БИОХИМИЯ, 2020, том 85, вып. 1, с. 80–92

УДК 577.1, 57.088

Методы вычислительной интерактомики в вопросах взаимодействия протеоформ человека

Обзор

© 2020 Е.В. Поверенная *, О.И. Киселева, А.С. Иванов, Е.А. Пономаренко

НИИ биомедицинской химии им. В.Н. Ореховича, 119121 Москва, Россия; электронная почта: k.poverennaya@gmail.com

Поступила в редакцию 25.03.2019
После доработки 16.09.2019
Принята к публикации 07.10.2019

DOI: 10.31857/S0320972520010066

КЛЮЧЕВЫЕ СЛОВА: белок-белковые взаимодействия, интерактомика, биоинформатика.

Аннотация

Для человека известно около 20000 белок-кодирующих генов, которые могут быть транслированы в миллионы уникальных видов белков (протеоформ). Протеоформы, кодируемые одним геном, зачастую отличаются по функции, что соответствует различиям в белковых партнерах. Взаимодействуя между собой, протеоформы образуют сеть, отражающую динамически изменяющиеся клеточные процессы в организме. Нарушение белок-белковых взаимодействий (ББВ) вызывает изменение в топологии сети, что зачастую приводит к возникновению патологических процессов. Изучение протеоформ — это относительно новая задача в протеомике, а потому экспериментальных работ по взаимодействию протеоформ немного. Биоинформатические инструменты позволяют решать ряд задач, комплементарно дополняя и обогащая экспериментальные результаты, в частности, расширяя возможности в исследовании взаимодействия протеоформ.

Сноски

* Адресат для корреспонденции.

Финансирование

Работа выполнена при поддержке Российского фонда фундаментальных исследований по теме «Построение интерактомной карты человека» (грант № 18-34-00879).

Конфликт интересов

Авторы заявляют об отсутствии конфликта интересов.

Соблюдение этических норм

Настоящая статья не содержит описания выполненных авторами исследований с участием людей или использованием животных в качестве объектов.

Список литературы

1. Braun, P., and Gingras, A.-C. (2012) History of protein–protein interactions: from egg-white to complex networks, Proteomics, 12, 1478–1498, doi: 10.1002/pmic.201100563.

2. Qi, Y., Bar-Joseph, Z., and Klein-Seetharaman, J. (2006) Evaluation of different biological data and computational classification methods for use in protein interaction prediction, Proteins, 63, 490–500, doi: 10.1002/prot.20865.

3. Snider, J., Kotlyar, M., Saraon, P., Yao, Z., Jurisica, I., and Stagljar, I. (2015) Fundamentals of protein interaction network mapping, Mol. Syst. Biol., 11, 848, doi: 10.15252/msb.20156351.

4. Brückner, A., Polge, C., Lentze, N., Auerbach, D., and Schlattner, U. (2009) Yeast two-hybrid, a powerful tool for systems biology, Int. J. Mol. Sci., 10, 2763–2788, doi: 10.3390/ijms10062763.

5. Hakhverdyan, Z., Domanski, M., Hough, L.E., Oroskar, A.A., Oroskar, A.R., Keegan, S., and LaCava, J. (2015) Rapid, optimized interactomic screening, Nat. Methods, 12, 553–560, doi: 10.1038/nmeth.3395.

6. Fields, S., and Song, O. (1989) A novel genetic system to detect protein–protein interactions, Nature, 340, 245–246, doi: 10.1038/340245a0.

7. Riegel, E., Heimbucher, T., Höfer, T., and Czerny, T. (2017) A sensitive, semi-quantitative mammalian two-hybrid assay, BioTechniques, 62, 206–214, doi: 10.2144/000114544.

8. Gaudinier, A., Tang, M., Bågman, A.-M., and Brady, S.M. (2017) Identification of protein–DNA interactions using enhanced yeast one-hybrid assays and a semiautomated approach, Methods Mol. Biol., 1610, 187–215, doi: 10.1007/978-1-4939-7003-2_13.

