Volume 12 Supplement 1
Comparative analysis of protein interaction networks reveals that conserved pathways are susceptible to HIV-1 interception
© Qian and Yoon; licensee BioMed Central Ltd. 2011
Published: 15 February 2011
Human immunodeficiency virus type one (HIV-1) is the major pathogen that causes the acquired immune deficiency syndrome (AIDS). With the availability of large-scale protein-protein interaction (PPI) measurements, comparative network analysis can provide a promising way to study the host-virus interactions and their functional significance in the pathogenesis of AIDS. Until now, there have been a large number of HIV studies based on various animal models. In this paper, we present a novel framework for studying the host-HIV interactions through comparative network analysis across different species.
Based on the proposed framework, we test our hypothesis that HIV-1 attacks essential biological pathways that are conserved across species. We selected the Homo sapiens and Mus musculus PPI networks with the largest coverage among the PPI networks that are available from public databases. By using a local network alignment algorithm based on hidden Markov models (HMMs), we first identified the pathways that are conserved in both networks. Next, we analyzed the HIV-1 susceptibility of these pathways, in comparison with random pathways in the human PPI network. Our analysis shows that the conserved pathways have a significantly higher probability of being intercepted by HIV-1. Furthermore, Gene Ontology (GO) enrichment analysis shows that most of the enriched GO terms are related to signal transduction, which has been conjectured to be one of the major mechanisms targeted by HIV-1 for the takeover of the host cell.
This proof-of-concept study clearly shows that the comparative analysis of PPI networks across different species can provide important insights into the host-HIV interactions and the detailed mechanisms of HIV-1. We expect that comparative multiple network analysis of various species that have different levels of susceptibility to similar lentiviruses may provide a very effective framework for generating novel, and experimentally verifiable hypotheses on the mechanisms of HIV-1. We believe that the proposed framework has the potential to expedite the elucidation of the important mechanisms of HIV-1, and ultimately, the discovery of novel anti-HIV drugs.
Acquired immune deficiency syndrome (AIDS), one of the most destructive pandemics in recorded history according to the statistics from the World Health Organization (WHO) , has killed more than 25 million people since it was first recognized in 1981. Human immunodeficiency virus type one (HIV-1) has been found to be the causative pathogen of AIDS [2, 3]. HIV-1 is a lentivirus, a slow retrovirus that is responsible for long-duration illness with a long incubation period. HIV-1 has 9 genes which encode up to 19 proteins due to post-translational cleavage . By reverse transcription from viral RNA to host-integrable DNA, the virus can become active and replicate to cause rapid T cell depletion, immune system collapse, and opportunistic infections that mark the advent of AIDS .
Although advances in antiviral therapy and management of opportunistic infection for AIDS have remarkably improved the general health, the expensive cost and adverse effects of the available drugs have motivated many researchers to explore novel avenues to anti-HIV-1 drug discovery. With the increasing coverage of HIV-1 and human protein interactions in the literature [6–11], a human/HIV-1 interactome has been created , which can play a critical role in better understanding the virology and pathology of this infectious disease and developing new therapeutics. In addition to this, the availability of large-scale biological networks, including protein-protein interaction (PPI) networks, has led to the introduction of systems biology approaches for novel HIV-1 drug discovery [13, 14]. In , Balakrishnan et al. proposed to find alternative pathways to circumvent the HIV-1 intercepted pathways based on the efficiency and robustness of biological processes. The main goal was to generate new hypotheses regarding HIV-1 targeted pathways and their effects on various molecular functions, which will help us better understand the mechanisms of HIV-1 takeover of the host cell and find ways to circumvent it. The study was based on curated signal transduction pathways obtained from multiple pathway databases. One practical problem of this pathway-based approach is that the currently known pathways cover only a limited number of human proteins, hence it may exclude important HIV-1 targets from the analysis. Moreover, many curated pathways in public databases overlap with each other, which may introduce bias in the analysis. On the other hand, Lin et al.  proposed comparative studies of host-virus protein interactions across human (Homo sapiens) and other animal models that may be invaded by similar lentiviruses that cause immunosuppression or immunoproliferation, including three mammalian species: chimpanzee (Pan troglodyte), rhesus macaque (Macaca mulatta), and mouse (Mus musculus). All these animal models have been extensively studied to understand the HIV-1 host-virus interplay [15, 16]. Comparative studies of host-virus interactions may provide new insights into why different species have different susceptibility to HIV-1, which may lead to the development of potential therapeutics in the long run.
