›› 2010, Vol. 25 ›› Issue (1): 124-130.

• Special Issue on Computational Challenges from Modern Molecular Biology • Previous Articles     Next Articles

Protein Interactome Analysis for Countering Pathogen Drug Resistance

Limsoon Wong and Guimei Liu   

  1. School of Computing, National University of Singapore, 13 Computing Drive, Singapore 117417
  • Received:2009-10-22 Revised:2009-11-16 Online:2010-01-05 Published:2010-01-05
  • About author:
    Limsoon Wong is concurrently a professor of computer science and a professor of pathology at the National University of Singapore. He works mostly on knowledge discovery technologies and their application to biomedicine. He serves on the editorial boards of Information Systems (Elsevier), Journal of Bioinformatics and Computational Biology (ICP), Bioinformatics (OUP), and Drug Discovery Today (Elsevier). He is a scientific advisor to Semantic Discovery Systems (UK), Molecular Connections (India), and CellSafe International (Malaysia).
    Guimei Liu is a senior research fellow at National University of Singapore School of Computing. She received her Ph.D. degree in computer science from Hong Kong University of Science and Technology in 2005. Her current research interests include frequent pattern mining and its applications, and protein interaction networks mining and analysis.
  • Supported by:

    This work was supported in part by Singapore National Research Foundation under Grant No. NRF-G-CRP-2997-04-082(d).

Drug-resistant varieties of pathogens are now a recognized global threat. Insights into the routes for drug resistance in these pathogens are critical for developing more effective antibacterial drugs. A systems-level analysis of the genes, proteins, and interactions involved is an important step to gaining such insights. This paper discusses some of the computational challenges that must be surmounted to enable such an analysis; viz., unreliability of bacterial interactome maps, paucity of bacterial interactome maps, and identification of pathways to bacterial drug resistance.

