Login Register Cart 0
Generic placeholder image

Protein & Peptide Letters

Editor-in-Chief

ISSN (Print): 0929-8665
ISSN (Online): 1875-5305

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Review Article

Analysis of Protein-Protein Interaction Networks through Computational Approaches

Author(s): Ying Han, Liang Cheng * and Weiju Sun

Volume 27, Issue 4, 2020

Page: [265 - 278] Pages: 14

DOI: 10.2174/0929866526666191105142034

Price: $65

Abstract

The interactions among proteins and genes are extremely important for cellular functions. Molecular interactions at protein or gene levels can be used to construct interaction networks in which the interacting species are categorized based on direct interactions or functional similarities. Compared with the limited experimental techniques, various computational tools make it possible to analyze, filter, and combine the interaction data to get comprehensive information about the biological pathways. By the efficient way of integrating experimental findings in discovering PPIs and computational techniques for prediction, the researchers have been able to gain many valuable data on PPIs, including some advanced databases. Moreover, many useful tools and visualization programs enable the researchers to establish, annotate, and analyze biological networks. We here review and list the computational methods, databases, and tools for protein−protein interaction prediction.

Keywords: Protein, interaction, network, computational, prediction, function.

« Previous Next »
Graphical Abstract

[1]
Nelson, D.L.; Lehninger, A.L.; Cox, M.M. Lehninger Principles of Biochemistry; W.H. Freeman: New York, 2008.
[2]
Li, Y.; Niu, M.; Zou, Q. ELM-MHC: An improved MHC identification method with extreme learning machine algorithm. J. Proteome Res., 2019, 18(3), 1392-1401.
[http://dx.doi.org/10.1021/acs.jproteome.9b00012] [PMID: 30698979]
[3]
Cheng, L.; Hu, Y. Human disease system biology. Curr. Gene Ther., 2018, 18(5), 255-256.
[http://dx.doi.org/10.2174/1566523218666181010101114] [PMID: 30306867]
[4]
Valencia, A.; Pazos, F. Computational methods for the prediction of protein interactions. Curr. Opin. Struct. Biol., 2002, 12(3), 368-373.
[http://dx.doi.org/10.1016/S0959-440X(02)00333-0] [PMID: 12127457]
[5]
Tang, H.; Zhao, Y.W.; Zou, P.; Zhang, C.M.; Chen, R.; Huang, P.; Lin, H. HBPred: A tool to identify growth hormone-binding proteins. Int. J. Biol. Sci., 2018, 14(8), 957-964.
[http://dx.doi.org/10.7150/ijbs.24174] [PMID: 29989085]
[6]
Sarkar, S.; Gulati, K.; Kairamkonda, M.; Mishra, A.; Poluri, K.M. Elucidating protein-protein interactions through computational approaches and designing small molecule inhibitors against them for various diseases. Curr. Top. Med. Chem., 2018, 18(20), 1719-1736.
[http://dx.doi.org/10.2174/1568026618666181025114903] [PMID: 30360722]
[7]
Arkin, M.R.; Randal, M.; DeLano, W.L.; Hyde, J.; Luong, T.N.; Oslob, J.D.; Raphael, D.R.; Taylor, L.; Wang, J.; McDowell, R.S.; Wells, J.A.; Braisted, A.C. Binding of small molecules to an adaptive protein-protein interface. Proc. Natl. Acad. Sci. USA, 2003, 100(4), 1603-1608.
[http://dx.doi.org/10.1073/pnas.252756299] [PMID: 12582206]
[8]
Arkin, M.R.; Tang, Y.; Wells, J.A. Small-molecule inhibitors of protein-protein interactions: Progressing toward the reality. Chem. Biol., 2014, 21(9), 1102-1114.
[http://dx.doi.org/10.1016/j.chembiol.2014.09.001] [PMID: 25237857]
[9]
Clackson, T.; Wells, J.A. A hot spot of binding energy in a hormone-receptor interface. Science, 1995, 267(5196), 383-386.
[http://dx.doi.org/10.1126/science.7529940] [PMID: 7529940]
[10]
Amp, J.A.W.; Mcclendon, C.L. Reaching for high-hanging fruit in drug discovery at protein-protein interfaces. Nature, 2007, 450(7172), 1001-1009.
[http://dx.doi.org/10.1038/nature06526] [PMID: 18075579]
[11]
Tuncbag, N.; Gursoy, A.; Guney, E.; Nussinov, R.; Keskin, O. Architectures and functional coverage of protein-protein interfaces. J. Mol. Biol., 2008, 381(3), 785-802.
[http://dx.doi.org/10.1016/j.jmb.2008.04.071] [PMID: 18620705]
[12]
Wei, G.; Xi, W.; Nussinov, R.; Ma, B. Protein ensembles: How does nature harness thermodynamic fluctuations for life? The diverse functional roles of conformational ensembles in the cell. Chem. Rev., 2016, 116(11), 6516-6551.
[http://dx.doi.org/10.1021/acs.chemrev.5b00562] [PMID: 26807783]
[13]
Liu, X.; Quan, Z.; Wu, Y.; Li, D.; Zeng, J. An empirical study of features fusion techniques for protein-protein interaction prediction. Curr. Bioinform., 2016, 11(1), 4-12.
