Prediction of Protein‐Protein Interactions

Max Kotlyar1, Andrea E.M. Rossos1, Igor Jurisica2

1 Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, 2 Institute of Neuroimmunology, Slovak Academy of Sciences, Bratislava
Publication Name:  Current Protocols in Bioinformatics
Unit Number:  Unit 8.2
DOI:  10.1002/cpbi.38
Online Posting Date:  December, 2017
GO TO THE FULL TEXT: PDF or HTML at Wiley Online Library

Abstract

The authors provide an overview of physical protein‐protein interaction prediction, covering the main strategies for predicting interactions, approaches for assessing predictions, and online resources for accessing predictions. This unit focuses on the main advancements in each of these areas over the last decade. The methods and resources that are presented here are not an exhaustive set, but characterize the current state of the field—highlighting key challenges and achievements. © 2017 by John Wiley & Sons, Inc.

Keywords: bioinformatics; interaction networks; machine learning; protein interactions

     
 
GO TO THE FULL PROTOCOL:
PDF or HTML at Wiley Online Library

Table of Contents

  • Introduction
  • Prediction Approaches
  • Assessment of Predictions
  • Accessibility of Predictions
  • Observations and Conclusions
  • Literature Cited
  • Figures
  • Tables
     
 
GO TO THE FULL PROTOCOL:
PDF or HTML at Wiley Online Library

Materials

GO TO THE FULL PROTOCOL:
PDF or HTML at Wiley Online Library

Figures

Videos

Literature Cited

Literature Cited
  Aloy, P., & Russell, R. B. (2002). Interrogating protein interaction networks through structural biology. Proceedings of the National Academy of Sciences of the United States of America, 99(9), 5896–5901. doi: 10.1073/pnas.092147999.
  Aloy, P., & Russell, R. B. (2006). Structural systems biology: Modelling protein interactions. Nature Reviews Molecular Cell Biology, 7(3), 188–197. doi: 10.1038/nrm1859.
  Bader, J. S., Chaudhuri, A., Rothberg, J. M., & Chant, J. (2004). Gaining confidence in high‐throughput protein interaction networks. Nature Biotechnology, 22(1), 78–85. doi: 10.1038/nbt924.
  Bader, J. S. (2003). Greedily building protein networks with confidence. Bioinformatics, 19(15), 1869–1874. doi: 10.1093/bioinformatics/btg358.
  Barabási, A.‐L., Gulbahce, N., & Loscalzo, J. (2011). Network medicine: A network‐based approach to human disease. Nature Reviews. Genetics, 12(1), 56–68. doi: 10.1038/nrg2918.
  Baspinar, A., Cukuroglu, E., Nussinov, R., Keskin, O., & Gursoy, A. (2014). PRISM: A web server and repository for prediction of protein‐protein interactions and modeling their 3D complexes. Nucleic Acids Research, 42(W1), W285–W289. doi: 10.1093/nar/gku397.
  Ben‐Hur, A., & Noble, W. S. (2006). Choosing negative examples for the prediction of protein‐protein interactions. BMC Bioinformatics, 7(Suppl 1), S2. doi: 10.1186/1471‐2105‐7‐S1‐S2.
  Ben‐Hur, A., & Noble, W. S. (2005). Kernel methods for predicting protein‐protein interactions. Bioinformatics (Oxford, England), 21(Suppl 1), i38–i46. doi: 10.1093/bioinformatics/bti1016.
  Bock, J. R., & Gough, D. A. (2001). Predicting protein–protein interactions from primary structure. Bioinformatics, 17(5), 455–460. doi: 10.1093/bioinformatics/17.5.455.
  Breuer, K., Foroushani, A. K., Laird, M. R., Chen, C., Sribnaia, A., Lo, R., … Lynn, D. J. (2013). InnateDB: Systems biology of innate immunity and beyond–recent updates and continuing curation. Nucleic Acids Research, 41(Database issue), D1228–33. doi: 10.1093/nar/gks1147.