1] Antimicrobial Resistance Interagency Task Force 2007 Annual Report. CDC USA. 2008.
[2] Johnson R et al. Drug resistance in mycobacterium tuberculosis. Curr. Issues Mol. Biol., 2006, 8(2): 97-111.
[3] Raman K, Chandra N. Mycobacterium tuberculosis interactome analysis unravels potential pathways to drug resistance. BMC Microbiol., 2008, 8: 234.
[4] Nguyen L, Thompson C J. Foundations of antibiotic resistance in bacteria physiology: The mycobacterial paradigm. Trends Microbiol., 2006, 14(7): 304-312.
[5] Uetz P, Giot L, Cagney G, Mansfield T A et al. A comprehensive analysis of protein-protein interactions in Saccharomyces cerevisiae. Nature, 2000, 403(6770): 623-627.
[6] Ito T, Chiba T, Ozawa R, Yoshida M et al. A comprehensive two-hybrid analysis to explore the yeast protein interactome. Proc. Natl. Acad. Sci. USA, 2001, 98(8): 4569-4574.
[7] Li S, Armstrong C M, Bertin N et al. A map of the interactome network of the metazoan C. elegans. Science, 2004, 303(5657): 540-543.
[8] Giot L, Bader J S, Brouwer C, Chaudhuri A et al. A protein interaction map of drosophila melanogaster. Science, 2003, 302(5651): 1727-1736.
[9] Rual J F, Venkatesan K, Hao T et al. Towards a proteomescale map of the human protein-protein interaction network. Nature, 2005, 437(7062): 1173-1178.
[10] Stelzl U, Worm U, Lalowski M, Haenig C et al. A human protein-protein interaction network: A resource for annotating the proteome. Cell, 2005, 122(6): 957-968.
[11] Gavin A C, Aloy P, Grandi P, Krause R et al. Proteome survey reveals modularity of the yeast cell machinery. Nature, 2006, 440(7084): 631-636.
[12] Krogan N J, Cagney G, Yu H et al. Global landscape of protein complexes in the yeast saccharomyces cerevisiae. Nature, 2006, 440(7084): 637-643.
[13] Collins S R, Kemmeren P, Zhao X C et al. Towards a comprehensive atlas of the physical interactome of saccharomyces cerevisiae. Molecular & Cellular Proteomics, 2007, 6(3): 439- 450.
[14] Rain J C, Selig L, De Reuse H et al. The protein-protein interaction map of Helicobacter pylori. Nature, 2001, 409(6817): 211-215.
[15] Parrish J R, Yu J, Liu G, Hines J A et al. A proteome-wide protein interaction map for campylobacter jejuni. Genome Biology, 2007, 8(7): R130.
[16] Su C et al. Bacteriome.org—An integrated protein interaction database for E. coli. Nucleic Acid Res., 2008, 36(Supplement 1): D632-D636.
[17] Hart G T, Ramani A K, Marcotte E M. How complete are current yeast and human protein-interaction networks? Genome Biology, 2006, 7(11): 120.
[18] Bailer S M, Haas J. Connecting viral with cellular interactomes. Current Opinion in Microbiology, 2009, 12(4): 453- 459.
[19] Sprinzak E, Sattath S, Margalit H. How reliable are experimental protein-protein interaction data? Journal of Molecular Biology, 2003, 327(5): 919-923.
[20] Xenarios I, Salwinski L, Duan X J, Higney P et al. DIP, the database of interacting proteins: A research tool for studying cellular networks of protein interactions. Nucleic Acids Research, 2002, 30(1): 303-305.
[21] Chua H N, Wong L. Increasing the reliability of protein interactomes. Drug Discovery Today, 2008, 13(15/16): 652-658.
[22] Nabieva E, Jim K, Agarwal A, Chazelle B, Singh M. Whole-proteome prediction of protein function via graphtheoretic analysis of interaction maps. Bioinformatics, 2005, 21(Suppl.1): i302-i310.
[23] Hart G T, Lee I, Marcotte E M. A high-accuracy consensus map of yeast protein complexes reveals modular nature of gene essentiality. BMC Bioinformatics, 2007, 8(1): 236.
[24] Ramani A K, Bunescu R C, Mooney R J, Marcotte E M. Consolidating the set of known human protein-protein interactions in preparation for large-scale mapping of the human interactome. Genome Biology, 2005, 6(5): R40.
[25] Chua H N, Hugo Willy, Liu G, Li X L, Wong L, Ng S-K. A probabilistic graph-theoretic approach to integrate multiple predictions for the protein-protein subnetwork prediction challenge. Annals of New York Academy of Sciences, 2009, 1158: 224-233.
[26] Liu G, Wong L, Chua H N. Complex discovery from weighted PPI networks. Bioinformatics, 2009, 25(15): 1891-1897.
[27] Schneider A et al. OMA Browser—Exploring orthologous relations across 352 complete genomes. Bioinformatics, 2007, 23(16): 2180-2182.
[28] Roth A et al. Algorithm of OMA for large-scale orthology inference. BMC Bioinformatics, 2008, 9: 518.
[29] Pertea M et al. OperonDB: A comprehensive database of predicted operons in microbial genomes. Nucleic Acid Res., 2009, 37(Database Issue): D479-D482.
[30] Zhang M, Leong H W. Gene team tree: A compact tree representation of all gene teams. In Proc. RECOMB Workshop on Comparative Genomics (RCG), Paris, France, October 13-15, 2008, pp.100-112.
[31] Jiang T. Some algorithmic challenges in genome-wide orthology assignment. Journal of Computer Science and Technology, 2010, 25(1): 42-52.
[32] Li X L et al. Improving domain-based protein interaction prediction using biologically-significant negative dataset. International Journal of Data Mining and Bioinformatics, 2006, 1(2): 138-149.
[33] Li H, Li J, Wong L. Discovering motif pairs at interaction sites from sequences on a proteome-wide scale. Bioinformatics, 2006, 22(8): 989-996.
[34] Mika S, Rost B. Protein-protein interactions more conserved within species than across species. PLoS Comput Biology, 2006, 2(7): 379.
[35] Wu X et al. Prediction of yeast protein-protein interaction network: Insights from the Gene Ontology and annotations. Nucleic Acid Res., 2006, 34(7): 2137-2150.
[36] Juan D, Pazos F, Valencia A. High-confidence prediction of global interactomes based on genome-wide coevolutionary networks. Proc. Natl. Acad. Sci. USA, 2008, 105(3): 934- 939.
[37] LiuG, Li J, Wong L. Assessing and predicting protein interactions using both local and global network topological metrics. In Proc. the 19th Int. Conf. Genome Informatics (GIW), Gold Coast, Australia, December 1-3, 2008, pp.138-149.
[38] Enright A J, Van Dongen S, Ouzounis C A. An efficient algorithm for large-scale detection of protein families. Nucleic Acids Research, 2002, 30(7): 1575-1584.
[39] Przulj N, Wigle D. Functional topology in a network of protein interactions. Bioinformatics, 2003, 20(3): 340-348.
[40] Altaf-Ul-Amin M et al. Development and implementation of an algorithm for detection of protein complexes in large interaction networks. BMC Bioinformatics, 2006, 7: 207.
[41] Adamcsek B et al. CFinder: Locating cliques and overlapping modules in biological networks. Bioinformatics, 2006, 22(8): 1021-1023.
[42] Chua H N, Ning K, Sung W-K, Leong H W, Wong L. Using indirect protein-protein interactions for protein complex prediction. Journal of Bioinformatics and Computational Biology, 2008, 6(3): 435-466.
[43] Leung H C M et al. Predicting protein complexes from PPI data: A core-attachment approach. J. Comput. Biol., 2009, 16(2): 133-164.
[44] Aloy P et al. Structure-based assembly of protein complexes in yeast. Science, 2004, 303(5666): 2026-2029.
[45] Mewes H W et al. MIPS: Analysis and annotation of proteins from whole genomes. Nucleic Acids Res., 2004, 32(Database Issue): D41-D44.
[46] Stark C, Breitkreutz B J, Reguly T, Boucher L et al. Bio- GRID: A general repository for interaction datasets. Nucleic Acids Research, 2006, 34(Database Issue): D535-D539.
[47] Altman R B. PharmGKB: A logical home for knowledge relating genotype to drug response phenotype. Nature Genet., 2007, 39(4): 426.
[48] Chowdhary R, Zhang J, Liu J S. Bayesian inference of proteinprotein interactions from biological literature. Bioinformatics, 2009, 25(12): 1536-1542.
[49] Dai H J, Chang Y C, Tsai R T H et al. New challenges for biological text mining in the next decade. Journal of Computer Science and Technology, 2010, 25(1): 169-inside back cover.
[50] Strong M, Eisenberg D. The protein network as a tool for finding novel drug targets. Progress in Drug Research, 2007, 64: 191-215.
[51] Smith P A, Romesberg F E. Combating bacteria and drug resistance by inhibiting mechanisms of persistence and adaptation. Nat. Chem. Biol., 2007, 3(9): 549-556.
[52] Valouev A, Johnson D S, Sundquist A, Medina C et al. Genome-wide analysis of transcription factor binding sites based on ChIP-Seq data. Nature Methods, 2008, 5(9): 829- 834.
[53] Karp R M. Reducibility among combinatorial problems. In Proc. Symp. Complexity of Computer Computations, New York, USA, March 20-22, 1972, pp.85-103.
[54] Leighton T, Rao S. Multicommodity max-flow min-cut theorems and their use in designing approximation algorithms. JACM, 1999, 46(6): 787-832.
[55] Powers D. Graph partitioning by eigenvectors. Lin. Alg. Appl., 1988, 101: 121-133.

