Dr. PIAS 2.0: an update of a database of predicted druggable protein-protein interactions

Nobuyoshi Sugaya, Satoru Kanai, Toshio Furuya, Nobuyoshi Sugaya, Satoru Kanai, Toshio Furuya

Abstract

Druggable Protein-protein Interaction Assessment System (Dr. PIAS) is a database of druggable protein-protein interactions (PPIs) predicted by our support vector machine (SVM)-based method. Since the first publication of this database, Dr. PIAS has been updated to version 2.0. PPI data have been increased considerably, from 71,500 to 83,324 entries. As the new positive instances in our method, 4 PPIs and 10 tertiary structures have been added. This addition increases the prediction accuracy of our SVM classifier in comparison with the previous classifier, despite the number of added PPIs and structures is small. We have introduced the novel concept of 'similar positives' of druggable PPIs, which will help researchers discover small compounds that can inhibit predicted druggable PPIs. Dr. PIAS will aid the effective search for druggable PPIs from a mine of interactome data being rapidly accumulated. Dr. PIAS 2.0 is available at http://www.drpias.net.

Figures

Figure 1
Figure 1
Pie charts of the number of times each positive instance (see ‘Legend’ of the chart) was located nearest to (A) IL1B/IL1R1 or (B) XIAP/DIABLO in a feature space, when IL1B/IL1R1 or XIAP/DIABLO was assessed by our SVM-based method. (A) Druggability score (using all attributes) of IL1B/IL1R1 is 0.8652. This means that IL1B/IL1R1 was judged to be positive 8652 times in the 10 000 training-prediction iteration. Among the 8652, IL1B/IL1R1 is 8600 times most closely located to itself in the feature space. Structural attributes are based on the PDB entry 1ITB. This is a screenshot of http://www.drpias.net/view_similar_positives.php?attr=all_attr&interaction_id=28988. (B) Druggability score (using all attributes) of XIAP/DIABLO is 0.8305. This means that XIAP/DIABLO was judged to be positive 8305 times in the 10 000 training-prediction iteration. Among the 8305, 4 positive instances (XIAP/CASP9(PDB:1nw9_A), XIAP/DIABLO(PDB:1g73_C), XIAP/DIABLO(PDB:1g73_D), and XIAP/DIABLO(PDB:2opy_A)) are 1011–2729 times most closely located to XIAP/DIABLO in the feature space. Structural attributes are based on the PDB entry 1G73. This is a screenshot of http://www.drpias.net/view_similar_positives.php?attr=all_attr&interaction_id=3100.

References

    1. Arkin MR, Wells JA. Small-molecule inhibitors of protein–protein interactions: progressing towards the dream. Nat. Rev. Drug Discov. 2004;3:301–317.
    1. Pagliaro L, Felding J, Audouze K, et al. Emerging classes of protein–protein interaction inhibitors and new tools for their development. Curr. Opin. Chem. Biol. 2004;8:442–449.
    1. Zhao L, Chmielewski J. Inhibiting protein–protein interactions using designed molecules. Curr. Opin. Struct. Biol. 2005;15:31–34.
    1. Wells JA, McClendon CL. Reaching for high-hanging fruit in drug discovery at protein–protein interfaces. Nature. 2007;450:1001–1009.
    1. Zhu F, Shi Z, Qin C, et al. Therapeutic target database update 2012: a resource for facilitating target-oriented drug discovery. Nucleic Acids Res. 2012;40:D1128–D1136.
    1. Hecker N, Ahmed J, von Eichborn J, et al. SuperTarget goes quantitative: update on drug–target interactions. Nucleic Acids Res. 2012;40:D1113–D1117.
    1. Higueruelo AP, Schreyer A, Bickerton GR, et al. Atomic interactions and profile of small molecules disrupting protein–protein interfaces: the TIMBAL database. Chem. Biol. Drug Des. 2009;74:457–467.
    1. Bourgeas R, Basse MJ, Morelli X, et al. Atomic analysis of protein–protein interfaces with known inhibitors: the 2P2I database. PLoS One. 2010;5:e9598.
    1. Edfeldt FN, Folmer RH, Breeze AL. Fragment screening to predict druggability (ligandability) and lead discovery success. Drug Discov. Today. 2011;16:284–287.
    1. Sugaya N, Ikeda K, Tashiro T, et al. An integrative in silico approach for discovering candidates for drug-targetable protein–protein interactions in interactome data. BMC Pharmacol. 2007;7:10.
    1. Sugaya N, Ikeda K. Assessing the druggability of protein–protein interactions by a supervised machine-learning method. BMC Bioinformatics. 2009;10:263.
    1. Sugaya N, Furuya T. Dr. PIAS: an integrative system for assessing the druggability of protein–protein interactions. BMC Bioinformatics. 2011;12:50.
    1. Maglott D, Ostell J, Pruitt KD, et al. Entrez Gene: gene-centered information at NCBI. Nucleic Acids Res. 2011;39:D52–D57.
    1. Bader GD, Betel D, Hogue CW. BIND: the Biomolecular Interaction Network Database. Nucleic Acids Res. 2003;31:248–250.
    1. Stark C, Breitkreutz BJ, Chatr-Aryamontri A, et al. The BioGRID Interaction Database: 2011 update. Nucleic Acids Res. 2011;39:D698–D704.
    1. Keshava Prasad TS, Goel R, Kandasamy K, et al. Human protein reference database—2009 update. Nucleic Acids Res. 2009;37:D767–D772.
    1. Stelzl U, Worm U, Lalowski M, et al. A human protein–protein interaction network: a resource for annotating the proteome. Cell. 2005;122:957–968.
    1. Lim J, Hao T, Shaw C, et al. A protein–protein interaction network for human inherited ataxias and disorders of Purkinje cell degeneration. Cell. 2006;125:801–814.
    1. Ramani AK, Li Z, Hart GT, et al. A map of human protein interactions derived from co-expression of human mRNAs and their orthologs. Mol. Syst. Biol. 2008;4:180.
    1. Meireles LM, Dömling AS, Camacho CJ. ANCHOR: a web server and database for analysis of protein–protein interaction binding pockets for drug discovery. Nucleic Acids Res. 2010;38:W407–W411.
    1. Meslamani J, Rognan D, Kellenberger E. sc-PDB: a database for identifying variations and multiplicity of ‘druggable’ binding sites in proteins. Bioinformatics. 2011;27:1324–1326.

Source: PubMed

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