  Brown, K. R., & Jurisica, I. (2007). Unequal evolutionary conservation of human protein interactions in interologous networks. Genome Biology, 8(5), R95. doi: 10.1186/gb‐2007‐8‐5‐r95.
  Chatr‐aryamontri, A., Oughtred, R., Boucher, L., Rust, J., Chang, C., Kolas, N. K. … Tyers, M. (2017). The BioGRID interaction database: 2017 update. Nucleic Acids Research, 45(D1), D369–D379. doi: 10.1093/nar/gkw1102.
  Chen, J. Y., Pandey, R., & Nguyen, T. M. (2017). HAPPI‐2: A comprehensive and high‐quality map of human annotated and predicted protein interactions. BMC Genomics, 18(1), 182. doi: 10.1186/s12864‐017‐3512‐1.
  Chen, X. W., & Liu, M. (2005). Prediction of protein‐protein interactions using random decision forest framework. Bioinformatics, 21(24), 4394–4400. doi: 10.1093/bioinformatics/bti721.
  Dandekar, T., Snel, B., Huynen, M., & Bork, P. (1998). Conservation of gene order: A fingerprint of proteins that physically interact. Trends in Biochemical Sciences, 23, 324–328. doi: 10.1016/S0968‐0004(98)01274‐2.
  De Juan, D., Pazos, F., & Valencia, A. (2013). Emerging methods in protein co‐evolution. Nature Reviews Genetics, 14(4), 249–261. doi: 10.1038/nrg3414.
  De Las Rivas, J., & Prieto, C. (2012). Protein interactions: Mapping interactome networks to support drug target discovery and selection. Methods in Molecular Biology (Clifton, N.J.), 910, 279–296. doi: 10.1007/978‐1‐61779‐965‐5_12.
  Deane, C. M., Salwiński, Ł., Xenarios, I., & Eisenberg, D. (2002). Protein interactions: Two methods for assessment of the reliability of high throughput observations. Molecular & Cellular Proteomics: MCP, 1(5), 349–356. doi: 10.1074/mcp.M100037‐MCP200.
  Ding, Y.‐D., Chang, J. W., Guo, J., Chen, D., Li, S., Xu, Q., … Chen, L. L. (2014). Prediction and functional analysis of the sweet orange protein‐protein interaction network. BMC Plant Biology, 14(1), 213. doi: 10.1186/s12870‐014‐0213‐7.
  Elefsinioti, A., Saraç, Ö. S., Hegele, A., Plake, C., Hubner, N. C., Poser, I., … Beyer, A. (2011). Large‐scale de novo prediction of physical protein‐protein association. Molecular & Cellular Proteomics, 10(11), M111.010629. doi: 10.1074/mcp.M111.010629.
  Enright, A. J., Iliopoulos, I., Kyrpides, N. C., & Ouzounis, C. A. (1999). Protein interaction maps for complete genomes based on gene fusion events. Nature, 402, 86–90. doi: 10.1038/47056.
  Garcia‐Garcia, J., Schleker, S., Klein‐Seetharaman, J., & Oliva, B. (2012). BIPS: BIANA Interolog Prediction Server. A tool for protein‐protein interaction inference. Nucleic Acids Research, 40(Web Server issue), W147–51. doi: 10.1093/nar/gks553.
  Goldberg, D. S., & Roth, F. P. (2003). Assessing experimentally derived interactions in a small world. Proceedings of the National Academy of Sciences of the United States of America, 100(8), 4372–4376. doi: 10.1073/pnas.0735871100.
  Gu, H., Zhu, P., Jiao, Y., Meng, Y., & Chen, M. (2011). PRIN: A predicted rice interactome network. BMC Bioinformatics, 12(1), 161. doi: 10.1186/1471‐2105‐12‐161.
  Guo, Y., Yu, L., Wen, Z., & Li, M. (2008). Using support vector machine combined with auto covariance to predict protein–protein interactions from protein sequences. Nucleic Acids Research, 36(9), 3025–3030. doi: 10.1093/nar/gkn159.
  Hamp, T., & Rost, B. (2015a). Evolutionary profiles improve protein–protein interaction prediction from sequence. Bioinformatics, 31(12), 1945–1950. doi: 10.1093/bioinformatics/btv077.
  Hamp, T., & Rost, B. (2015b). Evolutionary profiles improve protein–protein interaction prediction from sequence. Bioinformatics, 31(12), 1945–1950. doi: 10.1093/bioinformatics/btv077.