9. Glass, F., and Takenaka, M. (2018) The yeast three-hybrid system for protein interactions, Methods Mol. Biol., 1794, 195–205, doi: 10.1007/978-1-4939-7871-7_12.

10. Dunham, W.H., Mullin, M., and Gingras, A.-C. (2012) Affinity-purification coupled to mass spectrometry: basic principles and strategies, Proteomics, 12, 1576–1590, doi: 10.1002/pmic.201100523.

11. Morris, J.H., Knudsen, G.M., Verschueren, E., Johnson, J.R., Cimermancic, P., Greninger, A.L., and Pico, A.R. (2014) Affinity purification–mass spectrometry and network analysis to understand protein–protein interactions, Nat. Prot., 9, 2539–2554, doi: 10.1038/nprot.2014.164.

12. Serebriiskii, I.G., and Golemis, E.A. (2001) Two-hybrid system and false positives. Approaches to detection and elimination, Methods Mol. Biol., 177, 123–134, doi: 10.1385/1-59259-210-4:123.

13. Florinskaya, A., Ershov, P., Mezentsev, Y., Kaluzhskiy, L., Yablokov, E., Medvedev, A., and Ivanov, A. (2018) SPR biosensors in direct molecular fishing: implications for protein interactomics, Sensors, 18, 1616, doi: 10.3390/s18051616.

14. Иванов А.С., Згода В.Г., Арчаков А.И. (2011) Технологии белковой интерактомики, Биоорганическая химия, 37, 8–21, doi: 10.1134/s1068162011010092.

15. Nesvizhskii, A.I. (2012) Computational and informatics strategies for identification of specific protein interaction partners in affinity purification mass spectrometry experiments, Proteomics, 12, 1639–1655, doi: 10.1002/pmic.201100537.

16. Teng, Z., Guo, M., Liu, X., Tian, Z., and Che, K. (2017) Revealing protein functions based on relationships of interacting proteins and GO terms, J. Biomed. Semantics, 8, 27, doi: 10.1186/s13326-017-0139-8.

17. The UniProt Consortium (2008) The Universal Protein Resource (UniProt), Nucleic Acids Res., 36, Suppl. 1, D190–D195, doi: 10.1093/nar/gkm895.

18. Medvedev, A., Kopylov, A., Buneeva, O., Zgoda, V., and Archakov, A. (2012) Affinity-based proteomic profiling: problems and achievements, Proteomics, 12, 621–637, doi: 10.1002/pmic.201100373.

19. Kiseleva, O., Poverennaya, E., Shargunov, A., and Lisitsa, A. (2017) Proteomic cinderella: customized analysis of bulky MS/MS data in one night, J. Bioinform. Comput. Biol., 16, doi: 10.1142/S021972001740011X.

20. Aebersold, R., Agar, J.N., Amster, I.J., Baker, M.S., Bertozzi, C.R., et al. (2018) How many human proteoforms are there? Nat. Chem. Biol., 14, 206–214, doi: 10.1038/nchembio.2576.

21. Ponomarenko, E.A., Poverennaya, E.V., Ilgisonis, E.V., Pyatnitskiy, M.A., Kopylov, A.T., Zgoda, V.G., and Archakov, A.I. (2016) The size of the human proteome: the width and depth, Int. J. Anal. Chem., doi: 10.1155/2016/7436849.

22. Uversky, V.N. (2016) p53 proteoforms and intrinsic disorder: an illustration of the protein structure-function continuum concept, Int. J. Mol. Sci., 17, doi: 10.3390/ijms17111874.

23. Kelemen, O., Convertini, P., Zhang, Z., Wen, Y., Shen, M., Falaleeva, M., and Stamm, S. (2013) Function of alternative splicing, Gene, 514, 1–30, doi: 10.1016/j.gene.2012.07.083.

24. Pyatnitskiy, M., Karpov, D., Poverennaya, E., Lisitsa, A., and Moshkovskii, S. (2015) Bringing down cancer aircraft: searching for essential hypomutated proteins in skin melanoma, PLoS One, 10, e0142819, doi: 10.1371/journal.pone.0142819.

