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Bioinformatics Advance Access published online on January 21, 2009
Bioinformatics, doi:10.1093/bioinformatics/btp002
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© The Author (2009). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Properties and Identification of Human Protein Drug Targets

Tala M. Bakheet 1 and Andrew J. Doig 2,*

1 Faculty of Life Sciences, The University of Manchester, Michael Smith Building, Oxford Road, Manchester M13 9PT, UK
2Manchester Interdisciplinary Biocentre, The University of Manchester, 131 Princess Street, Manchester M1 7DN, UK.

*To whom correspondence should be addressed. Prof. Andrew Doig, E-mail: andrew.doig{at}manchester.ac.uk


  Abstract

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 summarised into eight key properties that are desirable in a human drug target, namely: high hydrophobicity, high length, Sig-nalP motif present, no PEST motif, more than 2 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, 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.

Contact: andrew.doig{at}manchester.ac.uk

Supplementary Information (SI):

Fig. 1 Gene Ontology Terms

  1. (a)Molecular Function. Drug Target Level 1
  2. (b)Molecular Function. Drug Target Level 2
  3. (c)Molecular Function. Non-Drug Target Level 1
  4. (d)Molecular Function. Non-Drug Target Level 2
  5. (e)Cellular Component. Drug Target Level 1
  6. (f)Cellular Component. Drug Target Level 2
  7. (g)Cellular Component. Non-Drug Target Level 1
  8. (h)Cellular Component. Non-Drug Target Level 2
  9. (i)Biological Process. Drug Target Level 1
  10. (j)Biological Process. Drug Target Level 2
  11. (k)Biological Process. Non-Drug Target Level 1
  12. (l)Biological Process. Non-Drug Target Level 2

Table 1. Data sets of human drug target and non-drug target pro-teins

Table 2. GOstat output of significant GO terms present in the drug target data set.

Table 3. Features of target and non-drug targets divided into mem-brane and non-membrane proteins.

Table 4. Predicted drug targets, subdivided by primary EC number, and predicted non-drug targets

Document 1.The Effect of Using an Imperfect Predictor on p-Values

Associate Editor: Prof. John Quackenbush

Received on July 22, 2008; revised on December 19, 2008; accepted on December 30, 2008

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