  Hamp, T., & Rost, B. (2015c). More challenges for machine‐learning protein interactions. Bioinformatics, 31(10), 1521–1525. doi: 10.1093/bioinformatics/btu857.
  Havugimana, P. C., Hart, G. T., Nepusz, T., Yang, H., Turinsky, A. L., Li, Z., … Emili, A. (2012). A census of human soluble protein complexes. Cell, 150, 1068–1081. doi: 10.1016/j.cell.2012.08.011.
  Hein, M. Y., Hubner, N. C., Poser, I., Cox, J., Nagaraj, N., Toyoda, Y., … Mann, M. (2015). A human interactome in three quantitative dimensions organized by stoichiometries and abundances. Cell, 163(3), 712–723. doi: 10.1016/j.cell.2015.09.053.
  Hart, G. T., Ramani, A. K., & Marcotte, E. M. (2006). How complete are current yeast and human protein‐interaction networks? Genome Biology, 7(11), 120. doi: 10.1186/gb‐2006‐7‐11‐120.
  Hermjakob, H., Montecchi‐Palazzi, L., Bader, G., Wojcik, J., Salwinski, L., Ceol, A., … Apweiler, R. (2004). The HUPO PSI's Molecular Interaction format—a community standard for the representation of protein interaction data. Nature Biotechnology, 22(2), 177–183. doi: 10.1038/nbt926.
  Huang, T. W., Lin, C. Y., & Kao, C. Y. (2007). Reconstruction of human protein interolog network using evolutionary conserved network. BMC Bioinformatics, 8, 152. doi: 10.1186/1471‐2105‐8‐152.
  Huttlin, E. L., Ting, L., Bruckner, R. J., Gebreab, F., Gygi, M. P., Szpyt, J., … Gygi, S. P. (2015). The BioPlex Network: A systematic exploration of the human interactome. Cell, 162(2), 425–440. doi: 10.1016/j.cell.2015.06.043.
  Janin, J., Henrick, K., Moult, J., Eyck, L. T., Sternberg, M. J., Vajda, S., … Wodak, S. J. (2003). CAPRI: A critical assessment of PRedicted interactions. Proteins: Structure, Function, and Genetics, 52(1), 2–9. doi: 10.1002/prot.10381.
  Jansen, R., Yu, H., Greenbaum, D., Kluger, Y., Krogan, N. J., Chung, S., … Gerstein, M. (2003). A Bayesian networks approach for predicting protein‐protein interactions from genomic data. Science, 302(5644), 449–453. doi: 10.1126/science.1087361.
  Jansen, R., & Gerstein, M. (2004). Analyzing protein function on a genomic scale: The importance of gold‐standard positives and negatives for network prediction. Current Opinion in Microbiology, 7(5), 535–545. doi: 10.1016/j.mib.2004.08.012.
  Kanaan, S. P., Huang, C., Wuchty, S., Chen, D. Z., & Izaguirre, J. A. (2009). Inferring protein‐protein interactions from multiple protein domain combinations. Methods in Molecular Biology, 541, 43–59. doi: 10.1007/978‐1‐59745‐243‐4_3.
  Kim, I., Liu, Y., & Zhao, H. (2007). Bayesian methods for predicting interacting protein pairs using domain information. Biometrics, 63(3), 824–833. doi: 10.1111/j.1541‐0420.2007.00755.x.
  Kotlyar, M., Pastrello, C., Pivetta, F., Lo Sardo, A., Cumbaa, C., Li, H., … Jurisica, I. (2015). In silico prediction of physical protein interactions and characterization of interactome orphans. Nature Methods, 12(1), 79–84. doi: 10.1038/nmeth.3178.
  Kotlyar, M., Pastrello, C., Sheahan, N., & Jurisica, I. (2016). Integrated interactions database: Tissue‐specific view of the human and model organism interactomes. Nucleic Acids Research, 44(D1), D536–41. doi: 10.1093/nar/gkv1115.
  Li, Z.‐W., You, Z.‐H., Chen, X., Li, L.‐P., Huang, D.‐S., Yan, G.‐Y., … Huang, Y.‐A. (2017). Accurate prediction of protein‐protein interactions by integrating potential evolutionary information embedded in PSSM profile and discriminative vector machine classifier. Oncotarget, 8(14), 23638–23649. doi: 10.18632/oncotarget.15564.
  Lin, M., Shen, X., & Chen, X. (2011). PAIR: The predicted Arabidopsis interactome resource. Nucleic Acids Research, 39(suppl_1), D1134–D1140. doi: 10.1093/nar/gkq938.