Motivated by these works, we propose a novel framework for studying human/HIV-1 interactions, based on comparative analysis of the human PPI network and the PPI network of other species that are susceptible to lentivirus invasion. It has been shown that the comparative analysis of PPI networks of different species can identify conserved pathways that carry essential cellular functionalities [17–36]. Furthermore, HIV-1 has to be a “minimalist” in order to survive, and for this reason, it has been believed to target these essential pathways that are conserved across species [13, 37]. As a result, the comparative analysis of PPI networks may be used to generate new hypotheses that will be useful in improving our understanding of the mechanisms of HIV-1 takeover of the host cell, and ultimately, for developing effective therapeutics for AIDS.
Results and discussion
Conserved pathways are susceptible to HIV-1 attacks
Our main goal in this paper is to validate the following hypothesis:
“Essential biological pathways that are conserved across different species that are susceptible to lentiviruses, have a high probability of being intercepted by HIV-1.”
Next, we extracted 3,000 random pathways of different sizes (L = 16, 32, and 64) by performing a random walk on the Homo sapiens network (see Methods). Then we compared the HIV-1 susceptibility of the conserved pathways with that of the random pathways in the Homo sapiens PPI network, by using the predicted human/HIV-1 interactome map in .
Number of proteins in the pathways that are intercepted by HIV-1
Total number of human/HIV-1 protein interactions within one pathway
To further validate our hypothesis, we checked the total number of human/HIV-1 interactions within each pathway. Again, we used the predicted human/HIV-1 interactome in  to count the total number of human/HIV-1 interactions within each pathway. Figure 4(B) shows the p-value of the total number of HIV-1 interactions within every conserved pathway (with no gaps). As before, the baseline distribution was estimated using the histogram of the total number of HIV-1 interactions in randomly extracted pathways. Note that, for long conserved pathways (L = 64), the p-values are always below 0.03. These results show that the difference in susceptibility to HIV-1 interception between the conserved and random pathways is statistically significant.
HIV-1 interaction score of conserved pathways
Finally, we evaluated the HIV-1 interaction score for conserved pathways based on the scoring scheme in . For this evaluation, we mapped the prediction scores of human/HIV-1 interactions onto the conserved pathways and computed their average. In a similar way, we computed the average HIV-1 interaction score for each random pathway and estimated the distribution of these average scores. Then we computed the p-values of the average prediction scores for all the conserved pathways. These p-values are shown in Fig. 4(C) for the conserved pathways with different lengths. By comparing the results in Fig. 4(C) and those shown in Fig. 4(A,B), we can clearly see that there exists considerable correlation between these results. This is especially interesting, if we consider the fact that our approach does not use any extra data except for the PPI networks, while the prediction algorithm in  is obtained by integrating the information from various sources, such as gene expression, domain and motif identification, tissue distribution, functional annotation, subcellular localization and human network features, and HIV-1’s mimicry of human protein binding partners.
GO term enrichment analysis
Selected GO terms enriched in the top 20 conserved pathways of size L = 64 with adjusted p-values.
Gene Ontology terms
regulation of cellular process
regulation of biological process
intracellular signaling pathway
intracellular signal transduction
phosphate metabolic process
enzyme linked receptor protein signaling pathway
anatomical structure development
cell surface receptor linked signaling pathway
response to endogenous stimulus
cellular response to stimulus
response to stimulus
protein modification process
regulation of metabolic process
response to hormone stimulus
regulation of biosynthetic process
Ras protein signal transduction
response to stress
RNA biosynthetic process
cellular macromolecule biosynthetic process
regulation of transferase activity
immune system development
regulation of immune system process
hemopoietic or lymphoid organ development
UniProt accession numbers of selected proteins in the top 20 conserved pathways of size L = 64 with protein names and the associated top ontology keywords and GO terms listed by the UniProt database .