[http://dx.doi.org/10.2174/1574893611666151119221435]
[14]
Papanikolaou, N.; Pavlopoulos, G.A.; Theodosiou, T.; Iliopoulos, I. Protein-protein interaction predictions using text mining methods. Methods, 2015, 74, 47-53.
[http://dx.doi.org/10.1016/j.ymeth.2014.10.026] [PMID: 25448298]
[15]
Cheng, L.; Zhuang, H.; Ju, H.; Yang, S.; Han, J.; Tan, R.; Hu, Y. Exposing the causal effect of body mass index on the risk of type 2 diabetes mellitus: A Mendelian randomization study. Front. Genet., 2019, 10, 94.
[http://dx.doi.org/10.3389/fgene.2019.00094] [PMID: 30891058]
[16]
Cheng, L.; Zhuang, H.; Yang, S.; Jiang, H.; Wang, S.; Zhang, J. Exposing the causal effect of C-Reactive protein on the risk of type 2 diabetes mellitus: A Mendelian randomization study. Front. Genet., 2018, 9, 657.
[http://dx.doi.org/10.3389/fgene.2018.00657] [PMID: 30619477]
[17]
Lalonde, S.; Ehrhardt, D.W.; Loqué, D.; Chen, J.; Rhee, S.Y.; Frommer, W.B. Molecular and cellular approaches for the detection of protein–protein interactions: Latest techniques and current limitations. Plant J., 2008, 53, 610-635.
[http://dx.doi.org/10.1111/j.1365-313X.2007.03332.x]
[18]
Ngounou-Wetie, A.G.; Sokolowska, I.; Woods, A.G.; Roy, U.; Deinhardt, K.; Darie, C.C. Protein-protein interactions: Switch from classical methods to proteomics and bioinformatics-based approaches. Cell. Mol. Life Sci., 2014, 71(2), 205-228.
[http://dx.doi.org/10.1007/s00018-013-1333-1] [PMID: 23579629]
[19]
Piehler, J. New methodologies for measuring protein interactions in vivo and in vitro. Curr. Opin. Struct. Biol., 2005, 15(1), 4-14.
[http://dx.doi.org/10.1016/j.sbi.2005.01.008] [PMID: 15718127]
[20]
Pastrello, C.; Pasini, E.; Kotlyar, M.; Otasek, D.; Wong, S.; Sangrar, W.; Rahmati, S.; Jurisica, I. Integration, visualization and analysis of human interactome. Biochem. Biophys. Res. Commun., 2014, 445(4), 757-773.
[http://dx.doi.org/10.1016/j.bbrc.2014.01.151] [PMID: 24491561]
[21]
Cheng, L.; Sun, J.; Xu, W.; Dong, L.; Hu, Y.; Zhou, M. OAHG: An integrated resource for annotating human genes with multi-level ontologies. Sci. Rep., 2016, 6, 34820.
[http://dx.doi.org/10.1038/srep34820] [PMID: 27703231]
[22]
Cheng, L.; Wang, P.; Tian, R.; Wang, S.; Guo, Q.; Luo, M.; Zhou, W.; Liu, G.; Jiang, H.; Jiang, Q. LncRNA2Target v2.0: A comprehensive database for target genes of lncRNAs in human and mouse. Nucleic Acids Res., 2019, 47(D1), D140-D144.
[http://dx.doi.org/10.1093/nar/gky1051] [PMID: 30380072]
[23]
Giot, L.; Bader, J.S.; Brouwer, C. A protein interaction map of Drosophila melanogaster. Science, 2003, 302(5651), 1727-1736.
[http://dx.doi.org/10.1126/science.1090289]
[24]
Uetz, P.; Giot, L.; Cagney, G.; Mansfield, T.A.; Judson, R.S.; Knight, J.R.; Lockshon, D.; Narayan, V.; Srinivasan, M.; Pochart, P.; Qureshi-Emili, A.; Li, Y.; Godwin, B.; Conover, D.; Kalbfleisch, T.; Vijayadamodar, G.; Yang, M.; Johnston, M.; Fields, S.; Rothberg, J.M. A comprehensive analysis of protein–protein interactions in Saccharomyces cerevisiae. Nature, 2000, 403(6770), 623-627.
[http://dx.doi.org/10.1038/35001009] [PMID: 10688190]
[25]
Huang, X.T.; Zhu, Y.; Chan, L.L.H.; Zhao, Z.; Yan, H. An integrative C. elegans protein-protein interaction network with reliability assessment based on a probabilistic graphical model. Mol. Biosyst., 2016, 12(1), 85-92.
[http://dx.doi.org/10.1039/C5MB00417A] [PMID: 26555698]
[26]
Rao, V.S.; Srinivas, K.; Sujini, G.N.; Kumar, G.N. Protein-protein interaction detection: Methods and analysis. Int. J. Proteomics, 2014, 2014(4) 147648
[http://dx.doi.org/10.1155/2014/147648] [PMID: 24693427]
[27]
Nourani, E.; Khunjush, F.; Durmuş, S. Computational approaches for prediction of pathogen-host protein-protein interactions. Front. Microbiol., 2015, 6(10), 94.
[http://dx.doi.org/10.3389/fmicb.2015.00094] [PMID: 25759684]
[28]
Chang, J.W.; Zhou, Y.Q.; Ul Qamar, M.T.; Chen, L.L.; Ding, Y.D. Prediction of protein-protein interactions by evidence combining methods. Int. J. Mol. Sci., 2016, 17(11), 1946.
[http://dx.doi.org/10.3390/ijms17111946] [PMID: 27879651]
[29]
Cheng, L.; Zhao, H.Q.; Wang, P.P.; Zhou, W.Y.; Luo, M.; Li, T.; Han, J.W.; Liu, S.L.; Jiang, Q.H. Computational methods for identifying similar diseases. Mol. Ther. Nucleic Acids, 2019, 18, 590-604.
[http://dx.doi.org/10.1016/j.omtn.2019.09.019]
[30]
Wei, L.; Zou, Q.; Liao, M.; Lu, H.; Zhao, Y. A novel machine learning method for cytokine-receptor interaction prediction. Comb. Chem. High Throughput Screen., 2016, 19(2), 144-152.
