[en] Proteins effect a number of biological functions, from cellular signaling, organization, mobility, and transport to catalyzing biochemical reactions and coordinating an immune response. These varied functions are often dependent upon macromolecular interactions, particularly with other proteins. Small-scale studies in the scientific literature report protein–protein interactions (PPIs), but slowly and with bias towards well-studied proteins. In an era where genomic sequence is readily available, deducing genotype–phenotype relationships requires an understanding of protein connectivity at proteome-scale. A proteome-scale map of the protein–protein interaction network provides a global view of cellular organization and function. Here, we discuss a summary of methods for building proteome-scale interactome maps and the current status and implications of mapping achievements. Not only do interactome maps serve as a reference, detailing global cellular function and organization patterns, but they can also reveal the mechanisms altered by disease alleles, highlight the patterns of interaction rewiring across evolution, and help pinpoint biologically and therapeutically relevant proteins. Despite the considerable strides made in proteome-wide mapping, several technical challenges persist. Therefore, future considerations that impact current mapping efforts are also discussed.
Disciplines :
Genetics & genetic processes
Author, co-author :
Desbuleux, Alice ✱; Université de Liège - ULiège > Form. doct. sc. (bioch., biol. mol. cel., bioinf. - paysage)
Cafarelli, Tiziana ✱; Harvard University > Department of Genetics
Wang, Yang; Harvard University > Department of Genetics
Choi, Soon Gang; Harvard University > Department of Genetics
De Ridder, David; Harvard University > Department of Genetics
Vidal, Marc; Harvard University > Department of Genetics
✱ These authors have contributed equally to this work.
Language :
English
Title :
Mapping, modeling, and characterization of protein–protein interactions on a proteomic scale
Vidal, M., Cusick, M.E., Barabási, A.-L., Interactome networks and human disease (2011) Cell, 144, pp. 986-998
Sahni, N., Yi, S., Taipale, M., Fuxman Bass, J.I., Coulombe-Huntington, J., Yang, F., Peng, J., Wang, Y., Widespread macromolecular interaction perturbations in human genetic disorders (2015) Cell, 161, pp. 647-660. , This is the first large-scale systematic study determining an interaction profile for disease-associated alleles and common variants. Disease-associated alleles are more likely to perturb interaction profiles than common variants
Sahni, N., Yi, S., Zhong, Q., Jailkhani, N., Charloteaux, B., Cusick, M.E., Vidal, M., Edgotype: a fundamental link between genotype and phenotype (2013) Curr. Opin. Genet. Dev., 23, pp. 649-657
Zhong, Q., Simonis, N., Li, Q.-R., Charloteaux, B., Heuze, F., Klitgord, N., Tam, S., Mou, D., Edgetic perturbation models of human inherited disorders (2009) Mol. Syst. Biol., 5, p. 321
Luck, K., Jailkhani, N., Cusick, M.E., Rolland, T., Calderwood, M.A., Charloteaux, B., Vidal, M., Interactomes—scaffolds of cellular systems (2016) Encycl. Cell Biol., 4, pp. 187-198
Fields, S., Song, O., A novel genetic system to detect protein–protein interactions (1989) Nature, 340, pp. 245-246
Venkatesan, K., Rual, J.-F., Vazquez, A., Stelzl, U., Lemmens, I., Hirozane-Kishikawa, T., Hao, T., Goh, K.-I., An empirical framework for binary interactome mapping (2009) Nat. Methods, 6, pp. 83-90