  Lu, L., Lu, H., & Skolnick, J. (2002). Multiprospector: An algorithm for the prediction of protein‐protein interactions by multimeric threading. Proteins: Structure, Function and Genetics, 49, 350–364. doi: 10.1002/prot.10222.
  Maheshwari, S., & Brylinski, M. (2017). Across‐proteome modeling of dimer structures for the bottom‐up assembly of protein‐protein interaction networks. BMC Bioinformatics, 18(1), 257. doi: 10.1186/s12859‐017‐1675‐z.
  Martin, S., Roe, D., & Faulon, J. L. (2005). Predicting protein‐protein interactions using signature products. Bioinformatics, 21, 218–226. doi: 10.1093/bioinformatics/bth483.
  McDowall, M. D., Scott, M. S., & Barton, G. J. (2009). PIPs: Human protein‐protein interaction prediction database. Nucleic Acids Research, 37(Database), D651–D656. doi: 10.1093/nar/gkn870.
  Mosca, R., Pons, C., Fernández‐Recio, J., & Aloy, P. (2009). Pushing structural information into the yeast interactome by high‐throughput protein docking experiments. PLoS Computational Biology, 5(8), e1000490. doi: 10.1371/journal.pcbi.1000490.
  Mostafavi, S., & Morris, Q. (2012). Combining many interaction networks to predict gene function and analyze gene lists. Proteomics, 12(10), 1687–1696. doi: 10.1002/pmic.201100607.
  Navlakha, S., & Kingsford, C. (2010). The power of protein interaction networks for associating genes with diseases. Bioinformatics (Oxford, England), 26(8), 1057–1063. doi: 10.1093/bioinformatics/btq076.
  Ngounou Wetie, A. G., Sokolowska, I., Woods, A. G., Roy, U., Deinhardt, K., & Darie, C. C. (2014). Protein–protein interactions: Switch from classical methods to proteomics and bioinformatics‐based approaches. Cellular and Molecular Life Sciences, 71(2), 205–228. doi: 10.1007/s00018‐013‐1333‐1.
  Orchard, S., Kerrien, S., Abbani, S., Aranda, B., Bhate, J., Bidwell, S., … Hermjakob, H. (2012). Protein interaction data curation: The International Molecular Exchange (IMEx) consortium. Nature Methods, 9(4), 345–350. doi: 10.1038/nmeth.1931.
  Overbeek, R., Fonstein, M., D'Souza, M., Pusch, G. D., & Maltsev, N. (1999). Use of contiguity on the chromosome to predict functional coupling. In Silico Biology, 1(2), 93–108.
  Park, Y., & Marcotte, E. M. (2012). Flaws in evaluation schemes for pair‐input computational predictions. Nature Methods, 9(12), 1134–1136. doi: 10.1038/nmeth.2259.
  Pawson, T., Gish, G. D., & Nash, P. (2001). SH2 domains, interaction modules and cellular wiring. Trends in Cell Biology, 11(12), 504–511. doi: 10.1016/S0962‐8924(01)02154‐7.
  Pawson, T., & Nash, P. (2003). Assembly of cell regulatory systems through protein interaction domains. Science, 300(5618), 445–452. doi: 10.1126/science.1083653.
  Pellegrini, M., Marcotte, E. M., Thompson, M. J., Eisenberg, D., & Yeates, T. O. (1999). Assigning protein functions by comparative genome analysis: Protein phylogenetic profiles. Proceedings of the National Academy of Sciences of the United States of America, 96, 4285–4288. doi: 10.1073/pnas.96.8.4285.
  Petschnigg, J., Groisman, B., Kotlyar, M., Taipale, M., Zheng, Y., Kurat, C. F., … Stagljar, I. (2014). The mammalian‐membrane two‐hybrid assay (MaMTH) for probing membrane‐protein interactions in human cells. Nature Methods, 11(5), 585–592. doi: 10.1038/nmeth.2895.
  Pitre, S., Hooshyar, M., Schoenrock, A., Samanfar, B., Jessulat, M., Green, J. R., … Golshani, A. (2012a). Short Co‐occurring polypeptide regions can predict global protein interaction maps. Scientific Reports, 2, 239. doi: 10.1038/srep00239.