[http://dx.doi.org/10.2174/1386207319666151110122621] [PMID: 26552440]
[31]
Cheng, L.; Jiang, Y.; Ju, H.; Sun, J.; Peng, J.; Zhou, M.; Hu, Y. InfAcrOnt: Calculating cross-ontology term similarities using information flow by a random walk. BMC Genomics, 2018, 19(Suppl. 1), 919.
[http://dx.doi.org/10.1186/s12864-017-4338-6] [PMID: 29363423]
[32]
Shen, J.; Zhang, J.; Luo, X.; Zhu, W.; Yu, K.; Chen, K.; Li, Y.; Jiang, H. Predicting protein-protein interactions based only on sequences information. Proc. Natl. Acad. Sci. USA, 2007, 104(11), 4337-4341.
[http://dx.doi.org/10.1073/pnas.0607879104] [PMID: 17360525]
[33]
Feng, C.Q.; Zhang, Z.Y.; Zhu, X.J. iTerm-PseKNC: A sequence-based tool for predicting bacterial transcriptional terminators. Bioinformatics, 2018, 35(9), 1469-1477.
[http://dx.doi.org/10.1093/bioinformatics/bty827] [PMID: 30247625]
[34]
Lv, H.; Zhang, Z.M.; Li, S.H.; Tan, J.X.; Chen, W.; Lin, H. Evaluation of different computational methods on 5-methylcytosine sites identification. Brief. Bioinform., 2019. [Epub ahead of print]
[http://dx.doi.org/10.1093/bib/bbz048] [PMID: 31157855]
[35]
Keskin, O.; Tuncbag, N.; Gursoy, A. Predicting protein-protein interactions from the molecular to the proteome level. Chem. Rev., 2016, 116(8), 4884-4909.
[http://dx.doi.org/10.1021/acs.chemrev.5b00683] [PMID: 27074302]
[36]
Zhao, T.; Hu, Y.; Zang, T.Y.; Cheng, L. Identifying Alzheimer’s disease-related proteins by LRRGD. BMC Bioinformatics, 2019, 20(Suppl. 18), 570.
[http://dx.doi.org/10.1186/s12859-019-3124-7]
[37]
Sun, J.; Li, Y.; Zhao, Z. Phylogenetic profiles for the prediction of protein-protein interactions: How to select reference organisms? Biochem. Biophys. Res. Commun., 2007, 353(4), 985-991.
[http://dx.doi.org/10.1016/j.bbrc.2006.12.146] [PMID: 17207465]
[38]
Muley, V.Y.; Ranjan, A. Effect of reference genome selection on the performance of computational methods for genome-wide protein-protein interaction prediction. PLoS One, 2012, 7(7) e42057
[http://dx.doi.org/10.1371/journal.pone.0042057] [PMID: 22844541]
[39]
Jothi, R.; Przytycka, T.M.; Aravind, L. Discovering functional linkages and uncharacterized cellular pathways using phylogenetic profile comparisons: A comprehensive assessment. BMC Bioinformatics, 2007, 8(1), 173.
[http://dx.doi.org/10.1186/1471-2105-8-173] [PMID: 17521444]
[40]
Škunca, N.; Dessimoz, C. Phylogenetic profiling: How much input data is enough? PLoS One, 2015, 10(2)e0114701
[http://dx.doi.org/10.1371/journal.pone.0114701] [PMID: 25679783]
[41]
Sato, T; Yamanishi, Y; Kanehisa, M; Horimoto, K; Toh, H Improvement of the mirrortree method by extracting evolutionary information. Avaiable from: Iconceptpress.com
[42]
Sato, T.; Yamanishi, Y.; Horimoto, K.; Toh, H.; Kanehisa, M. Prediction of protein-protein interactions from phylogenetic trees using partial correlation coefficient. Genome, 2003, 14, 496-497.
[43]
Kensche, P.R.; van Noort, V.; Dutilh, B.E.; Huynen, M.A. Practical and theoretical advances in predicting the function of a protein by its phylogenetic distribution. J. R. Soc. Interface, 2008, 5(19), 151-170.
[http://dx.doi.org/10.1098/rsif.2007.1047] [PMID: 17535793]
[44]
Pellegrini, M.; Marcotte, E.M.; Thompson, M.J.; Eisenberg, D.; Yeates, T.O. Assigning protein functions by comparative genome analysis: Protein phylogenetic profiles. Proc. Natl. Acad. Sci. USA, 1999, 96(8), 4285-4288.
[http://dx.doi.org/10.1073/pnas.96.8.4285] [PMID: 10200254]
[45]
Pazos, F.; Valencia, A. Similarity of phylogenetic trees as indicator of protein-protein interaction. Protein Eng., 2001, 14(9), 609-614.
[http://dx.doi.org/10.1093/protein/14.9.609]
[46]
Enright, A.J.; Iliopoulos, I.; Kyrpides, N.C.; Ouzounis, C.A. Protein interaction maps for complete genomes based on gene fusion events. Nature, 1999, 402(6757), 86-90.
[http://dx.doi.org/10.1038/47056] [PMID: 10573422]
[47]
Marcotte, E.M.; Pellegrini, M.; Ng, H.L.; Rice, D.W.; Yeates, T.O.; Eisenberg, D. Detecting protein function and protein-protein interactions from genome sequences. Science, 1999, 285(5428), 751-753.
[http://dx.doi.org/10.1126/science.285.5428.751] [PMID: 10427000]
[48]
Overbeek, R.; Fonstein, M.; D’Souza, M.; Pusch, G.D.; Maltsev, N. The use of gene clusters to infer functional coupling. Proc. Natl. Acad. Sci. USA, 1999, 96(6), 2896-2901.
[http://dx.doi.org/10.1073/pnas.96.6.2896] [PMID: 10077608]
[49]
Yanai, I.; Derti, A.; DeLisi, C. Genes linked by fusion events are generally of the same functional category: A systematic analysis of 30 microbial genomes. Proc. Natl. Acad. Sci. USA, 2001, 98(14), 7940-7945.
