser, low pI and membrane location. The sequence features were used as inputs to a support vector machine (SVM), allowing the assignment of any sequence to the drug target or non-target classes with an accuracy in the training set of 96%. We identified 668 proteins (23%) in the non-target set that have target-like properties. We suggest that drug discovery programmes would be more likely to succeed if new targets are chosen from this set or their homologues.
引用
收藏
页码:451 / 457
页数:7
相关论文
共 32 条
[1]  
[Anonymous], Data Mining Practical Machine Learning Tools and Techniques with Java
[2]   Gene Ontology: tool for the unification of biology [J].
Ashburner, M ;
Ball, CA ;
Blake, JA ;
Botstein, D ;
Butler, H ;
Cherry, JM ;
Davis, AP ;
Dolinski, K ;
Dwight, SS ;
Eppig, JT ;
Harris, MA ;
Hill, DP ;
Issel-Tarver, L ;
Kasarskis, A ;
Lewis, S ;
Matese, JC ;
Richardson, JE ;
Ringwald, M ;
Rubin, GM ;
Sherlock, G .
NATURE GENETICS, 2000, 25 (01) :25-29
[3]   GOstat: find statistically overrepresented Gene Ontologies within a group of genes [J].
Beissbarth, T ;
Speed, TP .
BIOINFORMATICS, 2004, 20 (09) :1464-1465
[4]   Improved prediction of signal peptides: SignalP 3.0 [J].
Bendtsen, JD ;
Nielsen, H ;
von Heijne, G ;
Brunak, S .
JOURNAL OF MOLECULAR BIOLOGY, 2004, 340 (04) :783-795
[5]   Sequence and structure-based prediction of eukaryotic protein phosphorylation sites [J].
Blom, N ;
Gammeltoft, S ;
Brunak, S .
JOURNAL OF MOLECULAR BIOLOGY, 1999, 294 (05) :1351-1362
[6]   Drug target identification using side-effect similarity [J].
Campillos, Monica ;
Kuhn, Michael ;
Gavin, Anne-Claude ;
Jensen, Lars Juhl ;
Bork, Peer .
SCIENCE, 2008, 321 (5886) :263-266
[7]   Structure-based maximal affinity model predicts small-molecule druggability [J].
Cheng, Alan C. ;
Coleman, Ryan G. ;
Smyth, Kathleen T. ;
Cao, Qing ;
Soulard, Patricia ;
Caffrey, Daniel R. ;
Salzberg, Anna C. ;
Huang, Enoch S. .
NATURE BIOTECHNOLOGY, 2007, 25 (01) :71-75
[8]   JPred: a consensus secondary structure prediction server [J].
Cuff, JA ;
Clamp, ME ;
Siddiqui, AS ;
Finlay, M ;
Barton, GJ .
BIOINFORMATICS, 1998, 14 (10) :892-893
[9]   Drug discovery: A historical perspective [J].
Drews, J .
SCIENCE, 2000, 287 (5460) :1960-1964
[10]   Predicting protein druggability [J].
Hajduk, PJ ;
Huth, JR ;
Tse, C .
DRUG DISCOVERY TODAY, 2005, 10 (23-24) :1675-1682