Gene Ontology terms
Cellular tumor antigen p53
apoptosis; host-virus interaction; DNA damage response; protein tetramerization
cAMP-dependent protein kinase catalytic subunit α
hormone-mediated signaling pathway; intracellular protein kinase cascade
Mitogen-activated protein kinase 1
Ras protein signal transduction; cell cycle; transcription; interspecies interaction between organisms; chemotaxis; synaptic transmission
Mitogen-activated protein kinase 3
Ras protein signal transduction; interspecies interaction between organisms
Transcription factor AP-1
SMAD protein signal transduction; positive regulation by host of viral transcription; transforming growth factor (TGF) β receptor signaling pathway
Tyrosine-protein kinase Fyn
T cell receptor signaling pathway; interspecies interaction between organisms
Cell division protein kinase 1
anti-apoptosis; cell division; mitosis
Mothers against decapentaplegic homolog 2
SMAD protein complex assembly; intracellular signaling pathway; palate development; transcription; TGF β receptor signaling pathway
Cell cycle; Host-virus interaction; androgen receptor signaling pathway; myoblast differentiation
RAF proto-oncogene serine/ threonine-protein kinase
Ras protein signal transduction; cell proliferation; protein amino acid phosphorylation
Suppressor of cytokine signaling 7
Ubl conjugation pathway; regulation of growth; negative regulation of signal transduction
Mast/stem cell growth factor receptor
male gonad development; transmembrane receptor protein tyrosine; kinase signaling pathway
Transcription factor 4
cerebral cortex development; regulation of smooth muscle cell proliferation; transcription
Cytotoxic T-lymphocyte protein 4
immune response; negative regulation of regulatory T cell differentiation
Non-receptor tyrosine-protein kinase TYK2
intracellular protein kinase cascade; peptidyl-tyrosine phosphorylation
GRB2-associated-binding protein 1
cell proliferation; epidermal growth factor receptor signaling pathway; insulin receptor signaling pathway
Son of sevenless homolog 2
apoptosis; regulation of Rho protein ; signal transduction; small GTPase mediated signal transduction
Cdk inhibitor p27KIP1
cell cycle arrest
Results using curated human/HIV-1 protein interactions
Local network alignment can effectively identify conserved pathways that are biologically meaningful . If HIV-1 is a minimalist in order to survive and therefore targets essential pathways , as other viruses do, it is natural to expect that essential pathways that are conserved across different species should be highly vulnerable to HIV-1 attacks. Our analysis based on comparing the Homo sapiens PPI network and the Mus musculus PPI network indicates that our conjecture is indeed true. This proof-of-concept study that we present clearly shows that the comparative network analysis of different species can provide important insights into the mechanisms of human/HIV-1 interactions. We believe that further studies based on aligning the networks of various species that are susceptible to similar lentiviruses will lead to breakthroughs in HIV research. For example, although chimpanzees are the human’s closest relative in nature, AIDS is rarely life-threatening to them . Identifying the main reasons for this difference in HIV-1 susceptibility may lead to the development of novel therapeutics for this highly destructive disease. Balakrishnan et al.  proposed a heuristic way to search for alternative pathways that can circumvent HIV-intercepted pathways, whose ultimate goal is to identify potential drug targets. In a similar way, comparative network analysis may also be used to identify alternative pathways in the PPI network, by querying known HIV-intercepted pathways in the human PPI network. Although comparative network analysis is still at an early stage and is not yet as mature as comparative sequence analysis, it can take direct advantage of the large-scale interaction measurements that have become available these days and it has the potential to generate experimentally verifiable hypotheses on the biological mechanisms of HIV-1, which may lead to the identification of better drug targets and innovative AIDS therapeutics in the future.
Protein-protein interaction (PPI) networks
We have obtained both the Homo sapiens and Mus musculus protein-protein interaction (PPI) networks from the open platform NATALIE , managed by the Knowledge Management in Bioinformatics group of the Humboldt-Universität Berlin. Both networks were obtained from several open databases [45–50] as described in [24, 51]. The Homo sapiens network has 34,979 interactions among 9,695 proteins, and the Mus musculus network has 3,116 interactions among 3, 247 proteins. The similarity between proteins in the two networks were determined based on protein sequences, protein domain information (InterProdomains) and functional annotations (GO annotations) [51–54]. Pairs of similar proteins in the two networks were identified based on a minimum protein identity threshold of α = 0.4 as in [24, 51].