[http://dx.doi.org/10.1073/pnas.141236298] [PMID: 11438739]
[50]
Veitia, R.A. Rosetta stone proteins: “Chance and necessity”? Genome Biol., 2002, 3(2), S1001.
[http://dx.doi.org/10.1186/gb-2002-3-2-interactions1001] [PMID: 11864366]
[51]
Huynen, M.; Snel, B.; Lathe, W., III; Bork, P. Predicting protein function by genomic context: Quantitative evaluation and qualitative inferences. Genome Res., 2000, 10(8), 1204-1210.
[http://dx.doi.org/10.1101/gr.10.8.1204] [PMID: 10958638]
[52]
Dandekar, T.; Snel, B.; Huynen, M.; Bork, P. Conservation of gene order: A fingerprint of proteins that physically interact. Trends Biochem. Sci., 1998, 23(9), 324-328.
[http://dx.doi.org/10.1016/S0968-0004(98)01274-2] [PMID: 9787636]
[53]
Szklarczyk, D.; Morris, J.H.; Cook, H.; Kuhn, M.; Wyder, S.; Simonovic, M.; Santos, A.; Doncheva, N.T.; Roth, A.; Bork, P.; Jensen, L.J.; von Mering, C. The STRING database in 2017: Quality-controlled protein-protein association networks, made broadly accessible. Nucleic Acids Res., 2017, 45(D1), D362-D368.
[http://dx.doi.org/10.1093/nar/gkw937] [PMID: 27924014]
[54]
Jansen, R.; Greenbaum, D.; Gerstein, M. Relating whole-genome expression data with protein-protein interactions. Genome Res., 2002, 12(1), 37-46.
[http://dx.doi.org/10.1101/gr.205602] [PMID: 11779829]
[55]
Ge, H.; Liu, Z.; Church, G.M.; Vidal, M. Correlation between transcriptome and interactome mapping data from Saccharomyces cerevisiae. Nat. Genet., 2001, 29(4), 482-486.
[http://dx.doi.org/10.1038/ng776] [PMID: 11694880]
[56]
Thompson, J.N. The coevolutionary process; The University of Chicago Press: Chicago, 1994.
[http://dx.doi.org/10.7208/chicago/9780226797670.001.0001]
[57]
Hakes, L.; Lovell, S.C.; Oliver, S.G.; Robertson, D.L. Specificity in protein interactions and its relationship with sequence diversity and coevolution. Proc. Natl. Acad. Sci. USA, 2007, 104(19), 7999-8004.
[http://dx.doi.org/10.1073/pnas.0609962104] [PMID: 17468399]
[58]
Pazos, F.; Valencia, A. Protein co-evolution, co-adaptation and interactions. EMBO J., 2008, 27(20), 2648-2655.
[http://dx.doi.org/10.1038/emboj.2008.189] [PMID: 18818697]
[59]
Barker, D.; Pagel, M. Predicting functional gene links from phylogenetic-statistical analyses of whole genomes. PLOS Comput. Biol., 2005, 1(1) e3
[http://dx.doi.org/10.1371/journal.pcbi.0010003] [PMID: 16103904]
[60]
Pazos, F.; Valencia, A. In silico two-hybrid system for the selection of physically interacting protein pairs. Proteins, 2002, 47(2), 219-227.
[http://dx.doi.org/10.1002/prot.10074] [PMID: 11933068]
[61]
Weigt, M.; White, R.A.; Szurmant, H.; Hoch, J.A.; Hwa, T. Identification of direct residue contacts in protein-protein interaction by message passing. Proc. Natl. Acad. Sci. USA, 2009, 106(1), 67-72.
[http://dx.doi.org/10.1073/pnas.0805923106] [PMID: 19116270]
[62]
Hopf, T.A.; Schärfe, C.P.; Rodrigues, J.P.; Green, A.G.; Kohlbacher, O.; Sander, C.; Bonvin, A.M.; Marks, D.S. Sequence co-evolution gives 3D contacts and structures of protein complexes. eLife, 2014, 3, 3.
[http://dx.doi.org/10.7554/eLife.03430] [PMID: 25255213]
[63]
Yin, C.; Yau, S.S. A coevolution analysis for identifying protein-protein interactions by Fourier transform. PLoS One, 2017, 12(4) e0174862
[http://dx.doi.org/10.1371/journal.pone.0174862] [PMID: 28430779]
[64]
Peng, X.; Wang, J.; Peng, W.; Wu, F.X.; Pan, Y. Protein-protein interactions: Detection, reliability assessment and applications. Brief. Bioinformatics., 2017, 18(5), 798-819.
[http://dx.doi.org/10.1093/bib/bbw066] [PMID: 27444371]
[65]
Valente, G.T.; Acencio, M.L.; Martins, C.; Lemke, N. The development of a universal in silico predictor of protein-protein interactions. PLoS One, 2013, 8(5)e65587
[http://dx.doi.org/10.1371/journal.pone.0065587] [PMID: 23741499]
[66]
Hamp, T.; Rost, B. Evolutionary profiles improve protein-protein interaction prediction from sequence. Bioinformatics, 2015, 31(12), 1945-1950.
[http://dx.doi.org/10.1093/bioinformatics/btv077] [PMID: 25657331]
[67]
Boolchandani, M.; D’Souza, A.W.; Dantas, G. Sequencing-based methods and resources to study antimicrobial resistance. Nat. Rev. Genet., 2019, 20(6), 356-370.
[http://dx.doi.org/10.1038/s41576-019-0108-4] [PMID: 30886350]
[68]
You, Z.H.; Chan, K.C.; Hu, P. Predicting protein-protein interactions from primary protein sequences using a novel multi-scale local feature representation scheme and the random forest. PLoS One, 2015, 10(5) e0125811
[http://dx.doi.org/10.1371/journal.pone.0125811] [PMID: 25946106]
