Skip to main content

Prediction, Analysis, Visualization, and Storage of Protein–Protein Interactions Using Computational Approaches

  • Chapter
  • First Online:
Protein-Protein Interactions

Abstract

For many years, protein–protein interactions have been determined solely by virtue of experiments. The experimental techniques employed to elucidate these interactions, however, have faced many inherent limitations. The experimental techniques are time-consuming and not well-suited for creating protein networks that comprise a voluminous amount of data. Consequently, researchers have looked forward to utilize computational techniques to determine such interactions. This chapter will discuss the currently employed computational methods and other web resources like databases and analysis tools. It will also describe how protein interactions are mapped into a network to provide better insight into various pathways that operate at the cellular level. Furthermore, the chapter also throws light on the computational techniques of protein–protein docking and states its implications in further experimental research design.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Afsar Minhas FuA, Geiss BJ, Ben-Hur A. (2014). PAIRpred: Partner-specific prediction of interacting residues from sequence and structure. Proteins: Structure, Function, and Bioinformatics, 82, 1142-1155.

    Article  CAS  Google Scholar 

  • Albert R. (2005). Scale-free networks in cell biology. Journal of cell science, 118, 4947-4957.

    Article  CAS  PubMed  Google Scholar 

  • Albert R, Jeong H, Barabasi AL. (2000). Error and attack tolerance of complex networks. Nature, 406, 378-382.

    Article  CAS  PubMed  Google Scholar 

  • Aoki Y, Okamura Y, Tadaka S, Kinoshita K, Obayashi T. (2016). ATTED-II in 2016: A Plant Coexpression Database Towards Lineage-Specific Coexpression. Plant & cell physiology, 57, e5.

    Article  CAS  Google Scholar 

  • Apweiler R et al. (2010). The universal protein resource (UniProt) in 2010. Nucleic acids research, 38, D142-D148.

    Article  CAS  Google Scholar 

  • Aranda B et al. (2010). The IntAct molecular interaction database in 2010. Nucleic acids research, 38, D525-531.

    Article  CAS  PubMed  Google Scholar 

  • Argos P. (1988). An investigation of protein subunit and domain interfaces. Protein Engineering, Design and Selection, 2, 101-113.

    Article  CAS  Google Scholar 

  • Arkin MR, Wells JA. (2004). Small-molecule inhibitors of protein–protein interactions: progressing towards the dream. Nature reviews Drug discovery, 3, 301-317.

    Article  CAS  PubMed  Google Scholar 

  • Assi SA, Tanaka T, Rabbitts TH, Fernandez-Fuentes N. (2010). PCRPi: Presaging Critical Residues in Protein interfaces, a new computational tool to chart hot spots in protein interfaces. Nucleic acids research, 38, e86-e86.

    Article  PubMed  CAS  Google Scholar 

  • Attwood TK et al. (2012). The PRINTS database: a fine-grained protein sequence annotation and analysis resource—its status in 2012. Database, 2012.

    Google Scholar 

  • Aytuna AS, Gursoy A, Keskin O. (2005). Prediction of protein–protein interactions by combining structure and sequence conservation in protein interfaces. Bioinformatics, 21, 2850-2855.

    Article  CAS  PubMed  Google Scholar 

  • Bader GD, Betel D, Hogue CW. (2003). BIND: the Biomolecular Interaction Network Database. Nucleic acids research, 31, 248-250.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Bahadur RP, Chakrabarti P, Rodier F, Janin J. (2003). Dissecting subunit interfaces in homodimeric proteins. Proteins, 53, 708-719.

    Article  CAS  PubMed  Google Scholar 

  • Bahadur RP, Chakrabarti P, Rodier F, Janin J. (2004). A dissection of specific and non-specific protein–protein interfaces. Journal of molecular biology, 336, 943-955.

    Article  CAS  PubMed  Google Scholar 

  • Bajaj CL, Chowdhury R, Siddahanavalli V. (2009). $ F^ 2$ Dock: Fast Fourier Protein-Protein Docking. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 8, 45-58.

    Article  CAS  Google Scholar 

  • Balaji S, McClendon C, Chowdhary R, Liu JS, Zhang J. (2012). IMID: integrated molecular interaction database. Bioinformatics, 28, 747-749.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Barabasi AL, Oltvai ZN. (2004). Network biology: understanding the cell's functional organization. Nature reviews. Genetics, 5, 101-113.

    Article  CAS  PubMed  Google Scholar 

  • Barbarino JM, Whirl-Carrillo M, Altman RB, Klein TE. (2018). PharmGKB: A worldwide resource for pharmacogenomic information. Wiley interdisciplinary reviews. Systems biology and medicine, 10, e1417.

    Google Scholar 

  • Barrett T et al. (2009). NCBI GEO: archive for high-throughput functional genomic data. Nucleic acids research, 37, D885-890.

    Article  CAS  PubMed  Google Scholar 

  • Barrett T et al. (2013). NCBI GEO: archive for functional genomics data sets--update. Nucleic acids research, 41, D991-995.

    Article  CAS  PubMed  Google Scholar 

  • Barsky A, Gardy JL, Hancock RE, Munzner T. (2007). Cerebral: a Cytoscape plugin for layout of and interaction with biological networks using subcellular localization annotation. Bioinformatics, 23, 1040-1042.