Rolland, T., Tasan, M., Charloteaux, B., Pevzner, S.J., Zhong, Q., Sahni, N., Yi, S., Mosca, R., A proteome-scale map of the human interactome network (2014) Cell, 159, pp. 1212-1226. , This study reports the largest systematic map of human binary protein–protein interactions. Comparison with PPIs derived from small-scale studies shows a broader and more homogenous coverage of the interactome network
Uetz, P., Giot, L., Cagney, G., Mansfield, T.A., Judson, R.S., Knight, J.R., Lockshon, D., Pochart, P., A comprehensive analysis of protein–protein interactions in Saccharomyces cerevisiae (2000) Nature, 403, pp. 623-627
Ito, T., Chiba, T., Ozawa, R., Yoshida, M., Hattori, M., Sakaki, Y., A comprehensive two-hybrid analysis to explore the yeast protein interactome (2001) Proc. Natl. Acad. Sci. U. S. A., 98, pp. 4569-4574
Yu, H., Braun, P., Yildirim, M.A., Lemmens, I., Venkatesan, K., Sahalie, J., Hirozane-Kishikawa, T., Simonis, N., High-quality binary protein interaction map of the yeast interactome network (2008) Science, 322, pp. 104-110
Vo, T.V., Das, J., Meyer, M.J., Cordero, N.A., Akturk, N., Wei, X., Fair, B.J., Liu, L.G., A proteome-wide fission yeast interactome reveals network evolution principles from yeasts to human (2016) Cell, 164, pp. 310-323
Rajagopala, S.V., Sikorski, P., Kumar, A., Mosca, R., Vlasblom, J., Arnold, R., Franca-Koh, J., Ceol, A., The binary protein–protein interaction landscape of Escherichia coli (2014) Nat. Biotechnol., 32, pp. 285-290
Li, S., Armstrong, C.M., Bertin, N., Ge, H., Milstein, S., Boxem, M., Vidalain, P.-O., Hao, T., A map of the interactome network of the metazoan C. elegans (2004) Science, 303, pp. 540-543
Simonis, N., Rual, J., Carvunis, A., Tasan, M., Lemmens, I., Hirozane-Kishikawa, T., Hao, T., Gebreab, F., Empirically controlled mapping of the Caenorhabditis elegans protein–protein interactome network (2009) Nat. Methods, 6, pp. 47-54
Arabidopsis Interactome Mapping Consortium, Evidence for network evolution in an Arabidopsis interactome map (2011) Science, 333, pp. 601-607
Braun, P., Tasan, M., Dreze, M., Barrios-Rodiles, M., Lemmens, I., Yu, H., Sahalie, J.M., de Smet, A.-S., An experimentally derived confidence score for binary protein–protein interactions (2009) Nat. Methods, 6, pp. 91-97
Huttlin, E.L., Ting, L., Bruckner, R.J., Gebreab, F., Gygi, M.P., Szpyt, J., Tam, S., Baltier, K., The BioPlex network: a systematic exploration of the human interactome (2015) Cell, 162, pp. 425-440. , This large-scale systematic study determines protein–protein associations for human proteins using AP-MS. The data permits characterization of proteins of unknown function
Hein, M.Y., Hubner, N.C., Poser, I., Cox, J., Nagaraj, N., Toyoda, Y., Gak, I.A., Buchholz, F., A human interactome in three quantitative dimensions organized by stoichiometries and abundances (2015) Cell, 163, pp. 712-723
Vinayagam, A., Stelzl, U., Wanker, E.E., Repeated two-hybrid screening detects transient protein–protein interactions (2010) Theor. Chem. Acc., 125, pp. 613-619
Snider, J., Kotlyar, M., Saraon, P., Yao, Z., Jurisica, I., Stagljar, I., Fundamentals of protein interaction network mapping (2015) Mol. Syst. Biol., 11. , 848
Havugimana, P.C., Hart, G.T., Nepusz, T., Yang, H., Turinsky, A.L., Li, Z., Wang, P.I., Phanse, S., A census of human soluble protein complexes (2012) Cell, 150, pp. 1068-1081. , The authors present the first large-scale systematic dataset of human protein–protein associations generated by co-fractionation and mass spectrometry. Associations within protein complexes were used to predict unknown protein function and disease association