  Planas‐Iglesias, J., Marin‐Lopez, M. A., Bonet, J., Garcia‐Garcia, J., & Oliva, B. (2013). iLoops: A protein–protein interaction prediction server based on structural features. Bioinformatics, 29(18), 2360–2362. doi: 10.1093/bioinformatics/btt401.
  Przulj, N., Wigle, D. A., & Jurisica, I. (2004). Functional topology in a network of protein interactions. Bioinformatics, 20(3), 340–348. doi: 10.1093/bioinformatics/btg415.
  Qi, Y., Bar‐Joseph, Z., & Klein‐Seetharaman, J. (2006). Evaluation of different biological data and computational classification methods for use in protein interaction prediction. Proteins, 63(3), 490–500. doi: 10.1002/prot.20865.
  Rhodes, D. R., Tomlins, S. A., Varambally, S., Mahavisno, V., Barrette, T., Kalyana‐Sundaram, S., … Chinnaiyan, A. M. (2005). Probabilistic model of the human protein‐protein interaction network. Nature Biotechnology, 23(8), 951–959. doi: 10.1038/nbt1103.
  Rolland, T., Taşan, M., Charloteaux, B., Pevzner, S. J., Zhong, Q., Sahni, N., … Vidal, M. (2014). A proteome‐scale map of the human interactome network. Cell, 159(5), 1212–1226. doi: 10.1016/j.cell.2014.10.050.
  Saito, R., Suzuki, H., & Hayashizaki, Y. (2003). Construction of reliable protein‐protein interaction networks with a new interaction generality measure. Bioinformatics, 19(6), 756–763. doi: 10.1093/bioinformatics/btg070.
  Saito, R., Suzuki, H., & Hayashizaki, Y. (2002). Interaction generality, a measurement to assess the reliability of a protein‐protein interaction. Nucleic Acids Research, 30(5), 1163–1168. doi: 10.1093/nar/30.5.1163.
  Schwartz, A. S., Yu, J., Gardenour, K. R., Finley, R. L. Jr., & Ideker, T. (2009). Cost‐effective strategies for completing the interactome. Nature Methods, 6(1), 55–61. doi: 10.1038/nmeth.1283.
  Scott, M. S., & Barton, G. J. (2007). Probabilistic prediction and ranking of human protein‐protein interactions. BMC Bioinformatics, 8, 239. doi: 10.1186/1471‐2105‐8‐239.
  Shen, J., Zhang, J., Luo, X., Zhu, W., Yu, K., Chen, K., … Jiang, H. (2007). Predicting protein‐protein interactions based only on sequences information. Proceedings of the National Academy of Sciences of the United States of America, 104, 4337–4341. doi: 10.1073/pnas.0607879104.
  Singh, R., Park, D., Xu, J., Hosur, R., & Berger, B. (2010). Struct2Net: A web service to predict protein‐protein interactions using a structure‐based approach. Nucleic Acids Research, 38(Web Server issue), W508–515. doi: 10.1093/nar/gkq481.
  Snider, J., Kotlyar, M., Saraon, P., Yao, Z., Jurisica, I., & Stagljar, I. (2015). Fundamentals of protein interaction network mapping. Molecular Systems Biology, 11(12), 848. doi: 10.15252/msb.20156351.
  Sprinzak, E., & Margalit, H. (2001). Correlated sequence‐signatures as markers of protein‐protein interaction. Journal of Molecular Biology, 311, 681–692. doi: 10.1006/jmbi.2001.4920.
  Szklarczyk, D., Morris, J. H., Cook, H., Kuhn, M., Wyder, S., Simonovic, M., & Santos, A. (2017). The STRING database in 2017: Quality‐controlled protein‐protein association networks, made broadly accessible. Nucleic Acids Research, 45(D1), D362–D368. doi: 10.1093/nar/gkw937.
  Turinsky, A. L., Razick, S., Turner, B., Donaldson, I. M., & Wodak, S. J. (2014). Navigating the global protein‐protein interaction landscape using iRefWeb. Methods in Molecular Biology (Clifton, N.J.), 1091, 315–331. doi: 10.1007/978‐1‐62703‐691‐7_22.
  Tyagi, M., Hashimoto, K., Shoemaker, B. A., Wuchty, S., & Panchenko, A. R. (2012). Large‐scale mapping of human protein interactome using structural complexes. EMBO Reports, 13(3), 266–271. doi: 10.1038/embor.2011.261.