25. Plymire, D.A., Wing, C.E., Robinson, D.E., and Patrie, S.M. (2017) Continuous elution proteoform identification of myelin basic protein by superficially porous reversed-phase liquid chromatography and fourier transform mass spectrometry, Anal. Chem., 89, 12030–12038, doi: 10.1021/acs.analchem.7b02426.

26. Nedelkov, D. (2017) Mass spectrometric studies of apolipoprotein proteoforms and their role in lipid metabolism and type 2 diabetes, Proteomes, 5, 27, doi: 10.3390/proteomes5040027.

27. Lacovich, V., Espindola, S.L., Alloatti, M., Pozo Devoto, V., Cromberg, L.E., Čarná, M.E., Giancarlo, F., Gallo, J.M., Bruno, L., Stokin, J.B., Avale, M.E., and Falzone, T.L. (2017) Tau isoforms imbalance impairs the axonal transport of the amyloid precursor protein in human neurons, J. Neurosci., 37, 58–69, doi: 10.1523/JNEUROSCI.2305-16.2017.

28. Пономаренко Е.А., Поверенная Е.В., Ильгисонис Е.В., Копылов А.Т., Згода В.Г., Лисица А.В., Арчаков А.И. (2017) Перспективы исследования протеома человека, Вестник Российской академии наук, 87, 599–604, doi: 10.1134/S1019331617040049.

29. Киселева О.И., Лисица А.В., Поверенная Е.В. (2018) Протеоформы: методы исследования и клинические перспективы, Молекулярная биология, 52, 394–410, doi: 10.7868/S0026898418030047.

30. Skinner, O.S., Havugimana, P.C., Haverland, N.A., Fornelli, L., Early, B.P., Greer, J.B., and Kelleher, N.L. (2016) An informatic framework for decoding protein complexes by top-down mass spectrometry, Nat. Methods, 13, 237–240, doi: 10.1038/nmeth.3731.

31. Ghadie, M.A., Lambourne, L., Vidal, M., and Xia, Y. (2017) Domain-based prediction of the human isoform interactome provides insights into the functional impact of alternative splicing, PLoS Comput. Biol., 13, e1005717, doi: 10.1371/journal.pcbi.1005717.

32. Hart, G.T., Ramani, A.K., and Marcotte, E.M. (2006) How complete are current yeast and human protein-interaction networks? Genome Biol., 7, 120, doi: 10.1186/gb-2006-7-11-120.

33. Stumpf, M.P.H., Thorne, T., de Silva, E., Stewart, R., An, H.J., Lappe, M., and Wiuf, C. (2008) Estimating the size of the human interactome, Proc. Natl. Acad. Sci. USA, 105, 6959–6964, doi: 10.1073/pnas.0708078105.

34. Kotlyar, M., Pastrello, C., Malik, Z., and Jurisica, I. (2019) IID 2018 update: context-specific physical protein–protein interactions in human, model organisms and domesticated species, Nucleic Acids Res., 47, D581–D589, doi: 10.1093/nar/gky1037.

35. Vidal, M. (2016) How much of the human protein interactome remains to be mapped? Sci. Signal., 9, eg7, doi: 10.1126/scisignal.aaf6030.

36. Wan, C., Borgeson, B., Phanse, S., Tu, F., Drew, K., Clark, G., and Emili, A. (2015) Panorama of ancient metazoan macromolecular complexes, Nature, 525, 339–344, doi: 10.1038/nature14877.

37. Hein, M.Y., Hubner, N.C., Poser, I., Cox, J., Nagaraj, N., Toyoda, Y., and Mann, M. (2015) A human interactome in three quantitative dimensions organized by stoichiometries and abundances, Cell, 163, 712–723, doi: 10.1016/j.cell.2015.09.053.

38. Huttlin, E.L., Bruckner, R.J., Paulo, J.A., Cannon, J.R., Ting, L., Baltier, K., and Harper, J.W. (2017) Architecture of the human interactome defines protein communities and disease networks, Nature, 545, 505–509, doi: 10.1038/nature22366.