[69]
Wang, Y.B.; You, Z.H.; Li, X.; Jiang, T.H.; Chen, X.; Zhou, X.; Wang, L. Predicting protein-protein interactions from protein sequences by a stacked sparse autoencoder deep neural network. Mol. Biosyst., 2017, 13(7), 1336-1344.
[http://dx.doi.org/10.1039/C7MB00188F] [PMID: 28604872]
[70]
Garcia-Garcia, J.; Valls-Comamala, V.; Guney, E.; Andreu, D.; Muñoz, F.J.; Fernandez-Fuentes, N.; Oliva, B. iFrag: A protein-protein interface prediction server based on sequence fragments. J. Mol. Biol., 2017, 429(3), 382-389.
[http://dx.doi.org/10.1016/j.jmb.2016.11.034] [PMID: 27956148]
[71]
Sun, X-Y.; Chen, Z-H.; Sun, X-Y.; You, Z-H.; Li, L-P.; Zhao, Y. An Ensemble classifier with random projection for predicting protein–protein interactions using sequence and evolutionary information. Appl. Sci. (Basel), 2018, 8(1), 89.
[http://dx.doi.org/10.3390/app8010089]
[72]
Göktepe, Y.E.; Kodaz, H. Prediction of protein-protein interactions using an effective sequence based combined method. Neurocomputing, 2018, 303, 68-74.
[http://dx.doi.org/10.1016/j.neucom.2018.03.062]
[73]
Li, L.P.; Wang, Y.B.; You, Z.H.; Li, Y.; An, J.Y. PCLPred: A bioinformatics method for predicting protein-protein interactions by combining relevance vector machine model with low-rank matrix approximation. Int. J. Mol. Sci., 2018, 19(4) E1029
[http://dx.doi.org/10.3390/ijms19041029] [PMID: 29596363]
[74]
Zhang, L.; Yu, G.; Xia, D.; Wang, J. Protein–protein interactions prediction based on ensemble deep neural networks. Neurocomputing, 2019, 324, 10-19.
[http://dx.doi.org/10.1016/j.neucom.2018.02.097]
[75]
Romero-Molina, S.; Ruiz-Blanco, Y.B.; Harms, M.; Münch, J.; Sanchez-Garcia, E. PPI-Detect: A support vector machine model for sequence-based prediction of protein-protein interactions. J. Comput. Chem., 2019, 40(11), 1233-1242.
[http://dx.doi.org/10.1002/jcc.25780] [PMID: 30768790]
[76]
Yang, H.; Tang, H.; Chen, X.X.; Zhang, C.J.; Zhu, P.P.; Ding, H.; Chen, W.; Lin, H. Identification of secretory proteins in Mycobacterium tuberculosis using pseudo amino acid composition. BioMed Res. Int., 2016, 2016 5413903
[http://dx.doi.org/10.1155/2016/5413903] [PMID: 27597968]
[77]
Chen, X.X.; Tang, H.; Li, W.C.; Wu, H.; Chen, W.; Ding, H.; Lin, H. Identification of bacterial cell wall lyases via pseudo amino acid composition. BioMed Res. Int., 2016, 2016 1654623
[http://dx.doi.org/10.1155/2016/1654623] [PMID: 27437396]
[78]
Koch, C.P.; Pillong, M.; Hiss, J.A.; Schneider, G. Computational resources for MHC ligand identification. Mol. Inform., 2013, 32(4), 326-336.
[http://dx.doi.org/10.1002/minf.201300042] [PMID: 27481589]
[79]
Wang, S.; Bai, Z.; Han, J.; Tian, Y.; Shang, X.; Wang, L.; Li, J.; Wu, Y. Improving the prediction of HLA class I-binding peptides using a supertype-based method. J. Immunol. Methods, 2014, 405, 109-120.
[80]
Xue, L.C.; Dobbs, D.; Bonvin, A.M.J.J.; Honavar, V. Computational prediction of protein interfaces: A review of data driven methods. FEBS Lett., 2015, 589(23), 3516-3526.
[http://dx.doi.org/10.1016/j.febslet.2015.10.003] [PMID: 26460190]
[81]
Vakser, I.A. Protein-protein docking: From interaction to interactome. Biophys. J., 2014, 107(8), 1785-1793.
[http://dx.doi.org/10.1016/j.bpj.2014.08.033] [PMID: 25418159]
[82]
Vreven, T.; Moal, I.H.; Vangone, A.; Pierce, B.G.; Kastritis, P.L.; Torchala, M.; Chaleil, R.; Jiménez-García, B.; Bates, P.A.; Fernandez-Recio, J.; Bonvin, A.M.; Weng, Z. Updates to the integrated protein-protein interaction benchmarks: Docking Benchmark Version 5 and Affinity Benchmark Version 2. J. Mol. Biol., 2015, 427(19), 3031-3041.
[http://dx.doi.org/10.1016/j.jmb.2015.07.016] [PMID: 26231283]
[83]
Tuncbag, N.; Gursoy, A.; Keskin, O. Prediction of protein-protein interactions: Unifying evolution and structure at protein interfaces. Phys. Biol., 2011, 8(3) 035006
[http://dx.doi.org/10.1088/1478-3975/8/3/035006] [PMID: 21572173]
[84]
Kundrotas, P.J.; Zhu, Z.; Janin, J.; Vakser, I.A. Templates are available to model nearly all complexes of structurally characterized proteins. Proc. Natl. Acad. Sci. USA, 2012, 109(24), 9438-9441.
[http://dx.doi.org/10.1073/pnas.1200678109] [PMID: 22645367]
[85]
Wang, Z. Knowledge-based machine learning methods for macromolecular 3D structure prediction. arXiv, 1609.05061.
[86]
Carpentier, M.; Chomilier, J. Protein multiple alignments: Sequence-based versus structure-based programs. Bioinformatics, 2019, 35(20), 3970-3980.
[http://dx.doi.org/10.1093/bioinformatics/btz236] [PMID: 30942864]
[87]