Wan, C., Phanse, S., Borgeson, B., Tu, F., Drew, K., Clark, G., Xiong, X., Bezginov, A., Proteome-wide dataset supporting the study of ancient metazoan macromolecular complexes (2016) Nature, 6, pp. 715-721. , The authors use co-fractionation and mass spectrometry to explore the evolutionary conservation of protein complexes among various metazoan models
Valencia, A., Pazos, F., Computational methods for the prediction of protein interactions (2002) Curr. Opin. Struct. Biol., 12, pp. 368-373
Shoemaker, B.A., Panchenko, A.R., Deciphering protein–protein interactions. Part II. Computational methods to predict protein and domain interaction partners (2007) PLoS Comput. Biol., 3, pp. 595-601
Yu, J., Fotouhi, F., Computational approaches for predicting protein–protein interactions: a survey (2006) J. Med. Syst., 30, pp. 39-44
Tuncbag, N., Kar, G., Keskin, O., Gursoy, A., Nussinov, R., A survey of available tools and web servers for analysis of protein–protein interactions and interfaces (2009) Brief. Bioinform., 10, pp. 217-232
Zahiri, J., Bozorgmehr, J.H., Masoudi-Nejad, A., Computational prediction of protein–protein interaction networks: algorithms and resources (2013) Curr. Genom., 14, pp. 397-414. , The authors provide a comprehensive summary of computational methods for PPI prediction and major PPI databases
Raman, K., Eisenberg, D., Marcotte, E., Xenarios, I., Yeates, T., Anderson, P., Valencia, A., Bsche, M., Construction and analysis of protein–protein interaction networks (2010) Autom. Exp., 2, p. 2
Rhodes, D.R., Tomlins, S.A., Tomlins, S.A., Varambally, S., Mahavisno, V., Barrette, T., Kalyana-sundaram, S., Chinnaiyan, A.M., Probabilistic model of the human protein–protein interaction network (2005) Nat. Biotechnol., 23, pp. 951-959
Zhang, Q.C., Petrey, D., Deng, L., Qiang, L., Shi, Y., Thu, C.A., Bisikirska, B., Hunter, T., Structure-based prediction of protein–protein interactions on a genome-wide scale (2012) Nature, 490, pp. 556-560
Hopf, T.A., Schärfe, C.P.I., Rodrigues, J.P.G.L.M., Green, A.G., Kohlbacher, O., Sander, C., Bonvin, A.M.J.J., Marks, D.S., Sequence co-evolution gives 3D contacts and structures of protein complexes (2014) Elife, 3, p. e03430. , The authors demonstrate that evolutionary couplings can be used to predict direct protein interactions at the resolution of amino acid residues
Lei, C., Ruan, J., A novel link prediction algorithm for reconstructing protein–protein interaction networks by topological similarity (2013) Bioinformatics, 29, pp. 355-364
Vreven, T., Hwang, H., Pierce, B.G., Weng, Z., Prediction of protein–protein binding free energies (2012) Protein Sci., 21, pp. 396-404
Erijman, A., Rosenthal, E., Shifman, J.M., How structure defines affinity in protein–protein interactions (2014) PLoS One, 9
Moal, I.H., Agius, R., Bates, P.A., Protein–protein binding affinity prediction on a diverse set of structures (2011) Bioinformatics, 27, pp. 3002-3009
Moal, I.H., Moretti, R., Baker, D., Fernández-Recio, J., Scoring functions for protein–protein interactions (2013) Curr. Opin. Struct. Biol., 23, pp. 862-867
Li, M., Petukh, M., Alexov, E., Panchenko, A.R., Predicting the impact of missense mutations on protein–protein binding affinity (2014) J. Chem. Theory Comput., 10, pp. 1770-1780. , By using in silico dynamic simulations of protein complex structures, the authors reported a pipeline to predict the PPI affinity changes for single and multiple missense mutations with consideration of protein flexibility