39. Luck, K., Sheynkman, G.M., Zhang, I., and Vidal, M. (2017) Proteome-scale human interactomics, Trends Biochem. Sci., 42, 342–354, doi: 10.1016/j.tibs.2017.02.006.

40. Kotlyar, M., Rossos, A.E.M., and Jurisica, I. (2017) Prediction of protein–protein interactions, Curr. Protoc. Bioinformatics, 60, 8.2.1–8.2.14, doi: 10.1002/cpbi.38.

41. Zhang, M., Su, Q., Lu, Y., Zhao, M., and Niu, B. (2017) Application of machine learning approaches for protein–protein interactions prediction, Med. Chem., 13, 506–514, doi: 10.2174/1573406413666170522150940.

42. Horvatovich, P., Lundberg, E.K., Chen, Y.-J., Sung, T.-Y., He, F., et al. (2015) Quest for missing proteins: update 2015 on chromosome-centric human proteome project, J. Proteome Res., 14, 3415–3431, doi: 10.1021/pr5013009.

43. Bradford, J.R., Needham, C.J., Bulpitt, A.J., and Westhead, D.R. (2006) Insights into protein–protein interfaces using a Bayesian network prediction method, J. Mol. Biol., 362, 365–386, doi: 10.1016/j.jmb.2006.07.028.

44. Jansen, R., Yu, H., Greenbaum, D., Kluger, Y., Krogan, N.J., Chung, S., and Gerstein, M. (2003) A Bayesian networks approach for predicting protein–protein interactions from genomic data, Science, 302, 449–53, doi: 10.1126/science.1087361.

45. Scott, M.S., and Barton, G.J. (2007) Probabilistic prediction and ranking of human protein–protein interactions., BMC bioinformatics, 8, 239, doi: 10.1186/1471-2105-8-239.

46. Chatterjee, P., Basu, S., Kundu, M., Nasipuri, M., and Plewczynski, D. (2011) PPI_SVM: prediction of protein–protein interactions using machine learning, domain–domain affinities and frequency tables, Cell. Mol. Biol. Lett., 16, 264–278, doi: 10.2478/s11658-011-0008-x.

47. Guo, Y., Sheng, Q., Li, J., Ye, F., Samuels, D.C., and Shyr, Y. (2013) Large scale comparison of gene expression levels by microarrays and RNAseq using TCGA data, PLoS One, 8, e71462, doi: 10.1371/journal.pone.0071462.

48. Zahiri, J., Bozorgmehr, J., and Masoudi-Nejad, A. (2013) Computational prediction of protein–protein interaction networks: algorithms and resources, Curr. Genomics, 14, 397–414, doi: 10.2174/1389202911314060004.

49. Bartoli, L., Martelli, P.L., Rossi, I., Fariselli, P., and Casadio, R. (2010) The prediction of protein–protein interacting sites in genome-wide protein interaction networks: the test case of the human cell cycle, Curr. Prot. Pept. Sci., 11, 601–608, doi: 10.2174/138920310794109157.

50. McDowall, M.D., Scott, M.S., and Barton, G.J. (2009) PIPs: human protein–protein interaction prediction database, Nucleic Acids Res., 37, D651–D656, doi: 10.1093/nar/gkn870.

51. Garzón, J.I., Deng, L., Murray, D., Shapira, S., Petrey, D., and Honig, B. (2016) A computational interactome and functional annotation for the human proteome, eLife, 5, doi: 10.7554/eLife.18715.

52. Dick, K., and Green, J.R. (2018) Reciprocal perspective for Improved protein–protein interaction prediction, Sci. Rep., 8, 11694, doi: 10.1038/s41598-018-30044-1.

53. Gromiha, M.M., Yugandhar, K., and Jemimah, S. (2017) Protein–protein interactions: scoring schemes and binding affinity, Curr. Opin. Struct. Biol., 44, 31–38, doi: 10.1016/j.sbi.2016.10.016.

