Integration of multiple biological features yields high confidence human protein interactome

Journal of Theoretical Biology

Volumn 403, Issue , 2016, Pages 85-96

  Karagoz, Kubra ^a   Sevimoglu, Tuba ^a   Arga, Kazim Yalcin ^a  

^a MARMARA UNIVERSITY   (Turkey)

Author keywords

Cancer subnetworks; Network topology; Protein interaction network; Protein protein interaction

Indexed keywords

BLOOM SYNDROME HELICASE; CYCLIN DEPENDENT KINASE 4; E1A ASSOCIATED P300 PROTEIN; EPIDERMAL GROWTH FACTOR RECEPTOR; FAS ANTIGEN; INSULIN; INSULIN RECEPTOR SUBSTRATE 1; JANUS KINASE 2; MITOGEN ACTIVATED PROTEIN KINASE KINASE; NEUROTROPHIN; NICOTINAMIDE ADENINE DINUCLEOTIDE ADENOSINE DIPHOSPHATE RIBOSYLTRANSFERASE 1; PHOSPHATIDYLINOSITOL 3 KINASE; PROTEIN BCL 2; PROTEIN MLH1; PROTEIN P53; PROTEIN SHC; PROTEIN SHC1; RAD51 PROTEIN; STAT3 PROTEIN; THROMBOSPONDIN 1; TRANSFORMING GROWTH FACTOR BETA RECEPTOR 2; UNCLASSIFIED DRUG; XERODERMA PIGMENTOSUM GROUP D PROTEIN;

CANCER; HOMINID; PROTEIN;

APOPTOSIS; ARTICLE; CONFIDENCE INTERVAL; CONTROLLED STUDY; DNA REPAIR; ENDOCYTOSIS; GENE EXPRESSION; HUMAN; PRIORITY JOURNAL; PROTEIN DOMAIN; PROTEIN EXPRESSION; PROTEIN FUNCTION; PROTEIN LOCALIZATION; PROTEIN PROTEIN INTERACTION; SENSITIVITY AND SPECIFICITY; SIGNAL TRANSDUCTION; INFORMATION PROCESSING; METABOLISM; NEOPLASM; PROCEDURES; PROTEIN ANALYSIS; PROTEIN DATABASE; RECEIVER OPERATING CHARACTERISTIC; REPRODUCIBILITY;

HOMO SAPIENS;

CONFIDENCE INTERVALS; DATA COLLECTION; DATABASES, PROTEIN; HUMANS; NEOPLASMS; PROTEIN INTERACTION MAPPING; PROTEIN INTERACTION MAPS; REPRODUCIBILITY OF RESULTS; ROC CURVE; SIGNAL TRANSDUCTION;

EID: 84968912147     PISSN: 00225193     EISSN: 10958541     Source Type: Journal    
DOI: 10.1016/j.jtbi.2016.05.020     Document Type: Article

References (85)