    Article  CAS  PubMed  Google Scholar 

  • Bartel PL, Roecklein JA, SenGupta D, Fields S. (1996). A protein linkage map of Escherichia coli bacteriophage T7. Nature genetics, 12, 72-77.

    Article  CAS  PubMed  Google Scholar 

  • Basse MJ et al. (2012). 2P2Idb: a structural database dedicated to orthosteric modulation of protein–protein interactions. Nucleic acids research, 41, D824-D827.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Batada NN. (2004). CNplot: visualizing pre-clustered networks. Bioinformatics, 20, 1455-1456.

    Article  CAS  PubMed  Google Scholar 

  • Batagelj V, Mrvar A. (1998). Pajek-program for large network analysis. Connections, 21, 47-57.

    Google Scholar 

  • Batagelj V, Mrvar A, Ferligoj A, Doreian P. (2004). Generalized blockmodeling with Pajek. Metodoloski zvezki, 1, 455.

    Google Scholar 

  • Battista GD, Eades P, Tamassia R, Tollis IG (1998) Graph drawing: algorithms for the visualization of graphs. Prentice Hall PTR,

    Google Scholar 

  • Becerra A, Bucheli VA, Moreno PA. (2017). Prediction of virus-host protein-protein interactions mediated by short linear motifs. BMC bioinformatics, 18, 163.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Beltrao P, Bork P, Krogan NJ, van Noort V. (2013). Evolution and functional cross-talk of protein post-translational modifications. Molecular systems biology, 9, 714.

    Article  PubMed  PubMed Central  Google Scholar 

  • Ben-Hur A, Noble WS. (2005). Kernel methods for predicting protein–protein interactions. Bioinformatics, 21, i38-i46.

    Article  CAS  PubMed  Google Scholar 

  • Bergmann S, Ihmels J, Barkai N. (2004). Similarities and differences in genome-wide expression data of six organisms. PLoS biology, 2, E9.

    Article  PubMed  CAS  Google Scholar 

  • Bernauer J, Bahadur RP, Rodier F, Janin J, Poupon A. (2008). DiMoVo: a Voronoi tessellation-based method for discriminating crystallographic and biological protein–protein interactions. Bioinformatics, 24, 652-658.

    Article  CAS  PubMed  Google Scholar 

  • Bertin N et al. (2007). Confirmation of organized modularity in the yeast interactome. PLoS biology, 5, e153.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Bessman MJ, Frick DN, O'Handley SF. (1996). The MutT proteins or “Nudix” hydrolases, a family of versatile, widely distributed,“housecleaning” enzymes. Journal of Biological Chemistry, 271, 25059-25062.

    Google Scholar 

  • Beuming T, Skrabanek L, Niv MY, Mukherjee P, Weinstein H. (2005). PDZBase: a protein-protein interaction database for PDZ-domains. Bioinformatics, 21, 827-828.

    Article  CAS  PubMed  Google Scholar 

  • Bhardwaj N, Lu H. (2005). Correlation between gene expression profiles and protein-protein interactions within and across genomes. Bioinformatics, 21, 2730-2738.

    Article  CAS  PubMed  Google Scholar 

  • Bloom JD, Lu Z, Chen D, Raval A, Venturelli OS, Arnold FH. (2007). Evolution favors protein mutational robustness in sufficiently large populations. BMC biology, 5, 29.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Boccaletti S, Latora V, Moreno Y, Chavez M, Hwang D-U. (2006). Complex networks: Structure and dynamics. Physics reports, 424, 175-308.

    Article  Google Scholar 

  • Bogan AA, Thorn KS. (1998). Anatomy of hot spots in protein interfaces. Journal of molecular biology, 280, 1-9.

    Article  CAS  PubMed  Google Scholar 

  • Bollobás B, Béla B (2001) Random graphs. vol 73. Cambridge university press,

    Google Scholar 

  • Bourgeas R, Basse MJ, Morelli X, Roche P. (2010). Atomic analysis of protein-protein interfaces with known inhibitors: the 2P2I database. PLoS One, 5, e9598.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Breitkreutz A et al. (2010). A global protein kinase and phosphatase interaction network in yeast. Science, 328, 1043-1046.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Breitkreutz BJ et al. (2008). The BioGRID Interaction Database: 2008 update. Nucleic acids research, 36, D637-640.

    Article  CAS  PubMed  Google Scholar 

  • Breitkreutz BJ, Stark C, Tyers M. (2003). Osprey: a network visualization system. Genome Biol, 4, R22.

    Article  PubMed  PubMed Central  Google Scholar 

  • Breuer K et al. (2013). InnateDB: systems biology of innate immunity and beyond--recent updates and continuing curation. Nucleic acids research, 41, D1228-1233.

    Article  CAS  PubMed  Google Scholar 

  • Brown KR, Jurisica I. (2005). Online predicted human interaction database. Bioinformatics, 21, 2076-2082.

    Article  CAS  PubMed  Google Scholar 

  • Brown KR, Jurisica I. (2007). Unequal evolutionary conservation of human protein interactions in interologous networks. Genome biology, 8, R95.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Brown KR et al. (2009). NAViGaTOR: Network Analysis, Visualization and Graphing Toronto. Bioinformatics, 25, 3327-3329.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Bru C, Courcelle E, Carrere S, Beausse Y, Dalmar S, Kahn D. (2005). The ProDom database of protein domain families: more emphasis on 3D. Nucleic acids research, 33, D212-215.