54. Gemovic, B., Sumonja, N., Davidovic, R., Perovic, V., and Veljkovic, N. (2018) Mapping of protein–protein interactions: web-based resources for revealing interactomes, Curr. Med. Chem., 26, 3890–3910, doi: 10.2174/0929867325666180214113704.

55. Velankar, S., and Kleywegt, G.J. (2011) The Protein Data Bank in Europe (PDBe): bringing structure to biology, Acta Crystallogr. D, 67, 324–330, doi: 10.1107/S090744491004117X.

56. Aloy, P., and Russell, R.B. (2002) The third dimension for protein interactions and complexes, Trends Biochem. Sci., 27, 633–638.

57. Fang, Y., Sun, M., Dai, G., and Ramain, K. (2016) The intrinsic geometric structure of protein–protein interaction networks for protein interaction prediction, IEEE/ACM Trans. Comput. Biol. Bioinform., 13, 76–85, doi: 10.1109/TCBB.2015.2456876.

58. Tuncbag, N., Keskin, O., Nussinov, R., and Gursoy, A. (2017) Prediction of protein interactions by structural matching: prediction of PPI networks and the effects of mutations on PPIs that combines sequence and structural information, Methods Mol. Biol., 1558, 255–270, doi: 10.1007/978-1-4939-6783-4_12.

59. Su, M.-G., Weng, J.T.-Y., Hsu, J.B.-K., Huang, K.-Y., Chi, Y.-H., and Lee, T.-Y. (2017) Investigation and identification of functional post-translational modification sites associated with drug binding and protein–protein interactions, BMC Systems Biology, 11, 132, doi: 10.1186/s12918-017-0506-1.

60. Keskin, O., Nussinov, R., and Gursoy, A. (2008) PRISM: protein–protein interaction prediction by structural matching, Methods Mol. Biol., 484, 505–521, doi: 10.1007/978-1-59745-398-1_30.

61. Sprinzak, E., and Margalit, H. (2001) Correlated sequence-signatures as markers of protein–protein interaction, J. Mol. Biol., 311, 681–692, doi: 10.1006/jmbi.2001.4920.

62. Deng, M., Mehta, S., Sun, F., and Chen, T. (2002) Inferring domain–domain interactions from protein–protein interactions, Genome Res., 12, 1540–1548, doi: 10.1101/gr.153002.

63. Hayashida, M., Ueda, N., and Akutsu, T. (2004) A simple method for inferring strengths of protein–protein interactions, Genome Inform., 15, 56–68.

64. Raghavachari, B., Tasneem, A., Przytycka, T.M., and Jothi, R. (2008) DOMINE: a database of protein domain interactions, Nucleic Acids Res., 36, D656–D661, doi: 10.1093/nar/gkm761.

65. Tseng, Y.-T., Li, W., Chen, C.-H., Zhang, S., Chen, J.J., Zhou, X., and Liu, C.-C. (2015) IIIDB: a database for isoform-isoform interactions and isoform network modules, BMC Genomics, 16, S10, doi: 10.1186/1471-2164-16-S2-S10.

66. Tay, A.P., Pang, C.N.I., Winter, D.L., and Wilkins, M.R. (2017) PTMOracle: a cytoscape app for covisualizing and coanalyzing post-translational modifications in protein interaction networks, J. Proteome Res., 16, 1988–2003, doi: 10.1021/acs.jproteome.6b01052.

67. Ivanov, A.A., Revennaugh, B., Rusnak, L., Gonzalez-Pecchi, V., Mo, X., Johns, M.A., and Fu, H. (2018) The OncoPPi Portal: an integrative resource to explore and prioritize protein–protein interactions for cancer target discovery, Bioinformatics, 34, 1183–1191, doi: 10.1093/bioinformatics/btx743.

68. Skusa, A., Rüegg, A., and Köhler, J. (2005) Extraction of biological interaction networks from scientific literature, Briefings Bioinform., 6, 263–276.