1. Althaus I.W., Gonzales A.J., Chous J.J., Romero D.L., Deibel M.R., Chou K.C., Kezdy F.J., Resnickll L., Bussoll M.E., SoII A.G., Downeyll K.M., Thomas R.C., Aristoff P.A., Tarpley W.G., Reusser F. The quinoline U-78036 is a potent inhibitor of HIV-1 reverse transcriptase. J. Biol. Chem. 1993, 268:14875-14880.
2. Aragues R., Sander C., Oliva B. Predicting cancer involvement of genes from heterogeneous data. BMC Bioinform. 2008, 9:172.
3. Aranda B., Blankenburg H., Kerrien S., Brinkman F.S.L., Ceol A., Chautard E., Dana J.M., Rivas J.D.L., Dumousseau M., Galeota E., Gaulton A., Goll J., Hancock R.E.W., Isserlin R., Jimenez R.C., Kerssemakers J., Khadake J., Lynn D.J., Michaut M., O'Kelly G., Ono K., Orchard S., Prieto C., Razick S., Rigina O., Salwinski L., Simonovic M., Velankar S., Winter A., Wu G., Bader G.D., Cesareni G., Donaldson I.M., Eisenberg D., Kleywegt G.J., Overington J., Ricard-Blum S., Tyers M., Albrecht M., Hermjakob H. PSICQUIC and PSISCORE: accessing and scoring molecular interactions 2012. Nat. Methods. 2011, 8:528-529.
4. Arga K.Y., Onsan Z.I., Kirdar B., Ulgen K.O., Nielsen J. Understanding signaling in yeast: insights from network analysis. Biotechnol. Bioeng. 2007, 97:1246-1258.
5. Bader G.D., Donaldson I., Wolting C., Ouellette B.F., Pawson T., Hogue C.W. Bind - the biomolecular interaction network database. Nucleic Acids Res. 2001, 29:242-245.
6. Banks C.A., Boanca G., Lee Z.T., Florens L., Washburn M.P. Proteins interacting with cloning scars: a source of false positive protein-protein interactions. Sci. Rep. 2015, 5:8530.
7. Barrett T., Wilhite S.E., Ledoux P., Evangelista C., Kim I.F., Tomashevsky M., Marshall K.A., Phillippy K.H., Sherman P.M., Holko M., Yefanov A., Lee H., Zhang N., Robertson C.L., Serova N., Davis S., Soboleva A. NCBI GEO: archive for functional genomics data sets-update. Nucleic Acids Res. 2013, 41:D991-D995.
8. Becker K.G., Barnes K.C., Bright T.J., Wang S.A. The genetic association database. Nat. Genet. 2004, 36:431-432.
9. Bolstad B.M., Irizarry R.A., Astrand M., Speed T.P. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 2003, 19:185-193.
10. Braun P., Gingras A.C. History of protein-protein interactions: from egg-white to complex networks. Proteomics 2012, 12:1478-1498.
11. Calimlioglu B., Karagoz K., Sevimoglu T., Gov E., Arga K.Y. Tissue-specific molecular biomarker signatures of Type 2 diabetes: an integrative analysis of transcriptomics and protein-protein interaction data. Omics 2015, 19:563-573.
12. Ceol A., Chatr Aryamontri A., Licata L., Peluso D., Briganti L., Perfetto L., Castagnoli L., Cesareni G. Mint, the molecular interaction database: 2009 update. Nucleic Acids Res. 2010, 38:D532-D539.
13. Chatr-Aryamontri A., Breitkreutz B.J., Oughtred R., Boucher L., Heinicke S., Chen D., Stark C., Breitkreutz A., Kolas N., O'Donnell L., Reguly T., Nixon J., Ramage L., Winter A., Sellam A., Chang C., Hirschman J., Theesfeld C., Rust J., Livstone M.S., Dolinski K., Tyers M. The BioGRID interaction database: 2015 update. Nucleic Acids Res. 2015, 43:D470-D478.
14. Chen C., Zhao J.F., Huang Q., Wang R.S., Zhang X.S. Inferring domain-domain interactions from protein-protein interactions. Genome Res. 2002, 12:1540-1548.
15. Chen J., Hsu W., Lee M.L., Ng S.K. Increasing confidence of protein interactomes using network topological metrics. Bioinformatics 2006, 22:1998-2004.
16. Chen C.Y., Ho A., Huang H.Y., Juan H.F., Huang H.C. Dissecting the human protein-protein interaction network via phylogenetic decomposition. Sci. Rep. 2014, 4:7153.
