目前，病原菌抗药性的研究在传染病领域的是一个棘手性问题。由于缺乏对抗药机制的深刻认识，人们很难设计出有效的抗病毒药物。然而，随着生物计算技术的迅猛发展，有人提出利用系统地分析基因、蛋白以及他们之间的相互作用去揭开病原菌的抗药机制，进而设计出高效的抗病毒药物。这种系统的分析方法必须要求较全面的蛋白相互作用的数据。例如，假设结核杆菌有较为全面的蛋白相互作用数据，那么就可以推断出哪些蛋白（或蛋白相互作用）对于结核杆菌是重要且必须的；另外，还可以找到药物靶标与各种外排泵相关蛋白以及药物修饰的酶蛋白之间相互作用的路径，这对抗药机制的研究会有很大的启发。
虽然关于人和线虫等的蛋白相互作用的实验数据较为丰富，但关于细菌蛋白相互作用的分析却很少，并且质量也有待提高。有数据表明，在10，000对蛋白相互作用中，只有不到25%的相互作用是多于两个实验检测到的。这说明这些数据存在高假阳性和假阴性率。因此，通过对病原菌蛋白相互作用的分析来寻找抗药通路存在一定的困难。概括的说，利用系统分析蛋白相互作用来鉴定抗药通路存在如下困难：（1）病原菌的蛋白相互作用数据可靠性低。（2）病原菌相互作用的数据较为缺乏。（3）识别病原菌抗药的候选通路。
针对以上困难，本文以结核杆菌为例，探讨其解决方法。
由于病原菌蛋白相互作用数据噪音大，因此，开发新的实验技术或方法以提高蛋白相互作用数据的质量极其重要。由于相互作用的蛋白通常拥有相同的细胞定位或者扮演相同的功能角色，因此，我们可以利用其它额外的信息，比如蛋白的功能注释等信息，以估计每一个蛋白相互作用的可靠指数。但是，像结核杆菌等一些物种往往缺乏这些额外的信息，这种情况下，我们只能依靠不依赖额外信息的方法去估计蛋白相互作用数据的可靠性，比如依靠蛋白相互作用网络的拓扑结构的方法。这种方法基于一个假设：即共享较多“伙伴”的两个蛋白更倾向于共定位或参与共同的细胞生物过程，因而更有可能存在相互作用。共享的“伙伴”的比率可以仅仅考虑蛋白的一级邻接节点，也可以考虑多级节点。用这个比率可以选取可靠性高的酵母蛋白相互作用数据。结果表明，在前3000对可靠性高度相互作用数据中，共细胞定位的占70%以上，相比总的蛋白相互作用数据，增加了15%以上。但是，这种方法不太适用于稀疏的蛋白相互作用网络。因此，寻找新的方法以过滤高质量的蛋白相互作用数据成为当前研究的重点。
对于缺乏纵多病原菌的蛋白相互作用信息，这个问题，主要可以从三个方面去解决：一、利用物种的同源性将其他病原菌的保守蛋白相互作用转化该物种的蛋白相互作用；二、从头预测该物种的蛋白相互作用和蛋白复合物，从头预测蛋白相互作用的方法主要包括利用功能域-功能域的相互作用、相互作用的功能域、旁系同源的相互作用、蛋白功能的相似性以及蛋白的共进化信息等，而对于蛋白复合物，则可以利用蛋白相互作用网络的拓扑结构，比如利用一些算法包括MCL、RNSC等进行网络聚类从而得到连系较为紧密的蛋白团，但是这些算法的敏感性以及精确性较低，主要是因为蛋白相互作用的数据噪音大；三、从已有的文献报道挖掘该物种的蛋白相互作用，这种方法通常设计一些工具挖掘数据库的信息，比如Pubmed的文献摘要、US FDA网站的报道以及类似PharmGKB的数据库。利用这种方法提取蛋白相互作用的敏感性和精确性高于80%，但是还不够满意，特别是关于药物-酶的相互作用数据的挖掘还不够成熟。
如何识别病原菌中抗药的候选通路。这个问题主要归结为两点，一是识别病原菌中抗药的必要通路，主要包括药物特异的外排或药物作用的酶或排毒蛋白，以及药物是通过哪个通路与外排泵或药物作用的酶或排毒蛋白相互作用的；二是寻找这些通路最终共同作用或扰乱的靶标。
目前关于病原菌抗药性的研究并无突破性进展，因此使得该领域的研究变得极其迫切和重要。通过系统地分析病原菌基因调节和蛋白相互作用网络，为揭示病原菌抗药性的机制、开发出高效的抗病毒药物开辟了新的途径。

School of Computing, National University of Singapore, 13 Computing Drive, Singapore 117417

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.

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

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.