Jurisica, I. Functional topology in a network of protein interactions. Bioinformatics, 2004, 20(3), 340-348.
[http://dx.doi.org/10.1093/bioinformatics/btg415] [PMID: 14960460]
[88]
Chua, H.N.; Sung, W.K.; Wong, L. Exploiting indirect neighbours and topological weight to predict protein function from protein-protein interactions. Bioinformatics, 2006, 22(13), 1623-1630.
[http://dx.doi.org/10.1093/bioinformatics/btl145] [PMID: 16632496]
[89]
Goldberg, D.S.; Roth, F.P. Assessing experimentally derived interactions in a small world. Proc. Natl. Acad. Sci. USA, 2003, 100(8), 4372-4376.
[90]
Phan, H.T.; Sternberg, M.J. PINALOG: A novel approach to align protein interaction networks--implications for complex detection and function prediction. Bioinformatics, 2012, 28(9), 1239-1245.
[http://dx.doi.org/10.1093/bioinformatics/bts119] [PMID: 22419782]
[91]
Lehne, B.; Schlitt, T. Protein-protein interaction databases: Keeping up with growing interactomes. Hum. Genomics, 2009, 3(3), 291-297.
[PMID: 19403463]
[92]
Cheng, L.; Hu, Y.; Sun, J.; Zhou, M.; Jiang, Q. DincRNA: A comprehensive web-based bioinformatics toolkit for exploring disease associations and ncRNA function. Bioinformatics, 2018, 34(11), 1953-1956.
[http://dx.doi.org/10.1093/bioinformatics/bty002] [PMID: 29365045]
[93]
Cheng, L.; Yang, H.; Zhao, H.; Pei, X.; Shi, H.; Sun, J.; Zhang, Y.; Wang, Z.; Zhou, M. MetSigDis: A manually curated resource for the metabolic signatures of diseases. Brief. Bioinform., 2019, 20(1), 203-209.
[http://dx.doi.org/10.1093/bib/bbx103] [PMID: 28968812]
[94]
Mayer, G.; Montecchi-Palazzi, L.; Ovelleiro, D.; Jones, A.R.; Binz, P.A.; Deutsch, E.W.; Chambers, M.; Kallhardt, M.; Levander, F.; Shofstahl, J.; Orchard, S.; Vizcaíno, J.A.; Hermjakob, H.; Stephan, C.; Meyer, H.E.; Eisenacher, M. The HUPO proteomics standards initiative- mass spectrometry controlled vocabulary. Database (Oxford), 2013, 2013 bat009
[http://dx.doi.org/10.1093/database/bat009] [PMID: 23482073]
[95]
Orchard, S.; Kerrien, S.; Abbani, S.; Aranda, B.; Bhate, J.; Bidwell, S.; Bridge, A.; Briganti, L.; Brinkman, F.S.; Cesareni, G.; Chatr-aryamontri, A.; Chautard, E.; Chen, C.; Dumousseau, M.; Goll, J.; Hancock, R.E.; Hannick, L.I.; Jurisica, I.; Khadake, J.; Lynn, D.J.; Mahadevan, U.; Perfetto, L.; Raghunath, A.; Ricard-Blum, S.; Roechert, B.; Salwinski, L.; Stümpflen, V.; Tyers, M.; Uetz, P.; Xenarios, I.; Hermjakob, H. Protein interaction data curation: The International Molecular Exchange (IMEx) consortium. Nat. Methods, 2012, 9(4), 345-350.
[http://dx.doi.org/10.1038/nmeth.1931] [PMID: 22453911]
[96]
Oughtred, R.; Stark, C.; Breitkreutz, B.J.; Rust, J.; Boucher, L.; Chang, C.; Kolas, N.; O’Donnell, L.; Leung, G.; McAdam, R.; Zhang, F.; Dolma, S.; Willems, A.; Coulombe-Huntington, J.; Chatr-Aryamontri, A.; Dolinski, K.; Tyers, M. The BioGRID interaction database: 2019 update. Nucleic Acids Res., 2019, 47(D1), D529-D541.
[http://dx.doi.org/10.1093/nar/gky1079] [PMID: 30476227]
[97]
Keshava-Prasad, 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.; Rani, S.; Ray, S.; Harrys Kishore, 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 update. Nucleic Acids Res., 2009, 37(Database issue), D767-D772.
[http://dx.doi.org/10.1093/nar/gkn892] [PMID: 18988627]
[98]
Turner, B.; Razick, S.; Turinsky, A.L.; Vlasblom, J.; Crowdy, E.K.; Cho, E.; Morrison, K.; Donaldson, I.M.; Wodak, S.J. iRefWeb: Interactive analysis of consolidated protein interaction data and their supporting evidence. Database (Oxford), 2010, 2010 baq023
[http://dx.doi.org/10.1093/database/baq023] [PMID: 20940177]
[99]
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(Database issue), D447-D452.
[http://dx.doi.org/10.1093/nar/gku1003] [PMID: 25352553]
[100]
Kalathur, R.K.; Pinto, J.P.; Hernández-Prieto, M.A.; Machado, R.S.; Almeida, D.; Chaurasia, G.; Futschik, M.E. UniHI 7: An enhanced database for retrieval and interactive analysis of human molecular interaction networks. Nucleic Acids Res., 2014, 42(Database issue), D408-D414.
[http://dx.doi.org/10.1093/nar/gkt1100] [PMID: 24214987]
[101]
Martens, L.; Chambers, M.; Sturm, M.; Kessner, D.; Levander, F.; Shofstahl, J.; Tang, WH.; Römpp, A.; Neumann, S.; Pizarro, A.D.; Montecchi-Palazzi, L.; Tasman, N.; Coleman, M.; Reisinger, F.; Souda, P.; Hermjakob, H.; Binz, PA.; Deutsch, E.W. mzML--a community standard for mass spectrometry data. Mol. Cell. Proteomics, 2011, 10(1) R110.000133.
[102]
Deutsch, E.W.; Chambers, M.; Neumann, S. TraML--a standard format for exchange of selected reaction monitoring transition lists. Mol. Cell. Proteomics, 2012, 11(4) R111.015040.