Human-HIV interaction data
As mentioned in , there are several types of interactions between HIV-1 proteins and human proteins, including direct physical interactions that are reported in the literature, indirect interactions reported in the literature, and interactions that have been manually annotated by experts [12, 43]. However, many HIV-1 virologists do not agree upon the majority of these interactions . For this reason, we focus on the human/HIV-1 interactome in  in our analysis, which has been computationally predicted by integrating various types of protein features. The human/HIV-1 interactome data can be obtained from the authors’ website . For further validation, we also performed similar analysis based on the curated human/HIV-1 protein interactions in HPID .
Identification of conserved pathways through local network alignment
The interaction reliability score w m (v i , v j ) between v i and v j in the Mus musculus network is defined in a similar way.
by computing the score (3) iteratively. As discussed in [35, 36], we can add auxiliary states to the HMMs that represent the PPI networks to find gapped path alignments. Instead of finding only the best matching pair of paths, we can also search for the top k path pairs by replacing the max operator in (4) and (3) by an operator that finds the k largest scores. The computational complexity of the described dynamic programming algorithm is only O(kLM1M2) for finding the top k pairs of matching paths, where M1 is the number of edges (i.e., interactions) in G h , and M2 is the number of edges in G m . Note that the computational complexity is linear with respect to each parameter k, L, M1, and M2. To avoid multiple occurrences of the same protein in the conserved pathways that are predicted by the algorithm, we incorporate a “look-back” step into each iteration of the dynamic programming algorithm .
Extraction of random pathways
In order to extract random pathways from the Homo sapiens PPI network, we performed random walks on the network starting from a randomly selected node in network G h . We randomly walk on the network to choose a random pathway, until the size of the pathway reaches a pre-specified size L. During this random walk, we avoid visiting a node that has been previously visited, so that the extracted random pathway contains only distinct nodes.
Comparison between conserved pathways and random pathways
To compare the HIV-1 susceptibility of conserved pathways with that of random pathways, we computed the following values: (1) The number of proteins within these pathways that have been predicted to be intercepted by at least one of the HIV-1 proteins according to the human/HIV-1 interactome in . (2) The total number of predicted human/HIV-1 protein interactions within these pathways; (3) The average HIV interaction scores within pathways. We also computed the p-values of the estimated results for conserved pathways, according to the process described in the next subsection. Finally, for GO term enrichment analysis, we used an open source software called the GO::TermFinder .
In order to evaluate the statistical significance of the estimated results in conserved pathways, we first extract a large number of random pathways (3, 000) from the Homo sapiens PPI network, based on random walk. For each random pathway, we also estimate the indices of HIV-1 susceptibility (i.e., the number of human proteins intercepted by HIV-1; the total number of human/HIV-1 interactions, the average interaction scores among the proteins in each pathway). Baseline distributions of different indices are estimated from these results. We can either model the baseline distributions using Gumbel distributions  or simply use histograms. The latter approach was adopted in this paper. Once we have estimated the baseline distributions, we can compute the p-values of the estimated results in conserved pathways according to the estimated distributions.
XQ was supported in part by the University of South Florida Internal Awards Program under Grant No. 78068. BJY was supported in part by the Texas A&M Faculty start-up fund.
This article has been published as part of BMC Bioinformatics Volume 12 Supplement 1, 2011: Selected articles from the Ninth Asia Pacific Bioinformatics Conference (APBC 2011). The full contents of the supplement are available online at http://www.biomedcentral.com/1471-2105/12?issue=S1.
- Joint United Nations Programme on HIV/AIDS: Overview of the global AIDS epidemic 2006 Report on the global AIDS epidemic. ISBN 9291734799 2006.