  Valencia, A., & Pazos, F. (2002). Computational methods for the prediction of protein interactions. Current Opinion in Structural Biology, 12(3), 368–373. doi: 10.1016/S0959‐440X(02)00333‐0.
  Walhout, A. J., Sordella, R., Lu, X., Hartley, J. L., Temple, G. F., Brasch, M. A., … Vidal, M. (2000). Protein interaction mapping in C. elegans using proteins involved in vulval development. Science (New York, N.Y.), 287, 116–122. doi: 10.1126/science.287.5450.116.
  Wan, C., Borgeson, B., Phanse, S., Tu, F., Drew, K., Clark, G., … Emili, A. (2015). Panorama of ancient metazoan macromolecular complexes. Nature, 525(7569), 339–344. doi: 10.1038/nature14877.
  Wang, H., Segal, E., Ben‐Hur, A., Koller, D., & Brutlag, D. L. (2005). Identifying protein‐protein interaction sites on a genome‐wide scale., Advances in Neural Information Processing Systems 17 (NIPS 2004). pp. 1465–1472. Neural Information Processing Systems Foundation, Inc. Available at https://papers.nips.cc/paper/2696‐identifying‐protein‐protein‐interaction‐sites‐on‐a‐genome‐wide‐scale.
  Wang, X., Gulbahce, N., & Yu, H. (2011). Network‐based methods for human disease gene prediction. Briefings in Functional Genomics, 10(5), 280–293. doi: 10.1093/bfgp/elr024.
  Wang, Z., Clark, N. R., & Ma'ayan, A. (2015). Dynamics of the discovery process of protein‐protein interactions from low content studies. BMC Systems Biology, 9, 26. doi: 10.1186/s12918‐015‐0173‐z.
  Warde‐Farley, D., Donaldson, S. L., Comes, O., Zuberi, K., Badrawi, R., Chao, P., … Morris, Q. (2010). The GeneMANIA prediction server: Biological network integration for gene prioritization and predicting gene function. Nucleic Acids Research, 38(Web Server issue), W214–20. Available at https://nar.oxfordjournals.org/content/38/suppl_2/W214.full doi: 10.1093/nar/gkq537.
  Wass, M. N., Fuentes, G., Pons, C., Pazos, F., & Valencia, A. (2014). Towards the prediction of protein interaction partners using physical docking. Molecular Systems Biology, 7(1), 469–469. doi: 10.1038/msb.2011.3.
  Wojcik, J., Boneca, I. G., & Legrain, P. (2002). Prediction, assessment and validation of protein interaction maps in bacteria. Journal of Molecular Biology, 323(4), 763–770. doi: 10.1016/S0022‐2836(02)01009‐4.
  Xenarios, I., Salwínski, L., Duan, X. J., Higney, P., Kim, S. M., & Eisenberg, D. (2002). DIP, the Database of Interacting Proteins: A research tool for studying cellular networks of protein interactions. Nucleic Acids Research, 30, 303–305. doi: 10.1093/nar/30.1.303.
  Yang, X., Coulombe‐Huntington, J., Kang, S., Sheynkman, G. M., Hao, T., Richardson, A., … Vidal, M. (2016). Widespread expansion of protein interaction capabilities by alternative splicing. Cell, 164(4), 805–817. doi: 10.1016/j.cell.2016.01.029.
  You, Z.‐H., Chan, K. C. C., & Hu, P. (2015). Predicting protein‐protein interactions from primary protein sequences using a novel multi‐scale local feature representation scheme and the random forest. PloS One, 10(5), e0125811. Available at https://www.ncbi.nlm.nih.gov/pubmed/25946106. doi: 10.1371/journal.pone.0125811.
  Yu, C.‐Y., Chou, L.‐C., & Chang, D. T.‐H. (2010). Predicting protein‐protein interactions in unbalanced data using the primary structure of proteins. BMC Bioinformatics, 11, 167. doi: 10.1186/1471‐2105‐11‐167.
  Zhang, Q. C., Petrey, D., Deng, L., Qiang, L., Shi, Y., Thu, C. A., … Honig, B. (2012). Structure‐based prediction of protein‐protein interactions on a genome‐wide scale. Nature, 490(7421), 556–560. doi: 10.1038/nature11503.
GO TO THE FULL PROTOCOL:
PDF or HTML at Wiley Online Library