Function prediction and analysis of mycobacterium tuberculosis hypothetical proteins

International Journal of Molecular Sciences

Volumn 13, Issue 6, 2012, Pages 7283-7302

  Mazandu, Gaston K ^a   Mulder, Nicola J ^a  

^a UNIVERSITY OF CAPE TOWN   (South Africa)

Author keywords

Function prediction; Genome analysis; Hypothetical protein; Interaction network; Tuberculosis

Indexed keywords

BACTERIAL PROTEIN; GLUTAMIC ACID; PROLINE;

AMINO ACID SEQUENCE; ARTICLE; CONTROLLED STUDY; EVOLUTIONARY ADAPTATION; HOST PATHOGEN INTERACTION; IMMUNE RESPONSE; MYCOBACTERIUM TUBERCULOSIS; NONHUMAN; NUCLEOTIDE SEQUENCE; PREDICTION; PROTEIN ANALYSIS; PROTEIN FUNCTION; PROTEIN PROTEIN INTERACTION; QUANTITATIVE ANALYSIS; STATISTICAL ANALYSIS; BACTERIAL GENOME; BIOLOGICAL MODEL; GENETICS; METABOLISM; MOLECULAR GENETICS; PHYSIOLOGY;

BACTERIAL PROTEINS; GENOME, BACTERIAL; MODELS, BIOLOGICAL; MOLECULAR SEQUENCE ANNOTATION; MYCOBACTERIUM TUBERCULOSIS;

EID: 84862492836     PISSN: None     EISSN: 14220067     Source Type: Journal    
DOI: 10.3390/ijms13067283     Document Type: Article

References (62)