- Weiss R: How does HIV cause AIDS? Science 1993, 260(5112):1273–1279. 10.1126/science.8493571View ArticlePubMedGoogle Scholar
- Douek D, Roederer M, Koup R: Emerging concepts in the immunopathogenesis of AIDS. Annu Rev Med 2009, 60: 471–484. 10.1146/annurev.med.60.041807.123549PubMed CentralView ArticlePubMedGoogle Scholar
- HIV Sequence Compendium 2008 Introduction Various 2008. [http://www.hiv.lanl.gov/content/sequence/HIV/COMPENDIUM/2008/frontmatter.pdf]
- Moore J: Coreceptors: implications for HIV pathogenesis and therapy. Science 1997, 276(5309):51–52. 10.1126/science.276.5309.51View ArticlePubMedGoogle Scholar
- Fu W, Sanders-Beer B, Katz K, Maglott D, KD KP, Ptak R: Human immunodeficiency virus type 1, human protein interaction database at NCBI. Nucleic Acids Res 2009, 37(Database):D417–422. 10.1093/nar/gkn708PubMed CentralView ArticlePubMedGoogle Scholar
- Ptak R, Fu W, Sanders-Beer B, Dickerson J, Pinney J, Robertson D, Rozanov M, Katz K, Maglott D, Pruitt K, et al.: Cataloguing the HIV type 1 human protein interaction network. AIDS Res Hum Retroviruses 2008, 24(12):1497–1502. 10.1089/aid.2008.0113PubMed CentralView ArticlePubMedGoogle Scholar
- Brass A, Dykxhoorn D, Benita Y, Yan N, Engelman A, Xavier R, Lieberman J, Elledge S: Identification of host proteins required for HIV infection through a functional genomic screen. Science 2008, 319(5865):921–926. 10.1126/science.1152725View ArticlePubMedGoogle Scholar
- Konig R, Zhou Y, Elleder D, Diamond T, Bonamy G, Irelan J, Chiang C, Tu B, Jesus PD, Lilley C, et al.: Global analysis of host-pathogen interactions that regulate early-stage HIV-1 replication. Cell 2008, 135: 49–60. 10.1016/j.cell.2008.07.032PubMed CentralView ArticlePubMedGoogle Scholar
- Zhou H, Xu M, Huang Q, Gates A, Zhang X, Castle J, Stec E, Ferrer M, Strulovici B, Hazuda D, et al.: Genome-scale RNAi screen for host factors required for HIV replication. Cell Host Microbe 2008, 4(5):495–504. 10.1016/j.chom.2008.10.004View ArticlePubMedGoogle Scholar
- Pinney J, Dickerson J, Fu W, Sanders-Beer B, Ptak R, Robertson D: HIV-host interactions: a map of viral perturbation of the host system. AIDS 2009.Google Scholar
- Tastan O, Qi Y, Carbonell J, J JKS: Prediction of interactions between HIV-1 and human proteins by information integration. Pac Symp Biocomput, Volume 14 2009, 516–527.Google Scholar
- Balakrishnan S, Tastan O, Carbonell J, Klein-Seetharaman J: Alternative paths in HIV-1 targeted human signal transduction pathways. BMC Genomics 2009, 10(Suppl 3):S30. 10.1186/1471-2164-10-S3-S30PubMed CentralView ArticlePubMedGoogle Scholar
- Lin F, Pan C, Yang J, Chuang T, Chen F: CAPIH: A web interface for comparative analyses and visualization of host-HIV protein-protein interactions. BMC Microbiol 2009, 9: 164. 10.1186/1471-2180-9-164PubMed CentralView ArticlePubMedGoogle Scholar
- McCune J: AIDS RESEARCH: Animal models of HIV-1 disease. Science 1997, 278(5346):2141–2142. 10.1126/science.278.5346.2141View ArticlePubMedGoogle Scholar
- Zink M, Laast V, Helke K, Brice A, Barber S, Clements J, Mankowski J: From mice to macaques–animal models of HIV nervous system disease. Curr HIV Res 2006, 4(3):293–305. 10.2174/157016206777709410View ArticlePubMedGoogle Scholar
- Kelley B, Sharan R, Karp R, Sittler T, Root D, Stockwell B, Ideker T: Conserved pathways within bacteria and yeast as revealed by global protein network alignment. Proc Natl Acad Sci USA 2003, 100(20):11394–11399. 10.1073/pnas.1534710100PubMed CentralView ArticlePubMedGoogle Scholar
- Sharan R, Ideker T: Modeling cellular machinery through biological network comparison. Nat Biotechnol 2006, 24: 427–433. 10.1038/nbt1196View ArticlePubMedGoogle Scholar