    Article  CAS  PubMed  Google Scholar 

  • Caspi R et al. (2010). The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic acids research, 38, D473-479.

    Article  CAS  PubMed  Google Scholar 

  • Caspi R et al. (2008). The MetaCyc Database of metabolic pathways and enzymes and the BioCyc collection of Pathway/Genome Databases. Nucleic acids research, 36, D623-631.

    Article  CAS  PubMed  Google Scholar 

  • Ceol A, Chatr-aryamontri A, Santonico E, Sacco R, Castagnoli L, Cesareni G. (2007). DOMINO: a database of domain-peptide interactions. Nucleic acids research, 35, D557-560.

    Google Scholar 

  • Ceol A et al. (2010). MINT, the molecular interaction database: 2009 update. Nucleic acids research, 38, D532-539.

    Article  CAS  PubMed  Google Scholar 

  • Cerami EG et al. (2011). Pathway Commons, a web resource for biological pathway data. Nucleic acids research, 39, D685-690.

    Article  CAS  PubMed  Google Scholar 

  • Chatr-aryamontri A, Ceol A, Palazzi LM, Nardelli G, Schneider MV, Castagnoli L, Cesareni G. (2007). MINT: the Molecular INTeraction database. Nucleic acids research, 35, D572-574.

    Google Scholar 

  • Chatr-aryamontri A et al. (2009). VirusMINT: a viral protein interaction database. Nucleic acids research, 37, D669-673.

    Google Scholar 

  • Chaudhury S, Berrondo M, Weitzner BD, Muthu P, Bergman H, Gray JJ. (2011). Benchmarking and analysis of protein docking performance in Rosetta v3. 2. PloS one, 6, e22477.

    Google Scholar 

  • Chen J, Hsu W, Lee ML, Ng SK. (2006a). Increasing confidence of protein interactomes using network topological metrics. Bioinformatics, 22, 1998-2004.

    Article  CAS  PubMed  Google Scholar 

  • Chen JY, Mamidipalli S, Huan T. (2009). HAPPI: an online database of comprehensive human annotated and predicted protein interactions. BMC genomics, 10 Suppl 1, S16.

    Google Scholar 

  • Chen L, Wu LY, Wang Y, Zhang XS. (2006b). Inferring protein interactions from experimental data by association probabilistic method. Proteins, 62, 833-837.

    Article  CAS  PubMed  Google Scholar 

  • Cheng S, Zhang Y, Brooks CL. (2015). PCalign: a method to quantify physicochemical similarity of protein-protein interfaces. BMC bioinformatics, 16, 33.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Cherry JM et al. (2012). Saccharomyces Genome Database: the genomics resource of budding yeast. Nucleic acids research, 40, D700-D705.

    Article  CAS  PubMed  Google Scholar 

  • Chothia C, Janin J. (1975). Principles of protein–protein recognition. Nature, 256, 705-708.

    Article  CAS  PubMed  Google Scholar 

  • Chua HN, Sung W-K, Wong L. (2006). Exploiting indirect neighbours and topological weight to predict protein function from protein–protein interactions. Bioinformatics, 22, 1623-1630.

    Article  CAS  PubMed  Google Scholar 

  • Clark GW, Bezginov A, Yang JM, Charlebois RL, Tillier ER (2011) Using coevolution to predict protein–protein interactions. In: Network Biology. Springer, pp 237-256

    Google Scholar 

  • Cochrane G, Karsch-Mizrachi I, Nakamura Y, International Nucleotide Sequence Database C. (2011). The International Nucleotide Sequence Database Collaboration. Nucleic acids research, 39, D15-18.

    Article  CAS  PubMed  Google Scholar 

  • Comeau SR, Gatchell DW, Vajda S, Camacho CJ. (2004). ClusPro: a fully automated algorithm for protein–protein docking. Nucleic acids research, 32, W96-W99.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Corpet F, Gouzy J, Kahn D. (1998). The ProDom database of protein domain families. Nucleic acids research, 26, 323-326.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Craig RA, Liao L. (2007). Phylogenetic tree information aids supervised learning for predicting protein-protein interaction based on distance matrices. BMC bioinformatics, 8, 1-12.

    Article  CAS  Google Scholar 

  • Croft D et al. (2011). Reactome: a database of reactions, pathways and biological processes. Nucleic acids research, 39, D691-697.

    Article  CAS  PubMed  Google Scholar 

  • Cukuroglu E, Gursoy A, Keskin O. (2012). HotRegion: a database of predicted hot spot clusters. Nucleic acids research, 40, D829-D833.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • Darnell SJ, LeGault L, Mitchell JC. (2008). KFC Server: interactive forecasting of protein interaction hot spots. Nucleic acids research, 36, W265-W269.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Davis AP et al. (2017). The Comparative Toxicogenomics Database: update 2017. Nucleic acids research, 45, D972-D978.

    Article  CAS  PubMed  Google Scholar 

  • Davis FP, Sali A. (2005). PIBASE: a comprehensive database of structurally defined protein interfaces. Bioinformatics, 21, 1901-1907.