1. Enault, F.; Suhre, K.; Claverie, J.M. Phydbac "Gene Function Predictor": A gene annotation tool based on genomic context analysis. BMC Bioinforma. 2005, 6, doi:10.1186/1471-2105-6-247.
2. Mazandu, G.K.; Mulder, N.J. Scoring protein relationships in functional interaction networks predicted from sequence data. PLoS One 2011, 6, doi:10.1371/journal.pone.0018607.
3. Lord, P.W.; Stevens, P.W.; Brass, A.; Goble, C.A. Investigating semantic similarity measures across the Gene Ontology: The relationship between sequence and annotation. emphBioinformatics 2003, 19, 1275-1283.
4. Gruber, T.R. Toward principles for the design of ontologies used for knowledge sharing. Int. J. Hum.-Comput. Stud. 1995, 43, 907-928.
5. Gruber, T.R. A translation approach to portable ontology specifications. Knowl. Acquis. 1993, 5, 199-220.
6. Stevens, R.; Goble, C.A.; Bechhofer, S. Ontology-based knowledge representation for bioinformatics. Brief. Bioinforma. 2000, 1, 398-414.
7. Ciocoiu, M.; Gruninger, M.; Nau, D. Ontologies for integrating engineering applications. J. Comput. Inf. Sci. Eng. 2001, 1, 45-60.
8. Uschold, M.; Gruninger, M. Ontologies and semantics for seamless connectivity. SIGMOD Rec. 2004, 33, 58-64.
9. Ashburner, M.; Ball, C.A.; Blake, J.A.; Botstein, D.; Butler, H.; Cherry, J.M.; Davis, A.P.; Dolinski, K.; Dwight, S.S.; Eppig, J.T.; et al. Gene Ontology: Tool for the unification of biology. Nat. Genet. 2000, 25, 25-29.
10. GO-Consortium. The Gene Ontology in 2010: Extensions and refinements. Nucleic Acids Res. 2009, 38, D331-D335.
11. Camon, E.; Magrane, M.; Barrell, D.; Binns, D.; Fleischmann, W.; Kersey, P.; Mulder, N.; Oinn, T.; Maslen, J.; Cox, A.; et al. The Gene Ontology Annotation (GOA) project: Implementation of GO in SWISS-PROT, TrEMBL, and InterPro. Genome Res. 2003, 13, 662-672.
12. Camon, E.; Barrell, D.; Lee, V.; Dimmer, E.; Apweiler, R. The Gene Ontology Annotation (GOA) Database-An integrated resource of GO annotations to the UniProt Knowledgebase. Silico Biol. 2004, 4, 5-6.
13. Camon, E.; Magrane, M.; Barrell, D.; Lee, V.; Dimmer, E.; Maslen, J.; Binns, D.; Harte, N.; Lopez, R.; Apweiler, R. The Gene Ontology Annotation (GOA) Database: Sharing knowledge in Uniprot with Gene Ontology. Nucleic Acids Res. 2004, 32, D262-D266.
14. Barrell, D.; Dimmer, E.; Huntley, R.P.; Binns, D.; O'Donovan, C.; Apweiler, R. The GOA database in 2009-An integrated Gene Ontology Annotation resource. Nucleic Acids Res. 2009, 37, D396-D403.
15. Dimmer, E.C.; Huntley, R.P.; Barrell, D.G.; Binns, D.; Draghici, S.; Camon, E.B.; Hubank, M.; Talmud, P.J.; Apweiler, R.; Lovering, R.C. The Gene Ontology-Providing a functional role in proteomic studies. Proteomics 2008, 8(Suppl), 2-11.
16. Camon, E.B.; Barrell, D.G.; Dimmer, E.C.; Lee, V.; Magrane, M.; Maslen, J.; Binns, D.; Apweiler, R. An evaluation of GO annotation retrieval for BioCreAtIve and GOA. BMC Bioinforma. 2005, 6, doi:10.1186/1471-2105-6-S1-S17.
17. Mazandu, G.K.; Mulder, N.J. Using the underlying biological organization of the MTB functional network for protein function prediction. Infect. Genet. Evol. 2011, 12, 922-932.
18. Altschul, S.F.; Gish, W.; Miller, W.; Myers, E.W.; Lipman, D.J. A basic local alignment search tool. J. Mol. Biolol. 1990, 215, 403-410.
19. Altschul, S.F.; Madden, T.L.; Shaffer, A.; Zhang, J.; Zhang, Z.; Miller, W.; Lipman, D.J. Gapped BLAST and PSI-BLAST: A new generation of protein database search programs. Nucleic Acids Res. 1997, 25, 3389-3402.
20. Browne, F.; Zheng, H.; Wang, H.; Azuaje, F. An integrative bayesian approach to supporting the prediction of protein-protein interactions: A case study in human heart failure. World Acad. Sci. Eng. Technol. 2009, 53, 457-463.
21. Persener, J. Bioinformatics and Functional Genomics; John Wiley & Sons: Hoboken, NJ, USA, 2003.
22. Brosch, R.; Gordon, V.; Eiglmeier, K.; Garnier, T.; Tekala, F.; Yeramian, E.; Cole, S.T. Genomics, Biology and Evolution of the Mycobacterium tuberculosis Complex. In Molecular Genetics of Mycobacteria; ASM Press: Washington, DC, USA, 2000; pp. 19-36.
23. Abdallah, A.M.; Verboom, T.; Weerdenburg, E.M.; Gey van Pittius, N.C.; Mahasha, P.W.; Jime ́nez, C.; Parra, M.; Cadieux, N.; Brennan, M.J.; Appelmelk, B.J.; et al. PPE and PE PGRS proteins of Mycobacterium marinum are transported via the type VII secretion system ESX-5. Mol. Microbiol. 2009, 73, 329-340.
24. Delogu, G.; Brennan, M. Comparative immune response to PE and PE PGRS antigens of Mycobacterium tuberculosis. Infect. Immun. 2001, 69, 5606-5611.
25. Brennan, M.J.; Delogu, G.; Chen, Y.; Bardarov, S.; Kriakov, J.; Alavi, M.; Jacobs, W.R, Jr. Evidence that Mycobacterial PE PGRS Proteins are cell surface constituents that influence interactions with other cells. Infect. Immun. 2001, 69, 7326-7333.
26. Banu, S.; Honore, N.; Saint-Joanis, B.; Philpott, D.; Prevost, M.C.; Cole, S.T. Are the PE-PGRS proteins of Mycobacterium tuberculosis variable surface antigens? Mol. Microbiol. 2002, 44, 9-19.
27. Huang, Y.; Wang, Y.; Bai, Y.; Wang, Z.G.; Yang, L.; Zhao, D. Expression of PE PGRS 62 protein in Mycobacterium smegmatis decrease mRNA expression of proinflammatory cytokines IL-1β, IL-6 in macrophages. Mol. Cell Biochem. 2010, 340, 223-229.