69. Yu, K., Lung, P.-Y., Zhao, T., Zhao, P., Tseng, Y.-Y., and Zhang, J. (2018) Automatic extraction of protein–protein interactions using grammatical relationship graph, BMC Med. Inform. Decis. Mak., 18, 42, doi: 10.1186/s12911-018-0628-4.

70. Stapley, B.J., and Benoit, G. (2000) Biobibliometrics: information retrieval and visualization from co-occurrences of gene names in Medline abstracts, Pac. Symp. Biocomput., 2000, 529–540.

71. Пономаренко Е.А., Лисица А.В., Ильгисонис Е.В., Арчаков А.И. (2010) Создание семантических сетей белков с использованием Pubmed/Medline, Молекулярная биология, 44, 152–161, doi: 10.1134/S0026893310010176.

72. Lee, J., Kim, S., Lee, S., Lee, K., and Kang, J. (2013) On the efficacy of per-relation basis performance evaluation for PPI extraction and a high-precision rule-based approach, BMC Med. Inform.Decis. Mak., 13 Suppl 1, S7, doi: 10.1186/1472-6947-13-S1-S7.

73. Huang, M., Zhu, X., Hao, Y., Payan, D.G., Qu, K., and Li, M. (2004) Discovering patterns to extract protein–protein interactions from full texts, Bioinformatics, 20, 3604–3612, doi: 10.1093/bioinformatics/bth451.

74. Murugesan, G., Abdulkadhar, S., and Natarajan, J. (2017) Distributed smoothed tree kernel for protein–protein interaction extraction from the biomedical literature, PLoS One, 12, e0187379, doi: 10.1371/journal.pone.0187379.

75. Niu, Y., and Wang, Y. (2015) Protein–protein interaction identification using a hybrid model, Artif. Intell. Med., 64, 185–193, doi: 10.1016/j.artmed.2015.05.003.

76. Chang, J.-W., Zhou, Y.-Q., Ul Qamar, M.T., Chen, L.-L., and Ding, Y.-D. (2016) Prediction of protein–protein interactions by evidence combining methods, Int. J. Mol. Sci., 17, E1946, doi: 10.3390/ijms17111946.

77. Wang, Q., Ross, K.E., Huang, H., Ren, J., Li, G., Vijay-Shanker, K., and Arighi, C.N. (2017) Analysis of protein phosphorylation and its functional impact on protein–protein interactions via text mining of the scientific literature, Methods Mol. Biol., 1558, 213–232, doi: 10.1007/978-1-4939-6783-4_10.

78. Armean, I.M., Lilley, K.S., and Trotter, M.W.B. (2013) Popular computational methods to assess multiprotein complexes derived from label-free affinity purification and mass spectrometry (AP-MS) experiments, Mol. Cell. Proteomics, 12, 1–13, doi: 10.1074/mcp.R112.019554.

79. Fernández, E., Collins, M.O., Uren, R.T., Kopanitsa, M.V., Komiyama, N.H., Croning, M. D.R., and Grant, S.G.N. (2009) Targeted tandem affinity purification of PSD-95 recovers core postsynaptic complexes and schizophrenia susceptibility proteins, Mol. Syst. Biol., 5, 269, doi: 10.1038/msb.2009.27.

80. Choi, H., Larsen, B., Lin, Z.-Y., Breitkreutz, A., Mellacheruvu, D., Fermin, D., and Nesvizhskii, A.I. (2011) SAINT: probabilistic scoring of affinity purification-mass spectrometry data, Nat. Methods, 8, 70–73, doi: 10.1038/nmeth.1541.

81. Sowa, M.E., Bennett, E.J., Gygi, S.P., and Harper, J.W. (2009) Defining the human deubiquitinating enzyme interaction landscape, Cell, 138, 389–403, doi: 10.1016/j.cell.2009.04.042.

82. Skarra, D.V., Goudreault, M., Choi, H., Mullin, M., Nesvizhskii, A.I., Gingras, A.-C., and Honkanen, R.E. (2011) Label-free quantitative proteomics and SAINT analysis enable interactome mapping for the human Ser/Thr protein phosphatase 5, Proteomics, 11, 1508–1516, doi: 10.1002/pmic.201000770.