No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] Cai Shijie; Zhang Fuyan;. A Fast Algorithm for Polygon Operations[J]. , 1991, 6(1): 91 -96 .
[2] Farid Mheir-ELSaadi; Bozena Kaminska;. An Automatic Hierarchical Delay Analysis Tool[J]. , 1994, 9(4): 349 -364 .
[3] Xiong Zhiguo; Xu Xi; Dong Shihai;. CX11: A Chinese Language Supporting Interface for X Window Environment[J]. , 1995, 10(1): 15 -22 .
[4] Zeng Jianchao; Hidehiko Sanada; Yoshikazu; Tezuka Xu Guangyou;. A Form-Correcting System of Chinese Characters Using a Model of Correcting Procedures of Calligraphists[J]. , 1995, 10(1): 23 -34 .
[5] LIN Hua; LU Mi; Jesse Z.FANG;. A Direct Approach for Finding Loop Transformation Matrices[J]. , 1996, 11(3): 237 -256 .
[6] Tian Zengping; Wang Yujun; Qu Yunyao; Shi Baile;. On the Expressive Power of F-Logic Language[J]. , 1997, 12(6): 510 -519 .
[7] WANG Xiaodong; XU Ming; ZHOU Xingming;. Fast Multicast on Multistage Interconnection Networks Using Multi-Head Worms[J]. , 1999, 14(3): 250 -258 .
[8] XU Xiaofei; YE Dan; LI Quanlong; ZHAN Dechen;. Dynamic Organization and Methodology for Agile Virtual Enterprises[J]. , 2000, 15(4): 368 -375 .
[9] Peter M. Haverty, Zhi-Ping Weng, and Ulla Hansen. Transcriptional Regulatory Networks Activated by PI3K and ERK Transduced Growth Signals in Human Glioblastoma Cells[J]. , 2005, 20(4): 439 -445 .
[10] Qi Ge, Hai-Tao Wang, and Hong Zhu. An Improved Algorithm for Finding the Closest Pair of Points[J]. , 2006, 21(1): 27 -31 .

ISSN 1000-9000(Print)

         1860-4749(Online)
CN 11-2296/TP

Home
Editorial Board
Author Guidelines
Subscription
Journal of Computer Science and Technology
Institute of Computing Technology, Chinese Academy of Sciences
P.O. Box 2704, Beijing 100190 P.R. China
Tel.:86-10-62610746
E-mail: jcst@ict.ac.cn
 
  Copyright ©2015 JCST, All Rights Reserved