[103]
Holman, S.W.; Sims, P.F.; Eyers, C.E. The use of selected reaction monitoring in quantitative proteomics. Bioanalysis, 2012, 4(14), 1763-1786.
[http://dx.doi.org/10.4155/bio.12.126] [PMID: 22877222]
[104]
Griss, J.; Jones, A.R.; Sachsenberg, T.; Walzer, M.; Gatto, L.; Hartler, J.; Thallinger, G.G.; Salek, R.M.; Steinbeck, C.; Neuhauser, N.; Cox, J.; Neumann, S.; Fan, J.; Reisinger, F.; Xu, Q.W.; Del Toro, N.; Pérez-Riverol, Y.; Ghali, F.; Bandeira, N.; Xenarios, I.; Kohlbacher, O.; Vizcaíno, J.A.; Hermjakob, H. The mzTab data exchange format: Communicating mass-spectrometry-based proteomics and metabolomics experimental results to a wider audience. Mol. Cell. Proteomics, 2014, 13(10), 2765-2775.
[http://dx.doi.org/10.1074/mcp.O113.036681] [PMID: 24980485]
[105]
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(Database issue), D358-D363.
[http://dx.doi.org/10.1093/nar/gkt1115] [PMID: 24234451]
[106]
Licata, L.; Briganti, L.; Peluso, D.; Perfetto, L.; Iannuccelli, M.; Galeota, E.; Sacco, F.; Palma, A.; Nardozza, A.P.; Santonico, E.; Castagnoli, L.; Cesareni, G. MINT, the molecular interaction database: 2012 update. Nucleic Acids Res., 2012, 40(Database issue), D857-D861.
[http://dx.doi.org/10.1093/nar/gkr930] [PMID: 22096227]
[107]
Hermjakob, H.; Montecchi-Palazzi, L.; Bader, G.; Wojcik, J.; Salwinski, L.; Ceol, A.; Moore, S.; Orchard, S.; Sarkans, U.; von Mering, C.; Roechert, B.; Poux, S.; Jung, E.; Mersch, H.; Kersey, P.; Lappe, M.; Li, Y.; Zeng, R.; Rana, D.; Nikolski, M.; Husi, H.; Brun, C.; Shanker, K.; Grant, S.G.; Sander, C.; Bork, P.; Zhu, W.; Pandey, A.; Brazma, A.; Jacq, B.; Vidal, M.; Sherman, D.; Legrain, P.; Cesareni, G.; Xenarios, I.; Eisenberg, D.; Steipe, B.; Hogue, C.; Apweiler, R. The HUPO PSI’s molecular interaction format--a community standard for the representation of protein interaction data. Nat. Biotechnol., 2004, 22(2), 177-183.
[http://dx.doi.org/10.1038/nbt926] [PMID: 14755292]
[108]
Qi, D.; Zhang, H.; Fan, J.; Perkins, S.; Pisconti, A.; Simpson, D.M.; Bessant, C.; Hubbard, S.; Jones, A.R. The mzqLibrary--An open source Java library supporting the HUPO-PSI quantitative proteomics standard. Proteomics, 2015, 15(18), 3152-3162.
[http://dx.doi.org/10.1002/pmic.201400535] [PMID: 26037908]
[109]
Gandhi, T.K.; Zhong, J.; Mathivanan, S.; Karthick, L.; Chandrika, K.N.; Mohan, S.S.; Sharma, S.; Pinkert, S.; Nagaraju, S.; Periaswamy, B.; Mishra, G.; Nandakumar, K.; Shen, B.; Deshpande, N.; Nayak, R.; Sarker, M.; Boeke, J.D.; Parmigiani, G.; Schultz, J.; Bader, J.S.; Pandey, A. Analysis of the human protein interactome and comparison with yeast, worm and fly interaction datasets. Nat. Genet., 2006, 38(3), 285-293.
[http://dx.doi.org/10.1038/ng1747] [PMID: 16501559]
[110]
Wan, S.; Duan, Y.; Zou, Q. HPSLPred: An ensemble multi-label classifier for human protein subcellular location prediction with imbalanced source. Proteomics, 2017, 17(17-18) 1700262
[http://dx.doi.org/10.1002/pmic.201700262] [PMID: 28776938]
[111]
Mathivanan, S.; Ahmed, M.; Ahn, N.G.; Alexandre, H.; Amanchy, R.; Andrews, P.C.; Bader, J.S.; Balgley, B.M.; Bantscheff, M.; Bennett, K.L.; Björling, E.; Blagoev, B.; Bose, R.; Brahmachari, S.K.; Burlingame, A.S.; Bustelo, X.R.; Cagney, G.; Cantin, G.T.; Cardasis, H.L.; Celis, J.E.; Chaerkady, R.; Chu, F.; Cole, P.A.; Costello, C.E.; Cotter, R.J.; Crockett, D.; DeLany, J.P.; De Marzo, A.M.; DeSouza, L.V.; Deutsch, E.W.; Dransfield, E.; Drewes, G.; Droit, A.; Dunn, M.J.; Elenitoba-Johnson, K.; Ewing, R.M.; Van Eyk, J.; Faca, V.; Falkner, J.; Fang, X.; Fenselau, C.; Figeys, D.; Gagné, P.; Gelfi, C.; Gevaert, K.; Gimble, J.M.; Gnad, F.; Goel, R.; Gromov, P.; Hanash, S.M.; Hancock, W.S.; Harsha, H.C.; Hart, G.; Hays, F.; He, F.; Hebbar, P.; Helsens, K.; Hermeking, H.; Hide, W.; Hjernø, K.; Hochstrasser, D.F.; Hofmann, O.; Horn, D.M.; Hruban, R.H.; Ibarrola, N.; James, P.; Jensen, O.N.; Jensen, P.H.; Jung, P.; Kandasamy, K.; Kheterpal, I.; Kikuno, R.F.; Korf, U.; Körner, R.; Kuster, B.; Kwon, M.S.; Lee, H.J.; Lee, Y.J.; Lefevre, M.; Lehvaslaiho, M.; Lescuyer, P.; Levander, F.; Lim, M.S.; Löbke, C.; Loo, J.A.; Mann, M.; Martens, L.; Martinez-Heredia, J.; McComb, M.; McRedmond, J.; Mehrle, A.; Menon, R.; Miller, C.A.; Mischak, H.; Mohan, S.S.; Mohmood, R.; Molina, H.; Moran, M.F.; Morgan, J.D.; Moritz, R.; Morzel, M.; Muddiman, D.C.; Nalli, A.; Navarro, J.D.; Neubert, T.A.; Ohara, O.; Oliva, R.; Omenn, G.S.; Oyama, M.; Paik, Y.K.; Pennington, K.; Pepperkok, R.; Periaswamy, B.; Petricoin, E.F.; Poirier, G.G.; Prasad, T.S.; Purvine, S.O.; Rahiman, B.A.; Ramachandran, P.; Ramachandra, Y.L.; Rice, R.H.; Rick, J.; Ronnholm, R.H.; Salonen, J.; Sanchez, J.C.; Sayd, T.; Seshi, B.; Shankari, K.; Sheng, S.J.; Shetty, V.; Shivakumar, K.; Simpson, R.J.; Sirdeshmukh, R.; Siu, K.W.; Smith, J.C.; Smith, R.D.; States, D.J.; Sugano, S.; Sullivan, M.; Superti-Furga, G.; Takatalo, M.; Thongboonkerd, V.; Trinidad, J.C.; Uhlen, M.; Vandekerckhove, J.; Vasilescu, J.; Veenstra, T.D.; Vidal-Taboada, J.M.; Vihinen, M.; Wait, R.; Wang, X.; Wiemann, S.; Wu, B.; Xu, T.; Yates, J.R.; Zhong, J.; Zhou, M.; Zhu, Y.; Zurbig, P.; Pandey, A. Human Proteinpedia enables sharing of human protein data. Nat. Biotechnol., 2008, 26(2), 164-167.
[http://dx.doi.org/10.1038/nbt0208-164] [PMID: 18259167]
[112]
Tarcea, V.G.; Weymouth, T.; Ade, A.; Bookvich, A.; Gao, J.; Mahavisno, V.; Wright, Z.; Chapman, A.; Jayapandian, M.; Ozgür, A.; Tian, Y.; Cavalcoli, J.; Mirel, B.; Patel, J.; Radev, D.; Athey, B.; States, D.; Jagadish, H.V. Michigan molecular interactions r2: From interacting proteins to pathways. Nucleic Acids Res., 2009, 37(Database issue), D642-D646.
[http://dx.doi.org/10.1093/nar/gkn722] [PMID: 18978014]
[113]
Isserlin, R.; El-Badrawi, R.A.; Bader, G.D. The biomolecular interaction network database in PSI-MI 2.5. Database (Oxford), 2011, 2011 baq037
[http://dx.doi.org/10.1093/database/baq037] [PMID: 21233089]
[114]
Kamburov, A.; Pentchev, K.; Galicka, H.; Wierling, C.; Lehrach, H.; Herwig, R. ConsensusPathDB: Toward a more complete picture of cell biology. Nucleic Acids Res., 2011, 39(Database issue), D712-D717.
[http://dx.doi.org/10.1093/nar/gkq1156] [PMID: 21071422]
[115]
López, Y.; Nakai, K.; Patil, A. HitPredict version 4: Comprehensive reliability scoring of physical protein-protein interactions from more than 100 species. Database (Oxford), 2015, 2015 pii bav117
[http://dx.doi.org/10.1093/database/bav117] [PMID: 26708988]
[116]
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(Database issue), D449-D451.
[http://dx.doi.org/10.1093/nar/gkh086] [PMID: 14681454]
[117]
Giurgiu, M.; Reinhard, J.; Brauner, B.; Dunger-Kaltenbach, I.; Fobo, G.; Frishman, G.; Montrone, C.; Ruepp, A. CORUM: The comprehensive resource of mammalian protein complexes-2019. Nucleic Acids Res., 2019, 47(D1), D559-D563.
[http://dx.doi.org/10.1093/nar/gky973] [PMID: 30357367]
[118]
Iragne, F.; Nikolski, M.; Mathieu, B.; Auber, D.; Sherman, D. ProViz: Protein interaction visualization and exploration. Bioinformatics, 2005, 21(2), 272-274.
[http://dx.doi.org/10.1093/bioinformatics/bth494] [PMID: 15347570]
[119]
Wright, D.W.; Angus, T.; Enright, A.J.; Freeman, T.C. Visualisation of BioPAX networks using biolayout express (3D). F1000 Res., 2014, 3, 246.
[http://dx.doi.org/10.12688/f1000research.5499.1] [PMID: 25949802]
[120]
Longabaugh, W.J. Combing the hairball with BioFabric: A new approach for visualization of large networks. BMC Bioinformatics, 2012, 13, 275.
[http://dx.doi.org/10.1186/1471-2105-13-275] [PMID: 23102059]
[121]
Taubert, J.; Hassani-Pak, K.; Castells-Brooke, N.; Rawlings, C.J. Ondex Web: Web-based visualization and exploration of heterogeneous biological networks. Bioinform, 2014, 30(7), 1034-1035.
[http://dx.doi.org/10.1093/bioinformatics/btt740] [PMID: 24363379]
[122]
Jang, Y.; Yu, N.; Seo, J.; Kim, S.; Lee, S. MONGKIE: An integrated tool for network analysis and visualization for multi-omics data. Biol. Direct, 2016, 11(1), 10.
[http://dx.doi.org/10.1186/s13062-016-0112-y] [PMID: 26987515]
[123]
Zhou, G.; Xia, J. OmicsNet: A web-based tool for creation and visual analysis of biological networks in 3D space. Nucleic Acids Res., 2018, 46(W1), W514-W522.
[http://dx.doi.org/10.1093/nar/gky510] [PMID: 29878180]
[124]
Kohl, M.; Wiese, S.; Warscheid, B. Cytoscape: Software for visualization and analysis of biological networks. Methods Mol. Biol., 2011, 696(696), 291-303.
[http://dx.doi.org/10.1007/978-1-60761-987-1_18] [PMID: 21063955]
[125]
Grandjean, M. GEPHI: Introduction to Network Analysis and Visualisation. H-HistBibl. Available from: http://www.martingrandjean.ch/gephi-introduction/ (Accessed on: 08-15-2016).
[126]
Mrvar, A.; Batagelj, V. Analysis and visualization of large networks with program package Pajek. Complex Adaptive Systems Modeling, 2016, 4(1), 6.
[http://dx.doi.org/10.1186/s40294-016-0017-8]

Rights & Permissions Print Cite
Article Metrics
31
2
© 2024 Bentham Science Publishers | Privacy Policy