83. Verschueren, E., Von Dollen, J., Cimermancic, P., Gulbahce, N., Sali, A., and Krogan, N.J. (2015) Scoring large-scale affinity purification mass spectrometry datasets with MiST, Curr. Protoc. Bioinformatics, 49, 8.19.1–8.19.16, doi: 10.1002/0471250953.bi0819s49.

84. Titeca, K., Meysman, P., Gevaert, K., Tavernier, J., Laukens, K., Martens, L., and Eyckerman, S. (2016) SFINX: straightforward filtering index for affinity purification–mass spectrometry data analysis, J. Proteome Res., 15, 332–338, doi: 10.1021/acs.jproteome.5b00666.

85. Mellacheruvu, D., Wright, Z., Couzens, A.L., Lambert, J.-P., St-Denis, N.A., Li, T., and Nesvizhskii, A.I. (2013) The CRAPome: a contaminant repository for affinity purification-mass spectrometry data, Nat. Methods, 10, 730–736, doi: 10.1038/nmeth.2557.

86. Lavallée-Adam, M., Cloutier, P., Coulombe, B., and Blanchette, M. (2011) Modeling contaminants in AP-MS/MS experiments, J. Proteome Res., 10, 886–895, doi: 10.1021/pr100795z.

87. Craig, R., Cortens, J.P., and Beavis, R.C. (2004) Open source system for analyzing, validating, and storing protein identification data, J. Proteome Res., 3, 1234–1242, doi: 10.1021/pr049882h.

88. Zhang, C., Rogalski, J.C., Evans, D.M., Klockenbusch, C., Beavis, R.C., and Kast, J. (2011) In silico protein interaction analysis using the global Proteome Machine Database Research articles, J. Proteome Res., 10, 656–668.

89. Kerrien, S., Aranda, B., Breuza, L., Bridge, A., Broackes-Carter, F., Chen, C., and Hermjakob, H. (2012) The IntAct molecular interaction database in 2012, Nucleic Acids Res., 40, D841–D846, doi: 10.1093/nar/gkr1088.

90. Jones, P., Côté, R.G., Cho, S.Y., Klie, S., Martens, L., Quinn, A.F., and Hermjakob, H. (2008) PRIDE: new developments and new datasets, Nucleic Acids Res., 36, D878–D883, doi: 10.1093/nar/gkm1021.

91. Deutsch, E.W. (2010) The PeptideAtlas Project, Methods Mol. Biol., 604, 285–296, doi: 10.1007/978-1-60761-444-9_19.

92. Veres, D.V., Gyurkó, D.M., Thaler, B., Szalay, K.Z., Fazekas, D., Korcsmáros, T., and Csermely, P. (2015) ComPPI: a cellular compartment-specific database for protein–protein interaction network analysis, Nucleic Acids Res., 43, D485–D493, doi: 10.1093/nar/gku1007.

93. Basha, O., Barshir, R., Sharon, M., Lerman, E., Kirson, B.F., Hekselman, I., and Yeger-Lotem, E. (2017) The TissueNet v.2 database: a quantitative view of protein–protein interactions across human tissues, Nucleic Acids Res., 45, D427–D431, doi: 10.1093/nar/gkw1088.

94. Brown, K.R., and Jurisica, I. (2005) Online Predicted Human Interaction Database, Bioinformatics, 21, 2076–2082, doi: 10.1093/bioinformatics/bti273.

95. Rozenblatt-Rosen, O., Deo, R.C., Padi, M., Adelmant, G., Calderwood, M.A., Rolland, T., and Vidal, M. (2012) Interpreting cancer genomes using systematic host network perturbations by tumour virus proteins, Nature, 487, 491–495, doi: 10.1038/nature11288.

96. Kovács, I.A., Luck, K., Spirohn, K., Wang, Y., Pollis, C., Schlabach, S., and Barabási, A.-L. (2019) Network-based prediction of protein interactions, Nat. Commun., 10, 1240, doi: 10.1038/s41467-019-09177-y.