    Article  CAS  PubMed  Google Scholar 

  • de Hoon MJ, Imoto S, Nolan J, Miyano S. (2004). Open source clustering software. Bioinformatics, 20, 1453-1454.

    Article  PubMed  CAS  Google Scholar 

  • De Juan D, Pazos F, Valencia A. (2013). Emerging methods in protein coevolution. Nature Reviews Genetics, 14, 249-261.

    Article  PubMed  CAS  Google Scholar 

  • de Matos P et al. (2010). Chemical Entities of Biological Interest: an update. Nucleic acids research, 38, D249-254.

    Article  PubMed  CAS  Google Scholar 

  • De S, Krishnadev O, Srinivasan N, Rekha N. (2005). Interaction preferences across protein-protein interfaces of obligatory and non-obligatory components are different. BMC structural biology, 5, 15.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Deane CM, Salwinski L, Xenarios I, Eisenberg D. (2002). Protein interactions: two methods for assessment of the reliability of high throughput observations. Molecular & cellular proteomics : MCP, 1, 349-356.

    Article  CAS  Google Scholar 

  • Deng L, Zhang QC, Chen Z, Meng Y, Guan J, Zhou S. (2014). PredHS: a web server for predicting protein–protein interaction hot spots by using structural neighborhood properties. Nucleic acids research, 42, W290-W295.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Deng M, Mehta S, Sun F, Chen T. (2002). Inferring domain-domain interactions from protein-protein interactions. Genome research, 12, 1540-1548.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Deng M, Zhang K, Mehta S, Chen T, Sun F. (2003). Prediction of protein function using protein-protein interaction data. Journal of computational biology : a journal of computational molecular cell biology, 10, 947-960.

    Article  CAS  Google Scholar 

  • Deribe YL, Pawson T, Dikic I. (2010). Post-translational modifications in signal integration. Nature structural & molecular biology, 17, 666-672.

    Article  CAS  Google Scholar 

  • Ding Z, Kihara D. (2018). Computational Methods for Predicting Protein-Protein Interactions Using Various Protein Features. Current protocols in protein science, 93, e62.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Dinkel H, Chica C, Via A, Gould CM, Jensen LJ, Gibson TJ, Diella F. (2011). Phospho.ELM: a database of phosphorylation sites--update 2011. Nucleic acids research, 39, D261-267.

    Article  CAS  PubMed  Google Scholar 

  • Dinkel H et al. (2012). ELM--the database of eukaryotic linear motifs. Nucleic acids research, 40, D242-251.

    Article  CAS  PubMed  Google Scholar 

  • Dominguez C, Boelens R, Bonvin AM. (2003). HADDOCK: a protein-protein docking approach based on biochemical or biophysical information. Journal of the American Chemical Society, 125, 1731-1737.

    Article  CAS  PubMed  Google Scholar 

  • Droit A, Hunter JM, Rouleau M, Ethier C, Picard-Cloutier A, Bourgais D, Poirier GG. (2007). PARPs database: a LIMS systems for protein-protein interaction data mining or laboratory information management system. BMC bioinformatics, 8, 483.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Duan G, Walther D. (2015). The roles of post-translational modifications in the context of protein interaction networks. PLoS computational biology, 11, e1004049.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Duan G, Walther D, Schulze WX. (2013). Reconstruction and analysis of nutrient-induced phosphorylation networks in Arabidopsis thaliana. Frontiers in plant science, 4, 540.

    Article  PubMed  PubMed Central  Google Scholar 

  • Dudkina NV, Kouril R, Bultema JB, Boekema EJ. (2010). Imaging of organelles by electron microscopy reveals protein-protein interactions in mitochondria and chloroplasts. FEBS letters, 584, 2510-2515.

    Article  CAS  PubMed  Google Scholar 

  • Dunker AK, Romero P, Obradovic Z, Garner EC, Brown CJ. (2000). Intrinsic protein disorder in complete genomes. Genome informatics, 11, 161-171.

    CAS  PubMed  Google Scholar 

  • Dutkowski J, Tiuryn J. (2007). Identification of functional modules from conserved ancestral protein-protein interactions. Bioinformatics, 23, i149-158.

    Article  CAS  PubMed  Google Scholar 

  • Eades P. (1984). A heuristic for graph drawing. Congressus numerantium, 42, 149-160.

    Google Scholar 

  • Encinar JA, Fernandez-Ballester G, Sánchez IE, Hurtado-Gomez E, Stricher F, Beltrao P, Serrano L. (2009). ADAN: a database for prediction of protein–protein interaction of modular domains mediated by linear motifs. Bioinformatics, 25, 2418-2424.

    Article  CAS  PubMed  Google Scholar 

  • Enright AJ, Iliopoulos I, Kyrpides NC, Ouzounis CA. (1999). Protein interaction maps for complete genomes based on gene fusion events. Nature, 402, 86-90.

    Article  CAS  PubMed  Google Scholar 

  • Enright AJ, Ouzounis CA. (2001a). BioLayout--an automatic graph layout algorithm for similarity visualization. Bioinformatics, 17, 853-854.

    Article  CAS  PubMed  Google Scholar 

  • Enright AJ, Ouzounis CA. (2001b). Functional associations of proteins in entire genomes by means of exhaustive detection of gene fusions. Genome biology, 2, 1-7.