- Ideker T, Sharan R: Protein networks in disease. Genome Research 2008, 18: 644–652. 10.1101/gr.071852.107PubMed CentralView ArticlePubMedGoogle Scholar
- Flannick J, Novak A, Srinivasan B, McAdams H, Batzoglou S: Græmlin: general and robust alignment of multiple large interaction networks. Genome Res 2006, 16(9):1169–1181. 10.1101/gr.5235706PubMed CentralView ArticlePubMedGoogle Scholar
- Li Z, Zhang S, Wang Y, Zhang X, Chen L: Alignment of molecular networks by integer quadratic programming. Bioinformatics 2007, 23(13):1631–1639. 10.1093/bioinformatics/btm156View ArticleGoogle Scholar
- Kalaev M, Bafna V, Sharan R: Fast and accurate alignment of multiple protein networks. Proc of the 10th Annu Int Conf Res Comput Mol Bio (RECOMB 2008) 2008.Google Scholar
- Singh R, Xu J, Berger B: Global alignment of multiple protein interaction networks with application to functional orthology detection. Proc Natl Acad Sci USA 2008, 105(35):12763–12768. 10.1073/pnas.0806627105PubMed CentralView ArticlePubMedGoogle Scholar
- Klau G: A new graph-based method for pairwise global network alignment. BMC Bioinformatics 2009, 10(Suppl 1):S59. 10.1186/1471-2105-10-S1-S59PubMed CentralView ArticlePubMedGoogle Scholar
- Zaslavskiy M, Bach F, Vert J: Global alignment of protein-protein interaction networks by graph matching methods. Bioinformatics 2009, 25: i259-i267. 10.1093/bioinformatics/btp196PubMed CentralView ArticlePubMedGoogle Scholar
- Liao C, Lu K, Baym M, Singh R, Berger B: IsoRankN: spectral methods for global alignment of multiple protein networks. Bioinformatics 2009, 25: i253-i258. 10.1093/bioinformatics/btp203PubMed CentralView ArticlePubMedGoogle Scholar
- Tian W, Samatova N: Pairwise alignment of interaction networks by fast identification of maximal conserved patterns. Pac Symp Biocomput, Volume 14 2009, 99–110.Google Scholar
- Steffen M, Petti A, Aach J, D’haeseleer P, Church G: Automated modelling of signal transduction networks. BMC Bioinformatics 2002, 3: 34. 10.1186/1471-2105-3-34PubMed CentralView ArticlePubMedGoogle Scholar
- Koyutürk M, Grama A, Szpankowski W: An efficient algorithm for detecting frequent subgraphs in biological networks. Bioinformatics 2004, 20 Suppl 1: i200-i207. 10.1093/bioinformatics/bth919View ArticlePubMedGoogle Scholar
- Pinter R, Rokhlenko O, Yeger-Lotem E, Ziv-Ukelson M: Alignment of metabolic pathways. Bioinformatics 2005, 21(16):3401–3408. 10.1093/bioinformatics/bti554View ArticlePubMedGoogle Scholar
- Sharan R, Suthram S, Kelley R, Kuhn T, McCuine S, Uetz P, Sittler T, Karp R, Ideker T: Conserved patterns of protein interaction in multiple species. Proc Natl Acad Sci USA 2005, 102(6):1974–1979. 10.1073/pnas.0409522102PubMed CentralView ArticlePubMedGoogle Scholar
- Scott J, Ideker T, Karp R, Sharan R: Efficient algorithms for detecting signaling pathways in protein interaction networks. J Comput Biol 2006, 13: 133–144. 10.1089/cmb.2006.13.133View ArticlePubMedGoogle Scholar
- Shlomi T, Segal D, Ruppin E, Sharan R: QPath: a method for querying pathways in a protein-protein interaction network. BMC Bioinformatics 2006, 7: 199. 10.1186/1471-2105-7-199PubMed CentralView ArticlePubMedGoogle Scholar
- Yang Q, Sze S: Path matching and graph matching in biological networks. J Comput Biol 2007, 14: 56–67. 10.1089/cmb.2006.0076View ArticlePubMedGoogle Scholar
- Qian X, Sze S, Yoon B: Querying pathways in protein interaction networks based on hidden Markov models. J Comput Biol 2009, 16(2):145–157. 10.1089/cmb.2008.02TTPubMed CentralView ArticlePubMedGoogle Scholar