97. Agapito, G., Guzzi, P.H., and Cannataro, M. (2013) Visualization of protein interaction networks: problems and solutions, BMC Bioinformatics, 14, Suppl. 1, S1, doi: 10.1186/1471-2105-14-S1-S1.

98. Lehner, B., and Fraser, A.G. (2004) A first-draft human protein-interaction map, Genome Biol., 5, R63, doi: 10.1186/gb-2004-5-9-r63.

99. Stelzl, U., Worm, U., Lalowski, M., Haenig, C., Brembeck, F.H., Goehler, H., and Wanker, E.E. (2005) A human protein–protein interaction network: a resource for annotating the proteome, Cell, 122, 957–968, doi: 10.1016/j.cell.2005.08.029.

100. Ewing, R.M., Chu, P., Elisma, F., Li, H., Taylor, P., Climie, S., and Figeys, D. (2007) Large-scale mapping of human protein–protein interactions by mass spectrometry, Mol. Syst. Biol., 3, 89, doi: 10.1038/msb4100134.

101. Taylor, I.W., and Wrana, J.L. (2012) Protein interaction networks in medicine and disease, Proteomics, 12, 1706–1716, doi: 10.1002/pmic.201100594.

102. Li, Q., Chen, W., Song, M., Chen, W., Yang, Z., and Yang, A. (2019) Weighted gene co-expression network analysis and prognostic analysis identifies hub genes and the molecular mechanism related to head and neck squamous cell carcinoma, Cancer Biol. Ther., 20, 750–759, doi: 10.1080/15384047.2018.1564560.

103. Taylor, I.W., Linding, R., Warde-Farley, D., Liu, Y., Pesquita, C., Faria, D., and Wrana, J.L. (2009) Dynamic modularity in protein interaction networks predicts breast cancer outcome, Nat. Biotechnol., 27, 199–204, doi: 10.1038/nbt.1522.

104. Sardiu, M.E., Gilmore, J.M., Groppe, B.D., Dutta, A., Florens, L., and Washburn, M.P. (2019) Topological scoring of protein interaction networks, Nat. Commun., 10, 1118, doi: 10.1038/s41467-019-09123-y.

105. Chen, S.-J., Liao, D.-L., Chen, C.-H., Wang, T.-Y., and Chen, K.-C. (2019) Construction and analysis of protein–protein interaction network of heroin use disorder, Sci. Rep., 9, 4980, doi: 10.1038/s41598-019-41552-z.

106. Ackerman, E.E., Kawakami, E., Katoh, M., Watanabe, T., Watanabe, S., Tomita, Y., and Kawaoka, Y. (2018) Network-guided discovery of influenza virus replication host factors, mBio, 9, doi: 10.1128/mBio.02002-18.

107. Macalino, S.J.Y., Basith, S., Clavio, N.A.B., Chang, H., Kang, S., and Choi, S. (2018) Evolution of in silico strategies for protein–protein interaction drug discovery, Molecules, 23, 1963, doi: 10.3390/molecules23081963.

108. Miho, E., Roškar, R., Greiff, V., and Reddy, S.T. (2019) Large-scale network analysis reveals the sequence space architecture of antibody repertoires, Nat. Commun., 10, 1321, doi: 10.1038/s41467-019-09278-8.

109. Soetkamp, D., Raedschelders, K., Mastali, M., Sobhani, K., Bairey Merz, C.N., and Van Eyk, J. (2017) The continuing evolution of cardiac troponin I biomarker analysis: from protein to proteoform, Expert Rev. Proteomics, 14, 973–986, doi: 10.1080/14789450.2017.1387054.

110. Van der Burgt, Y.E.M., and Cobbaert, C.M. (2018) Proteoform analysis to fulfill unmet clinical needs and reach global standardization of protein measurands in clinical chemistry croteomics, Clin. Lab. Med., 38, 487–497, doi: 10.1016/j.cll.2018.05.001.