    Article  Google Scholar 

  • Enright AJ, Van Dongen S, Ouzounis CA. (2002). An efficient algorithm for large-scale detection of protein families. Nucleic acids research, 30, 1575-1584.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Finn RD et al. (2017). InterPro in 2017-beyond protein family and domain annotations. Nucleic acids research, 45, D190-D199.

    Article  CAS  PubMed  Google Scholar 

  • Finn RD et al. (2016). The Pfam protein families database: towards a more sustainable future. Nucleic acids research, 44, D279-285.

    Article  CAS  PubMed  Google Scholar 

  • Finn RD, Miller BL, Clements J, Bateman A. (2014). iPfam: a database of protein family and domain interactions found in the Protein Data Bank. Nucleic acids research, 42, D364-D373.

    Article  CAS  PubMed  Google Scholar 

  • Fischer TB et al. (2003). The binding interface database (BID): a compilation of amino acid hot spots in protein interfaces. Bioinformatics, 19, 1453-1454.

    Article  CAS  PubMed  Google Scholar 

  • Flicek P et al. (2010). Ensembl's 10th year. Nucleic acids research, 38, D557-562.

    Article  CAS  PubMed  Google Scholar 

  • Flicek P et al. (2011). Ensembl 2011. Nucleic acids research, 39, D800-806.

    Article  CAS  PubMed  Google Scholar 

  • Folador EL, Hassan SS, Lemke N, Barh D, Silva A, Ferreira RS, Azevedo V. (2014). An improved interolog mapping-based computational prediction of protein–protein interactions with increased network coverage. Integrative Biology, 6, 1080-1087.

    Article  CAS  PubMed  Google Scholar 

  • Franzot G, Carugo O. (2003). Computational approaches to protein-protein interaction. Journal of structural and functional genomics, 4, 245-255.

    Article  CAS  PubMed  Google Scholar 

  • Freeman TC et al. (2007). Construction, visualisation, and clustering of transcription networks from microarray expression data. PLoS computational biology, 3, 2032-2042.

    Article  CAS  PubMed  Google Scholar 

  • Frenkel-Morgenstern M et al. (2013). ChiTaRS: a database of human, mouse and fruit fly chimeric transcripts and RNA-sequencing data. Nucleic acids research, 41, D142-151.

    Article  CAS  PubMed  Google Scholar 

  • Fromont-Racine M, Rain JC, Legrain P. (1997). Toward a functional analysis of the yeast genome through exhaustive two-hybrid screens. Nature genetics, 16, 277-282.

    Article  CAS  PubMed  Google Scholar 

  • Fu W, Sanders-Beer BE, Katz KS, Maglott DR, Pruitt KD, Ptak RG. (2009). Human immunodeficiency virus type 1, human protein interaction database at NCBI. Nucleic acids research, 37, D417-422.

    Article  CAS  PubMed  Google Scholar 

  • Gao M, Skolnick J. (2010). iAlign: a method for the structural comparison of protein–protein interfaces. Bioinformatics, 26, 2259-2265.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • 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, W147-151.

    Google Scholar 

  • Gavin AC et al. (2002). Functional organization of the yeast proteome by systematic analysis of protein complexes. Nature, 415, 141-147.

    Article  CAS  PubMed  Google Scholar 

  • Ge H, Liu Z, Church GM, Vidal M. (2001). Correlation between transcriptome and interactome mapping data from Saccharomyces cerevisiae. Nature genetics, 29, 482-486.

    Article  CAS  PubMed  Google Scholar 

  • Geisler-Lee J, O'Toole N, Ammar R, Provart NJ, Millar AH, Geisler M. (2007). A predicted interactome for Arabidopsis. Plant physiology, 145, 317-329.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Gene Ontology C. (2010). The Gene Ontology in 2010: extensions and refinements. Nucleic acids research, 38, D331-335.

    Article  CAS  Google Scholar 

  • Gerber D, Maerkl SJ, Quake SR. (2009). An in vitro microfluidic approach to generating protein-interaction networks. Nature methods, 6, 71-74.

    Article  CAS  PubMed  Google Scholar 

  • Ghoorah AW, Devignes M-D, Smaïl-Tabbone M, Ritchie DW. (2014). KBDOCK 2013: a spatial classification of 3D protein domain family interactions. Nucleic acids research, 42, D389-D395.

    Article  CAS  PubMed  Google Scholar 

  • Gilson MK, Liu T, Baitaluk M, Nicola G, Hwang L, Chong J. (2016). BindingDB in 2015: A public database for medicinal chemistry, computational chemistry and systems pharmacology. Nucleic acids research, 44, D1045-1053.

    Article  CAS  PubMed  Google Scholar 

  • Giurgiu M et al. (2019). CORUM: the comprehensive resource of mammalian protein complexes-2019. Nucleic acids research, 47, D559-D563.

    Article  CAS  PubMed  Google Scholar 

  • Goel R, Harsha HC, Pandey A, Prasad TS. (2012). Human Protein Reference Database and Human Proteinpedia as resources for phosphoproteome analysis. Molecular bioSystems, 8, 453-463.

    Article  CAS  PubMed  Google Scholar 

  • Goh C-S, Bogan AA, Joachimiak M, Walther D, Cohen FE. (2000). Coevolution of proteins with their interaction partners. Journal of molecular biology, 299, 283-293.