28. Mazandu, G.K.; Opap, K.; Mulder, N.J. Contribution of microarray data to the advancement of knowledge on the Mycobacterium tuberculosis interactome: Use of the random partial least squares approach. Infect. Genet. Evol. 2011, 11, 181-189.
29. Mazandu, G.K.; Mulder, N.J. Generation and analysis of large-scale data driven Mycobacterium tuberculosis functional networks for drug target identification. Adv. Bioinforma. 2011, 2011, doi:10.1155/2011/801478.
30. Tsoka, S.; Ouzounis, C.A. Recent developments and future directions in computational genomics. FEBS Lett. 2000, 480, 42-48.
31. Mason, O.; Verwoerd, M. Graph theory and networks in biology. IET Syst. Biol. 2007, 1, 89-119.
32. Gursoy, A.; Keskin, O.; Nussinov, R. Topological properties of protein interaction networks from structural perspective. Biochem. Soc. Trans. 2008, 36, 1398-1403.
33. Sassetti, C.M.; Boyd, D.H.; Rubin, E.J. Genes required for mycobacterial growth defined by high density mutagenesis. Mol. Microbiol. 2003, 48, 77-84.
34. Sassetti, C.M.; Rubin, E.J. Genetic requirements for mycobacterial survival during infection. PNAS 2003, 100, 12989-12994.
35. InterPro Database. Available online: http://www.ebi.ac.uk/interpro (accessed on 20 April 2012).
36. Mazandu, G.K.; Mulder, N.J. A topology-based metric for measuring term similarity in the gene ontology. Adv. Bioinforma. 2012, 2012, doi:10.1155/2012/975783.
37. Brennan, P.J. Structure, function, and biogenesis of the cell wall of Mycobacterium tuberculosis. Tuberculosis 2003, 83, 91-97.
38. Brennan, P.J.; Crick, D.C. The cell-wall core of Mycobacterium tuberculosis in the context of drug discovery. Curr. Top. Med. Chem. 2007, 7, 475-488.
39. Wolfe, L.M.; Mahaffey, S.B.; Kruh, N.A.; Dobos, K.M. Proteomic definition of the cell wall of Mycobacterium tuberculosis. J. Proteome Res. 2010, 9, 5816-5826.
40. Brennan, P.J.; Nikaido, H. The envelope of mycobacteria. Annu. Rev. Biochem. 1995, 64, 29-63.
41. Gu, S.; Chen, J.; Dobos, K.M.; Bradbury, E.M.; Belisle, J.T.; Chen, X. Comprehensive proteomic profiling of the membrane constituents of a Mycobacterium tuberculosis strain. Mol. Cell Proteomics 2003, 2, 1284-1296.
42. Ng, S.K.; Zhang, Z.; Tan, S.H. Integrative approach for computationally inferring protein domain interactions. Bioinformatics 2003, 19, 923-929.
43. Walhout, A.J.; Sordella, R.; Lu, X.; Hartley, J.L.; Temple, G.F.; Brasch, M.A.; Thierry-Mieg, N.; Vidal, M. Protein interaction mapping in C. elegans using proteins involved in vulval development. Science 2000, 287, 116-122.
44. IntAct Database. http://www.ebi.ac.uk/intact/main.xhtml (accessed on 6 October 2011).
45. Aranda, B.; Achuthan, P.; Alam-Faruque, Y.; Armean, I.; Bridge, A.; Derow, C.; Feuermann, M.; Ghanbarian, A.; Kerrien, S.; Khadake, J.; et al. The IntAct molecular interaction database in 2010. Nucleic Acids Res. 2010, 38(Database issue), D525-D531.
46. Kerrien, S.; Aranda, B.; Breuza, L.; Bridge, A.; Broackes-Carter, F.; Chen, C.; Duesbury, M.; Dumousseau, M.; Feuermann, M.; Hinz, U.; et al. The IntAct molecular interaction database in 2012. Nucleic Acids Res. 2012, 40, D841-D846.
47. Integr8 Project. Available online: http://www.ebi.ac.uk/integr8 (accessed on 28 October 2011).
48. Pruess, M.; Kersey, P.; Apweiler, R. The Integr8 project-A resource for genomic and proteomic data. Silico Biol. 2004, 5, 179-185.
49. Ng, S.K.; Zhang, Z.; Tan, S.H.; Lin, K. InterDom: A database of putative interacting protein domains for validating predicted protein interactions and complexes. Nucleic Acids Res. 2003, 31, 251-254.
50. Pagel, P.; Oesterheld, M.; Tovstukhina, O.; Strack, N.; Stümpflen, V.; Frishman, D. DIMA 2.0-predicted and known domain interactions. Nucleic Acids Res. 2008, 36, D651-D655.
51. TubercuList Database. Available online: http://genolist.pasteur.fr/Tuberculist (accessed on 28 October 2011).
52. Swets, J. Measuring the accuracy of diagnostic systems. Science 1988, 240, 1285-1293.
53. Swets, J.; Dawes, R.; Monahan, J. Better decisions through science. Sci. Am. 2000, 283, 82-87.
54. Buckland, M.; Gey, F. The relationship between recall and precision. J. Am. Soc. Inf. Sci. 1994, 45, 12-19.
55. Sing, T.; Sander, O.; Beerenwinkel, N.; Lengauer, T. ROCR: Visualizing classifier performance in R. Bioinformatics 2005, 21, 3940-3941.
56. R Development Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2010.
57. R Development Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2011.
58. Apweiler, R.; Bairoch, A.; Wu, C.H.; Barker, W.C.; Boeckmann, B.; Ferro, S.; Gasteiger, E.; Huang, H.; Lopez, R.; Magrane, M.; et al. UniProt: The Universal Protein knowledgebase. Nucleic Acids Res. 2004, 32, D115-D119.
59. UniProt-Consortium. The Universal Protein Resource (UniProt) in 2010. Nucleic Acids Res. 2010, 38, D142-D148.
60. Jain, E.; Bairoch, A.; Duvaud, S.; Phan, I.; Redaschi, N.; Suzek, B.E.; Martin, M.J.; McGarvey, P.; Gasteiger, E. Infrastructure for the life sciences: Design and implementation of the UniProt website. BMC Bioinforma. 2009, 10, 136.
61. Martin, D.; Brun, C.; Remy, E.; Mouren, P.; Thieffry, D.; Jacq, B. GOToolBox: Functional analysis of gene datasets based on Gene Ontology. Genome Biol. 2004, 5, doi:10.1186/gb-2004-5-12-r101.
62. Li, S.; Chen, H.J. A Note on the determination of sample sizes for hypergeometric distributions. Commun. Stat. Theory Methods 1999, 28, 1749-1757.