17. Chou K.C. Graphic rules in steady and non-steady enzyme kinetics. J. Biol. Chem. 1989, 264:12074-12079.
18. Chou K.C. Review: applications of graph theory to enzyme kinetics and protein folding kinetics. steady and non-steady state systems. Biophys. Chem. 1990, 35:1-24.
19. Chou K.C. Graphic rule for drug metabolism systems. Curr. Drug Metab. 2010, 11:369-378.
20. Chou K.C. Impacts of bioinformatics to medicinal chemistry. Med. Chem. 2015, 11:218-234.
21. Chou K.C., Cai Y.D. Predicting protein-protein interactions from sequences in a hybridization space. J. Proteome Res. 2006, 5:316-322.
22. Chowdhury S.A., Nibbe R.K., Chance M.R., Koyutürk M. Subnetwork state functions define dysregulated subnetworks in cancer. J. Comput. Biol. 2011, 18:263-281.
23. Chuang H.Y., Lee E., Liu Y.T., Lee D., Ideker T. Network-based classification of breast cancer metastasis. Mol. Syst. Biol. 2007, 3:140.
24. Croft D., O'Kelly G., Wu G., Haw R., Gillespie M., Matthews L., Caudy M., Garapati P., Gopinath G., Jassal B., Jupe S., Kalatskaya I., Mahajan S., May B., Ndegwa N., Schmidt E., Shamovsky V., Yung C., Birney E., Hermjakob H., D'Eustachio P., Stein L. Reactome: a database of reactions, pathways and biological processes. Nucleic Acids Res. 2011, 39:D691-D697.
25. Cusick M.E., Yu H., Smolyar A., Venkatesan K., Carvunis A.R., Simonis N., Rual J.F., Borick H., Braun P., Dreze M., Vandenhaute J., Galli M., Yazaki J., Hill D.E., Ecker J.R., Roth F.P., Vidal M. Literature-curated protein interaction datasets. Nat. Methods. 2009, 6:39-46.
26. Das J., Yu H. Hint: high-quality protein interactomes and their applications in understanding human disease. BMC Syst. Biol. 2012, 6:92.
27. Fang L., Long R., Lan X. iEnhancer-2L: a two-layer predictor for identifying enhancers and their strength by pseudo k-tuple nucleotide composition. Bioinformatics 2016, 32:362-389.
28. Gautier L., Cope L., Bolstad B.M., Irizarry R.A. Affy- analysis of affymetrix genechip data at the probe level. Bioinformatics 2004, 20:307-315.
29. Gentleman R.C., Carey V.J., Bates D.M., Bolstad B., Dettling M., Dudoit S., Ellis B., Gautier L., Ge Y., Gentry J., Hornik K., Hothorn T., Huber W., Lacus S., Irizarry R., Leisch F., Li C., Maechler M., Rossini A.J., Sawitzki G., Smith C., Symth G., Tierney L., Yang J.Y., Zhang J. Bioconductor: open software development for computational biology and bioinformatics. Genome Biol. 2004, 5:R80.
30. Gillis J., Ballouz S., Pavlidis P. Bias tradeoffs in the creation and analysis of protein-protein interaction networks. J. Proteom. 2014, 100:44-54.
31. Goh K.I., Cusick M.E., Valle D., Childs B., Vidal M., Barabasi A.L. The human disease network. Proc. Natl. Acad. Sci. USA 2007, 104:8685-8690.
32. Gonzales A.J., Kezdy F.J., Romero D.L., Aristoff P.A., Tarpley W.G., Reusser Kinetic studies with the nonnucleoside Hiv-1 reverse transcriptase inhibitor u-88204e. Biochemistry 1993, 32:6548-6554.
33. Gulati S., Cheng T.M., Bates P.A. Cancer networks and beyond: interpreting mutations using the human interactome and protein structure. Semin. Cancer Biol. 2013, 23:219-226.
34. Hosur R., Peng J., Vinayagam A., Stelzl U., Xu J., Perrimon N., Bienkowska J., Berger B. A computational framework for boosting confidence in high-throughput protein-protein interaction datasets. Genome Biol. 2012, 13:R76.
35. Hu L., Huang T., Shi X., Lu W.C. Predicting functions of proteins in mouse based on weighted protein-protein interaction network and protein hybrid properties. PLoS One 2011, 6:e14556.