    Article  CAS  PubMed  Google Scholar 

  • Goh C-S, Cohen FE. (2002). Coevolutionary analysis reveals insights into protein–protein interactions. Journal of molecular biology, 324, 177-192.

    Article  CAS  PubMed  Google Scholar 

  • Goldberg DS, Roth FP. (2003). Assessing experimentally derived interactions in a small world. Proceedings of the National Academy of Sciences, 100, 4372-4376.

    Google Scholar 

  • Gomez SM, Noble WS, Rzhetsky A. (2003). Learning to predict protein–protein interactions from protein sequences. Bioinformatics, 19, 1875-1881.

    Article  CAS  PubMed  Google Scholar 

  • Gong S et al. (2005). A protein domain interaction interface database: InterPare. BMC bioinformatics, 6, 207.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • González-Ruiz D, Gohlke H. (2006). Targeting protein-protein interactions with small molecules: challenges and perspectives for omputational binding epitope detection and ligand finding. Current medicinal chemistry, 13, 2607-2625.

    Article  PubMed  Google Scholar 

  • Goodman N et al. (2003). Plans for HDBase—a research community website for Huntington's Disease. Clinical Neuroscience Research, 3, 197-217.

    Article  Google Scholar 

  • Gramates LS et al. (2017). FlyBase at 25: looking to the future. Nucleic acids research, 45, D663-D671.

    Article  CAS  PubMed  Google Scholar 

  • Grigoriev A. (2001). A relationship between gene expression and protein interactions on the proteome scale: analysis of the bacteriophage T7 and the yeast Saccharomyces cerevisiae. Nucleic acids research, 29, 3513-3519.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Grindrod P, Kibble M. (2004). Review of uses of network and graph theory concepts within proteomics. Expert review of proteomics, 1, 229-238.

    Article  CAS  PubMed  Google Scholar 

  • Gu H, Zhu P, Jiao Y, Meng Y, Chen M. (2011). PRIN: a predicted rice interactome network. BMC bioinformatics, 12, 161.

    Article  PubMed  PubMed Central  Google Scholar 

  • Gulati K, Gangele K, Agarwal N, Jamsandekar M, Kumar D, Poluri KM. (2018). Molecular cloning and biophysical characterization of CXCL3 chemokine. International journal of biological macromolecules, 107, 575-584.

    Article  CAS  PubMed  Google Scholar 

  • Guney E, Tuncbag N, Keskin O, Gursoy A. (2007). HotSprint: database of computational hot spots in protein interfaces. Nucleic acids research, 36, D662-D666.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Günther S, May P, Hoppe A, Frömmel C, Preissner R. (2007). Docking without docking: ISEARCH—prediction of interactions using known interfaces. Proteins: Structure, Function, and Bioinformatics, 69, 839-844.

    Article  CAS  Google Scholar 

  • 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, 3025-3030.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Hakes L, Lovell SC, Oliver SG, Robertson DL. (2007). Specificity in protein interactions and its relationship with sequence diversity and coevolution. Proceedings of the National Academy of Sciences, 104, 7999-8004.

    Google Scholar 

  • Haliloglu T, Ben-Tal N, Garzon J, Ozbek P, Soner S. (2015). DynaFace: Discrimination between Obligatory and Non-obligatory Protein-Protein Interactions Based on the Complex’s Dynamics.

    Google Scholar 

  • Hamp T, Rost B. (2015). Evolutionary profiles improve protein-protein interaction prediction from sequence. Bioinformatics, 31, 1945-1950.

    Article  CAS  PubMed  Google Scholar 

  • Han K, Park B, Kim H, Hong J, Park J. (2004). HPID: the human protein interaction database. Bioinformatics, 20, 2466-2470.

    Article  CAS  PubMed  Google Scholar 

  • Hashemifar S, Neyshabur B, Khan AA, Xu J. (2018). Predicting protein-protein interactions through sequence-based deep learning. Bioinformatics, 34, i802-i810.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Hastings J et al. (2013). The ChEBI reference database and ontology for biologically relevant chemistry: enhancements for 2013. Nucleic acids research, 41, D456-463.

    Article  CAS  PubMed  Google Scholar 

  • Hayashida M, Kamada M, Song J, Akutsu T. (2011). Conditional random field approach to prediction of protein-protein interactions using domain information. BMC systems biology, 5 Suppl 1, S8.

    Google Scholar 

  • Henrick K, Thornton JM. (1998). PQS: a protein quaternary structure file server. Trends in biochemical sciences, 23, 358-361.

    Article  CAS  PubMed  Google Scholar 

  • Herman D, Ochoa D, Juan D, Lopez D, Valencia A, Pazos F. (2011). Selection of organisms for the coevolution-based study of protein interactions. BMC bioinformatics, 12, 363.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Hermjakob H et al. (2004a). The HUPO PSI's molecular interaction format--a community standard for the representation of protein interaction data. Nature biotechnology, 22, 177-183.

    Article  CAS  PubMed  Google Scholar 

  • Hermjakob H et al. (2004b). IntAct: an open source molecular interaction database. Nucleic acids research, 32, D452-455.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Higurashi M, Ishida T, Kinoshita K. (2009). PiSite: a database of protein interaction sites using multiple binding states in the PDB. Nucleic acids research, 37, D360-D364.