Brender, J.R., Zhang, Y., Predicting the effect of mutations on protein–protein binding interactions through structure-based interface profiles (2015) PLoS Comput. Biol., 11, pp. 1-25
Tang, H., Thomas, P.D., Tools for predicting the functional impact of nonsynonymous genetic variation (2016) Genetics, 203, pp. 635-647
Hassan, I., Editorial recent advances in the structure-based drug design and discovery (2016) Curr. Top. Med. Chem., 16, pp. 899-900
Mosca, R., Céol, A., Aloy, P., Interactome3D: adding structural details to protein networks (2012) Nat. Methods, 10, pp. 47-53. , This resource provides structural information for protein–protein interactions in 18 organisms
Szilagyi, A., Zhang, Y., Template-based structure modeling of protein–protein interactions (2014) Curr. Opin. Struct. Biol., 24, pp. 10-23
Muratcioglu, S., Guven-Maiorov, E., Keskin, O., Gursoy, A., Advances in template-based protein docking by utilizing interfaces towards completing structural interactome (2015) Curr. Opin. Struct. Biol., 35, pp. 87-92
Vakser, I.A., Low-resolution structural modeling of protein interactome (2013) Curr. Opin. Struct. Biol., 23, pp. 198-205
Stenson, P.D., Mort, M., Ball, E.V., Shaw, K., Phillips, A.D., Cooper, D.N., The human gene mutation database: building a comprehensive mutation repository for clinical and molecular genetics, diagnostic testing and personalized genomic medicine (2014) Hum. Genet., 133, pp. 1-9
Hamosh, A., Scott, A.F., Amberger, J.S., Bocchini, C.A., McKusick, V.A., Online Mendelian inheritance in man (OMIM), a knowledge base of human genes and genetic disorders (2005) Nucleic Acids Res., 33, pp. 514-517
Goh, K.-I., Cusick, M.E., Valle, D., Childs, B., Vidal, M., Barabási, A.-L., The human disease network (2007) Proc. Natl. Acad. Sci. U. S. A., 104, pp. 8685-8690
Menche, J., Sharma, A., Kitsak, M., Ghiassian, S.D., Vidal, M., Loscalzo, J., Barabási, A.-L., Disease networks. Uncovering disease–disease relationships through the incomplete interactome (2015) Science, 347, p. 1257601
Lim, J., Hao, T., Shaw, C., Patel, A.J., Szabó, G., Rual, J.F., Fisk, C.J., Hill, D.E., A protein–protein interaction network for human inherited ataxias and disorders of Purkinje cell degeneration (2006) Cell, 125, pp. 801-814
Kitsak, M., Sharma, A., Menche, J., Guney, E., Ghiassian, S.D., Loscalzo, J., Barabási, A.-L., Tissue specificity of human disease module (2016) Sci. Rep., 6, p. 35241. , Disease-associated genes tend to cluster in a particular network neighborhoods and show patterns of co-expression in tissues where disease manifests. This study can predict disease-tissue associations
Rozenblatt-Rosen, O., Deo, R.C., Padi, M., Adelmant, G., Calderwood, M.A., Rolland, T., Grace, M., Tavares, M., Interpreting cancer genomes using systematic host network perturbations by tumour virus proteins (2012) Nature, 487, pp. 491-495
Gavin, A.-C., Aloy, P., Grandi, P., Krause, R., Boesche, M., Marzioch, M., Rau, C., Dümpelfeld, B., Proteome survey reveals modularity of the yeast cell machinery (2006) Nature, 440, pp. 631-636
Collins, S.R., Kemmeren, P., Zhao, X.-C., Greenblatt, J.F., Spencer, F., Holstege, F.C.P., Weissman, J.S., Krogan, N.J., Toward a comprehensive atlas of the physical interactome of Saccharomyces cerevisiae (2007) Mol. Cell. Proteom., 6, pp. 439-450