36. Huang T., Chen L. Classification and analysis of regulatory pathways using graph property, biochemical and physicochemical property, and functional property. PLoS One 2011, 6:e25297.
37. Huang da W., Sherman B.T., Lempicki R.A. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 2009, 37:1-13.
38. Ideker T., Sharan R. Protein networks in disease. Genome Res. 2008, 18:644-652.
39. Jaeger S., Aloy P. From protein interaction networks to novel therapeutic strategies. IUBMB Life 2012, 64:529-538.
40. Jansen R., Gerstein M. Analyzing protein function on a genomic scale: the importance of gold-standard positives and negatives for network prediction. Curr. Opin. Microbiol. 2004, 7:535-545.
41. Jia J., Liu Z., Xiao X. iPPI-Esml: an ensemble classifier for identifying the interactions of proteins by incorporating their physicochemical properties and wavelet transforms into PseAAC. J. Theor. Biol. 2015, 377:47-56.
42. Jia J., Liu Z., Xiao X., Liu B. Identification of protein-protein binding sites by incorporating the physicochemical properties and stationary wavelet transforms into pseudo amino acid composition (iPPBS-PseAAC). J. Biomol. Struct. Dyn. 2015, 10.1080/07391102.2015.1095116.
43. Jiang S.P., Liu W.M., Fee C.H. Graph theory of enzyme kinetics: 1. steady-state reaction system. Sci. Sin. 1979, 22:341-358.
44. Jonsson P.F., Bates P.A. Global topological features of cancer proteins in the human interactome. Bioinformatics 2006, 22:2291-2297.
45. Kamburov A., Stelzl U., Herwig R. INTSCORE: a web tool for confidence scoring of biological interactions. Nucleic Acids Res. 2012, 40:W140-W146.
46. Kanehisa M., Goto S., Sato Y., Furumichi M., Tanabe M. Kegg for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res. 2012, 40:D109-D114.
47. Karagoz K., Arga K.Y. Assessment of high-confidence protein-protein interactome in yeast. Comput. Biol. Chem. 2013, 45:1-8.
48. Karagoz K., Sinha R., Arga K.Y. Triple negative breast cancer: a multi-omics network discovery strategy for candidate targets and driving pathways. OMICS 2015, 19:115-130.
49. Karagoz K., Lehman H.L., Stairs D.B., Sinha R., Arga K.Y. Proteomic and metabolic signatures of esophageal squamous cell carcinoma. Curr. Cancer Drug Targets 2016, 16. 10.2174/1568009616666160203113721.
50. Kim Y., Min B., Yi G.S. Iddi: integrated domain-domain interaction and protein interaction analysis system. Proteome Sci. 2012, 10:S9.
51. Li D., Liu W., Liu Z., Wang J., Liu Q., Zhu Y., He F. PRINCESS, a protein interaction confidence evaluation system with multiple data sources. Mol. Cell. Proteom. 2008, 7:1043-1052.
52. Li J., Fine J.P. ROC analysis with multiple tests and multiple classes: methodology and its application in microarray studies. Biostatistics 2008, 9:566-576.
53. Lin N., Wu B., Jansen R., Gerstein M., Zhao H. Information assessment on predicting protein-protein interactions. BMC Bioinform. 2004, 5:154.
54. Lin W.Z., Xiao X. Wenxiang: a web-server for drawing wenxiang diagrams. Nat. Sci. 2011, 3:862-865.
55. Liu Z., Xiao X., Yu D.J., Jia J., Qiu W.R. pRNAm-PC: predicting N-methyladenosine sites in RNA sequences via physical-chemical properties. Anal. Biochem. 2016, 497:60-67.
56. Lopez Y., Nakai K., Patil A. HitPredict version 4: comprehensive reliability scoring of physical protein-protein interactions from more than 100 species. Database 2015, bav117.
57. Memišević V., Wallqvist A., Reifman J. Reconstructing protein interaction networks using parameter-dependent domain-domain interactions. BMC Bioinform. 2013, 14:154.
58. Nguyen T.N., Goodrich J.A. Protein-protein interaction assays: eliminating false positive interactions. Nat. Methods 2006, 3:135-139.
59. Orchard S., Ammari M., Aranda B., Breuza L., Briganti L., Broackes-Carter F., Campbell N.H., Chavali G., Chen C., del-Toro N., Duesbury M., Dumousseau M., Galeota E., Hinz U., Iannuccelli M., Jagannathan S., Jimenez R., Khadake J., Lagreid A., Licata L., Lovering R.C., Meldal B., Melidoni A.N., Milagros M., Peluso D., Perfetto L., Porras P., Raghunath A., Ricard-Blum S., Roechert B., Stutz A., Tognolli M., van Roey K., Cesareni G., Hermjakob H. The mintact project - intact as a common curation platform for 11 molecular interaction databases. Nucleic Acids Res. 2014, 42:D358-D363.
60. Patil A., Nakamura H. Filtering high-throughput protein-protein interaction data using a combination of genomic features. BMC Bioinform. 2005, 6:100.
61. Persico M., Ceol A., Gavrila C., Hoffmann R., Florio A., Cesareni G. Homomint: an inferred human network based on orthology mapping of protein interactions discovered in model organisms. BMC Bioinform. 2005, 6:S21.
62. Prasad K.T.S., Goel R., Kandasamy K., Keerthikumar S., Kumar S., Mathivanan S., Telikicherla D., Raju R., Shafreen B., Venugopal A., Balakrishnan L., Marimuthu A., Banerjee S., Somanathan D.S., Sebastian A., Ran S., Ray S., Kishore H.C.J., Kanth S., Ahmed M., Kashyap M.K., Mohmood R., Ramachandra Y.L., Krishna V., Rahiman B.A., Mohan S., Ranganathan P., Ramabadran S., Chaerkady R., Pandey A. Human Protein Reference Database-2009 Updat. Nucleic Acids Res. 2009, 37:D767-D772.
63. Pujana M.A., Han J.D., Starita L.M., Stevens K.N., Tewari M., Ahn J.S., Rennert G., Moreno V., Kirchhoff T., Gold B., Assmann V., Elshamy W.M., Rual J.F., Levine D., Rozek L.S., Gelman R.S., Gunsalus K.C., Greenberg R.A., Sobhian B., Bertin N., Venkatesan K., Ayivi-Guedehoussou N., Sole X., Hernandez P., Lazaro C., Nathanson K.L., Weber B.L., Cusick M.E., Hill D.E., Offit K., Livingston D.M., Gruber S.B., Parvin J.D., Vidal M. Network modeling links breast cancer susceptibility and centrosome dysfunction. Nat. Genet. 2007, 39:1338-1349.
64. Reguly T., Breitkreutz A., Boucher L., Breitkreutz B.J., Hon G.C., Myers C.L., Parsons A., Friesen H., Oughtred R., Tong A., Stark C., Ho Y., Botstein D., Andrews B., Boone C., Troyanskya O.G., Ideker T., Dolinski K., Batada N.N., Tyers M. Comprehensive curation and analysis of global interaction networks in Saccharomyces cerevisiae. J. Biol. 2006, 5:11.
65. Rolland T., Tasan M., Charloteaux B., Pevzner S.J., Zhong Q., Sahni N., Yi S., Lemmens I., Fontanillo C., Mosca R., Kamburov A., Ghiassian S.D., Yang X., Ghamsari L., Balcha D., Begg B.E., Braun P., Brehme M., Broly M.P., Carvunis A.R., Convery-Zupan D., Corominas R., Coulombe-Huntington J., Dann E., Dreze M., Dricot A., Fan C., Franzosa E., Gebreab F., Gutierrez B.J., Hardy M.F., Jin M., Kang S., Kiros R., Lin G.N., Luck K., MacWilliams A., Menche J., Murray R.R., Palagi A., Poulin M.M., Rambout X., Rasla J., Reichert P., Romero V., Ruyssinck E., Sahalie J.M., Scholz A., Shah A.A., Sharma A., Shen Y., Spirohn K., Tam S., Tejeda A.O., Trigg S.A., Twizere J.C., Vega K., Walsh J., Cusick M.E., Xia Y., Barabasi A.L., Iakoucheva L.M., Aloy P., De Las Rivas J., Tavernier J., Calderwood M.A., Hill D.E., Hao T., Roth F.P., Vidal M. A proteome-scale map of the human interactome network. Cell 2014, 159:1212-1226.