    Article  CAS  PubMed  Google Scholar 

  • Ho Y et al. (2002). Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry. Nature, 415, 180-183.

    Article  CAS  PubMed  Google Scholar 

  • Hooper SD, Bork P. (2005). Medusa: a simple tool for interaction graph analysis. Bioinformatics, 21, 4432-4433.

    Article  CAS  PubMed  Google Scholar 

  • Hopf TA et al. (2014). Sequence coevolution gives 3D contacts and structures of protein complexes. Elife, 3, e03430.

    Article  PubMed Central  Google Scholar 

  • Horner DS, Pirovano W, Pesole G. (2008). Correlated substitution analysis and the prediction of amino acid structural contacts. Briefings in bioinformatics, 9, 46-56.

    Article  CAS  PubMed  Google Scholar 

  • Hoskins J, Lovell S, Blundell TL. (2006). An algorithm for predicting protein–protein interaction sites: abnormally exposed amino acid residues and secondary structure elements. Protein Science, 15, 1017-1029.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Hosur R, Xu J, Bienkowska J, Berger B. (2011). iWRAP: an interface threading approach with application to prediction of cancer-related protein–protein interactions. Journal of molecular biology, 405, 1295-1310.

    Article  CAS  PubMed  Google Scholar 

  • Howe K et al. WormBase: annotating many nematode genomes. In: Worm, 2012. vol 1. Taylor & Francis, pp 15-21

    Google Scholar 

  • Hu Z, Ma B, Wolfson H, Nussinov R. (2000). Conservation of polar residues as hot spots at protein interfaces. Proteins, 39, 331-342.

    Article  CAS  PubMed  Google Scholar 

  • Huang TW et al. (2004). POINT: a database for the prediction of protein-protein interactions based on the orthologous interactome. Bioinformatics, 20, 3273-3276.

    Article  CAS  PubMed  Google Scholar 

  • Huttenhower C et al. (2009). Detailing regulatory networks through large scale data integration. Bioinformatics, 25, 3267-3274.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Hwang H, Vreven T, Janin J, Weng Z. (2010). Protein-protein docking benchmark version 4.0. Proteins, 78, 3111-3114.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Inbar Y, Benyamini H, Nussinov R, Wolfson HJ. (2005). Combinatorial docking approach for structure prediction of large proteins and multi-molecular assemblies. Physical biology, 2, S156.

    Article  CAS  PubMed  Google Scholar 

  • International HapMap C et al. (2007). A second generation human haplotype map of over 3.1 million SNPs. Nature, 449, 851-861.

    Google Scholar 

  • Iragne F, Nikolski M, Mathieu B, Auber D, Sherman D. (2005). ProViz: protein interaction visualization and exploration. Bioinformatics, 21, 272-274.

    Article  CAS  PubMed  Google Scholar 

  • Ito T, Chiba T, Ozawa R, Yoshida M, Hattori M, Sakaki Y. (2001). A comprehensive two-hybrid analysis to explore the yeast protein interactome. Proceedings of the National Academy of Sciences of the United States of America, 98, 4569-4574.

    Google Scholar 

  • Izarzugaza JM, Juan D, Pons C, Pazos F, Valencia A. (2008). Enhancing the prediction of protein pairings between interacting families using orthology information. BMC bioinformatics, 9, 35.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Jaeger S, Sers CT, Leser U. (2010). Combining modularity, conservation, and interactions of proteins significantly increases precision and coverage of protein function prediction. BMC genomics, 11, 717.

    Article  PubMed  PubMed Central  Google Scholar 

  • Jain E et al. (2009). Infrastructure for the life sciences: design and implementation of the UniProt website. BMC bioinformatics, 10, 136.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Janin J. (2010). Protein-protein docking tested in blind predictions: the CAPRI experiment. Molecular bioSystems, 6, 2351-2362.

    Article  CAS  PubMed  Google Scholar 

  • Janin J, Chothia C. (1990). The structure of protein-protein recognition sites. Journal of Biological Chemistry, 265, 16027-16030.

    Article  CAS  PubMed  Google Scholar 

  • Janin J, Miller S, Chothia C. (1988). Surface, subunit interfaces and interior of oligomeric proteins. Journal of molecular biology, 204, 155-164.

    Article  CAS  PubMed  Google Scholar 

  • Jansen R, Greenbaum D, Gerstein M. (2002). Relating whole-genome expression data with protein-protein interactions. Genome research, 12, 37-46.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Jayapandian M et al. (2007). Michigan Molecular Interactions (MiMI): putting the jigsaw puzzle together. Nucleic acids research, 35, D566-571.

    Article  CAS  PubMed  Google Scholar 

  • Jefferson ER, Walsh TP, Roberts TJ, Barton GJ. (2007). SNAPPI-DB: a database and API of Structures, iNterfaces and Alignments for Protein-Protein Interactions. Nucleic acids research, 35, D580-589.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Jensen LJ et al. (2009). STRING 8--a global view on proteins and their functional interactions in 630 organisms. Nucleic acids research, 37, D412-416.

    Article  CAS  PubMed  Google Scholar 

  • Jeon C, Agarwal K. (1996). Fidelity of RNA polymerase II transcription controlled by elongation factor TFIIS. Proceedings of the National Academy of Sciences, 93, 13677-13682.