Krogan, N.J., Cagney, G., Yu, H., Zhong, G., Guo, X., Ignatchenko, A., Li, J., Tikuisis, A.P., Global landscape of protein complexes in the yeast Saccharomyces cerevisiae (2006) Nature, 440, pp. 637-643
Guruharsha, K.G., Rual, J.-F., Zhai, B., Mintseris, J., Vaidya, P., Vaidya, N., Beekman, C., Cenaj, O., A protein complex network of Drosophila melanogaster (2011) Cell, 147, pp. 690-703
Zhong, Q., Pevzner, S.J., Mosca, R., Menche, J., Taipale, M., Remnants of co-functionality between two evolutionarily distant proteomes (2016) Mol. Syst. Biol., 12, p. 865
Diss, G., Gagnon-Arsenault, I., Dion-Coté, A.-M., Vignaud, H., Ascencio, D.I., Berger, C.M., Landry, C.R., Gene duplication can impart fragility, not robustness, in the yeast protein interaction network (2017) Science, 355, pp. 630-634
Carvunis, A.-R., Rolland, T., Wapinski, I., Calderwood, M.A., Yildirim, M.A., Simonis, N., Charloteaux, B., Santhanam, B., Proto-genes and de novo gene birth (2012) Nature, 487, pp. 370-374. , This study computationally identifies candidate proto-genes in the S. cerevisiae genome. The authors provide evidence that de novo gene birth may be an important mechanism for generating new protein coding genes
Tautz, D., Domazet-Lošo, T., The evolutionary origin of orphan genes (2011) Nat. Rev. Genet., 12, pp. 692-702
McLysaght, A., Guerzoni, D., New genes from non-coding sequence: the role of de novo protein-coding genes in eukaryotic evolutionary innovation (2015) Philos. Trans. R. Soc. Lond. B. Biol. Sci., 370. , This review summarizes various aspects of de novo gene birth, including generation, fixation and functional contribution
Siepel, A., Knowles, D.G., Mclysaght, A., Darwinian alchemy: human genes from noncoding DNA Darwinian alchemy: human genes from noncoding DNA (2009) Genome Res., 19, pp. 1693-1695
Knowles, D.G., Mclysaght, A., Recent de novo origin of human protein-coding genes (2009) Genome Res., 19, pp. 1-9
Rodriguez, J.M., Maietta, P., Ezkurdia, I., Pietrelli, A., Wesselink, J.J., Lopez, G., Valencia, A., Tress, M.L., APPRIS: annotation of principal and alternative splice isoforms (2013) Nucleic Acids Res., 41, pp. 110-117
Corominas, R., Yang, X., Lin, G.N., Kang, S., Shen, Y., Ghamsari, L., Broly, M., Trigg, S.A., Protein interaction network of alternatively spliced isoforms from brain links genetic risk factors for autism (2014) Nat. Commun., 5, p. 3650
Yang, X., Coulombe-Huntington, J., Kang, S., Sheynkman, G.M., Hao, T., Richardson, A., Sun, S., Murray, R.R., Widespread expansion of protein interaction capabilities by alternative splicing (2016) Cell, 164, pp. 805-817. , This paper presents the first large-scale attempt to identify differences in the interaction profiles of human protein isoforms
Woodsmith, J., Stelzl, U., Studying post-translational modifications with protein interaction networks (2014) Curr. Opin. Struct. Biol., 24, pp. 34-44
Ellis, J.D., Barrios-Rodiles, M., Çolak, R., Irimia, M., Kim, T., Calarco, J.A., Wang, X., Kim, P.M., Tissue-specific alternative splicing remodels protein–protein interaction networks (2012) Mol. Cell, 46, pp. 884-892
Buntru, A., Trepte, P., Klockmeier, K., Schnoegl, S., Wanker, E.E., Current approaches toward quantitative mapping of the interactome (2016) Front. Genet., 7, pp. 1-9
Meyer, K., Selbach, M., Quantitative affinity purification mass spectrometry: a versatile technology to study protein–protein interactions (2015) Front. Genet., 6, pp. 1-7