66. Salwinski L., Miller C.S., Smith A.J., Pettit F.K., Bowie J.U., Eisenberg D. The database of interacting proteins: 2004 update. Nucleic Acids Res. 2004, 32:D449-D451.
67. Schaefer M.H., Fontaine J.F., Vinayagam A., Porras P., Wanker E.E., Andrade-Navarro M.A. Hippie: integrating protein interaction networks with experiment based quality scores. PLoS One 2012, 7:e31826.
68. Sevimoglu T., Arga K.Y. The role of protein interaction networks in systems biomedicine. Comput. Struct. Biotechnol. J. 2014, 11:22-27.
69. Sevimoglu T., Arga K.Y. Computational systems biology of psoriasis: are we ready for the age of omics and systems biomarkers?. OMICS 2015, 19:669-687.
70. Sinha I., Karagoz K., Fogle R.L., Hollenbeak C.S., Zea A.H., Arga K.Y., Stanley A.E., Hawkes W.C., Sinha R. "Omics" of selenium biology: a prospective study of plasma proteome network before and after selenized-yeast supplementation in healthy men. OMICS 2016, 20:202-213.
71. Smoot M.E., Ono K., Ruscheinski J., Wang P.L., Ideker T. Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics 2011, 27:431-432.
72. Sun J., Zhao Z. A comparative study of cancer proteins in the human protein-protein interaction network. BMC Genomics 2010, 11:S5.
73. Szklarczyk D., Franceschini A., Wyder S., Forslund K., Heller D., Huerta-Cepas J., Simonovic M., Roth A., Santos A., Tsafou K.P., Kuhn M., Bork P., Jensen L.J., von Mering C. String V10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 2015, 43:D447-D452.
74. UniProt, Consortium Activities at the Universal Protein Resource (Uniprot). Nucleic Acids Res. 2014, 42:D191-D198.
75. Wang E., Lenferink A., O'Connor-McCourt M. Cancer systems biology: exploring cancer-associated genes on cellular networks. Cell. Mol. Life Sci. 2007, 64:1752-1762.
76. Wu Z.C., Xiao X. 2D-MH: a web-server for generating graphic representation of protein sequences based on the physicochemical properties of their constituent amino acids. J. Theor. Biol. 2010, 267:29-34.
77. Wu M., Li X., Chua H.N., Kwoh C.K., Ng S.K. Integrating diverse biological and computational sources for reliable protein-protein interactions. BMC Bioinform. 2010, 11:S8.
78. Wu G., Feng X., Stein L. A human functional protein interaction network and its application to cancer data analysis. Genome Biol. 2010, 11:R53.
79. Xiao X., Min J.L., Lin W.Z., Liu Z. iDrug-target: predicting the interactions between drug compounds and target proteins in cellular networking via the benchmark dataset optimization approach. J. Biomol. Struct. Dyn. 2015, 33:2221-2233.
80. Xiao X., Min J.L., Wang P. iCDI-PseFpt: Identify the channel-drug interaction in cellular networking with PseAAC and molecular fingerprints. J. Theor. Biol. 2013, 337C:71-79.
81. Vazquez A., Rual J.F., Venkatesan K. Quality control methodology for high-throughput protein-protein interaction screening. Methods Mol. Biol. 2011, 781:279-294.
82. Yu G., Li F., Qin Y., Bo X., Wu Y., Wang S. GOSemSim: an R package for measuring semantic similarity among GO terms and gene products. Bioinformatics 2010, 26:976-978.
83. Yu J., Finley R.L. Combining multiple positive training sets to generate confidence scores for protein-protein interactions. Bioinformatics 2009, 25:105-111.
84. Zhou G.P., Deng M.H. An extension of Chou's graphic rules for deriving enzyme kinetic equations to systems involving parallel reaction pathways. Biochem. J. 1984, 222:169-176.
85. Zhou G.P. The disposition of the LZCC protein residues in wenxiang diagram provides new insights into the protein-protein interaction mechanism. J. Theor. Biol. 2011, 284:142-148.