- Qian X, Yoon B: Effective identification of conserved pathways in biological networks using hidden Markov models. PLoS ONE 2009, 4(12):e8070. 10.1371/journal.pone.0008070PubMed CentralView ArticlePubMedGoogle Scholar
- Dyer M, Murali T, Sobral B: The landscape of human proteins interacting with viruses and other pathogens. PLoS Pathog 2008, 4(2):e32. 10.1371/journal.ppat.0040032PubMed CentralView ArticlePubMedGoogle Scholar
- Ashburner M, et al.: Gene ontology: tool for the unification of biology. The Gene ontology consortium. Nat Genet 2000, 25: 25–29. 10.1038/75556PubMed CentralView ArticlePubMedGoogle Scholar
- Boyle E, Weng S, Gollub J, Jin H, Botstein D, Cherry J, Sherlock G: GO::TermFinder–open source software for accessing Gene Ontology information and finding significantly enriched Gene Ontology terms associated with a list of genes. Bioinformatics 2004, 20(18):3710–3715. 10.1093/bioinformatics/bth456PubMed CentralView ArticlePubMedGoogle Scholar
- Monroe J, Silberstein L: HIV-mediated B-lymphocyte activation and lymphomagenesis. J Clin Immunol 1995, 15(2):61–68. 10.1007/BF01541733View ArticlePubMedGoogle Scholar
- UniProt database[http://www.uniprot.org/]
- HIV-1, Human Protein Interaction Database[http://www.ncbi.nlm.nih.gov/RefSeq/HIVInteractions/]
- Blake J, et al.: The Mouse Genome Database (MGD): genetic and genomic information about the laboratory mouse. The Mouse Genome Database Group. Nucleic Acids Research 1999, 27: 95–98. 10.1093/nar/27.1.95PubMed CentralView ArticlePubMedGoogle Scholar
- Bader G, Betel D, Hogue C: BIND: the Biomolecular Interaction Network Database. Nucleic Acids Research 2003, 31: 248–250. 10.1093/nar/gkg056PubMed CentralView ArticlePubMedGoogle Scholar
- Boeckmann B, et al.: The SWISS-PROT protein knowledgebase and its supplement TrEMBL in 2003. Nucleic Acids Research 2003, 31: 365–370. 10.1093/nar/gkg095PubMed CentralView ArticlePubMedGoogle Scholar
- Peri S, et al.: Development of human protein reference database as an initial platform for approaching systems biology in humans. Genome Res 2003, 13(10):2363–2371. 10.1101/gr.1680803PubMed CentralView ArticlePubMedGoogle Scholar
- Salwinski L, et al.: The Database of Interacting Proteins: 2004 update. Nucleic Acids Res 2004, 32(Database issue):D449-D451. 10.1093/nar/gkh086PubMed CentralView ArticlePubMedGoogle Scholar
- Pagel P, et al.: The MIPS mammalian protein-protein interaction database. Bioinformatics 2005, 21(6):832–834. 10.1093/bioinformatics/bti115View ArticlePubMedGoogle Scholar
- Jaeger S, Leser U: High-precision function prediction using conserved interactions. In Proc German Conference on Bioinformatics (GCB 07), Volume 115 Edited by: Falter C, et al. 2007, 146–162.Google Scholar
- Needleman S, Wunsch C: A general method applicable to the search for similarity in the amino acid sequences of two proteins. J Mol Biol 1970, 48: 443–453. 10.1016/0022-2836(70)90057-4View ArticlePubMedGoogle Scholar
- Hermjakob H, et al.: IntAct: an open source molecular interaction database. Nucleic Acids Res 2004, 32(Database issue):D452-D455. 10.1093/nar/gkh052PubMed CentralView ArticlePubMedGoogle Scholar
- O’Brien K, Remm M, Sonnhammer E: Inparanoid: a comprehensive database of eukaryotic orthologs. Nucleic Acids Res 2005, 33(Database issue):D476-D480. 10.1093/nar/gki107PubMed CentralView ArticlePubMedGoogle Scholar
- Durbin R, Eddy S, Krogh A, Mitchison G: Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids. Cambridge University Press, Cambridge, UK; 1998.View ArticleGoogle Scholar
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