    Google Scholar 

  • Ji ZL et al. (2003). KDBI: kinetic data of bio-molecular interactions database. Nucleic acids research, 31, 255-257.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Jiménez-García B, Pons C, Fernández-Recio J. (2013). pyDockWEB: a web server for rigid-body protein–protein docking using electrostatics and desolvation scoring. Bioinformatics, 29, 1698-1699.

    Article  PubMed  CAS  Google Scholar 

  • Jones S, Marin A, M. Thornton J. (2000). Protein domain interfaces: characterization and comparison with oligomeric protein interfaces. Protein Engineering, 13, 77-82.

    Article  CAS  PubMed  Google Scholar 

  • Jones S, Thornton JM. (1995). Protein-protein interactions: a review of protein dimer structures. Progress in biophysics and molecular biology, 63, 31-65.

    Article  CAS  PubMed  Google Scholar 

  • Jones S, Thornton JM. (1996). Principles of protein-protein interactions. Proceedings of the National Academy of Sciences, 93, 13-20.

    Google Scholar 

  • Jones S, Thornton JM. (1997). Analysis of protein-protein interaction sites using surface patches. Journal of molecular biology, 272, 121-132.

    Article  CAS  PubMed  Google Scholar 

  • Jothi R, Cherukuri PF, Tasneem A, Przytycka TM. (2006). Coevolutionary analysis of domains in interacting proteins reveals insights into domain–domain interactions mediating protein–protein interactions. Journal of molecular biology, 362, 861-875.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Jothi R, Kann MG, Przytycka TM. (2005). Predicting protein–protein interaction by searching evolutionary tree automorphism space. Bioinformatics, 21, i241-i250.

    Article  CAS  PubMed  Google Scholar 

  • Juan D, Pazos F, Valencia A. (2008a). Coevolution and co-adaptation in protein networks. FEBS letters, 582, 1225-1230.

    Article  CAS  PubMed  Google Scholar 

  • Juan D, Pazos F, Valencia A. (2008b). High-confidence prediction of global interactomes based on genome-wide coevolutionary networks. Proceedings of the National Academy of Sciences, 105, 934-939.

    Google Scholar 

  • Kalathur RKR, Pinto JP, Hernandez-Prieto MA, Machado RS, Almeida D, Chaurasia G, Futschik ME. (2014). UniHI 7: an enhanced database for retrieval and interactive analysis of human molecular interaction networks. Nucleic acids research, 42, D408-D414.

    Article  CAS  PubMed  Google Scholar 

  • Kamada M, Sakuma Y, Hayashida M, Akutsu T. (2014). Prediction of protein-protein interaction strength using domain features with supervised regression. TheScientificWorldJournal, 2014, 240673.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Kamburov A, Pentchev K, Galicka H, Wierling C, Lehrach H, Herwig R. (2011). ConsensusPathDB: toward a more complete picture of cell biology. Nucleic acids research, 39, D712-717.

    Article  CAS  PubMed  Google Scholar 

  • Kandasamy K et al. (2010). NetPath: a public resource of curated signal transduction pathways. Genome Biol, 11, R3.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Kanehisa M. (1997). A database for post-genome analysis. Trends in genetics : TIG, 13, 375-376.

    Article  CAS  PubMed  Google Scholar 

  • Kanehisa M, Furumichi M, Tanabe M, Sato Y, Morishima K. (2017). KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic acids research, 45, D353-D361.

    Article  CAS  PubMed  Google Scholar 

  • Kanehisa M, Goto S, Furumichi M, Tanabe M, Hirakawa M. (2010). KEGG for representation and analysis of molecular networks involving diseases and drugs. Nucleic acids research, 38, D355-360.

    Article  CAS  PubMed  Google Scholar 

  • Kann MG, Jothi R, Cherukuri PF, Przytycka TM. (2007). Predicting protein domain interactions from coevolution of conserved regions. Proteins: Structure, Function, and Bioinformatics, 67, 811-820.

    Article  CAS  Google Scholar 

  • Kar G, Gursoy A, Keskin O. (2009). Human cancer protein-protein interaction network: a structural perspective. PLoS computational biology, 5, e1000601.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Kara A, Vickers M, Swain M, Whitworth DE, Fernandez-Fuentes N. (2016). MetaPred2CS: a sequence-based meta-predictor for protein-protein interactions of prokaryotic two-component system proteins. Bioinformatics, 32, 3339-3341.

    Article  CAS  PubMed  Google Scholar 

  • Kastritis PL, Moal IH, Hwang H, Weng Z, Bates PA, Bonvin AM, Janin J. (2011). A structure-based benchmark for protein–protein binding affinity. Protein Science, 20, 482-491.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Kenworthy AK. (2001). Imaging protein-protein interactions using fluorescence resonance energy transfer microscopy. Methods, 24, 289-296.

    Article  CAS  PubMed  Google Scholar 

  • Kerrien S et al. (2012). The IntAct molecular interaction database in 2012. Nucleic acids research, 40, D841-846.

    Article  CAS  PubMed  Google Scholar 

  • Kersey PJ et al. (2010). Ensembl Genomes: extending Ensembl across the taxonomic space. Nucleic acids research, 38, D563-569.

    Article  CAS  PubMed  Google Scholar 

  • Keshava Prasad TS et al. (2009). Human Protein Reference Database--2009 update. Nucleic acids research, 37, D767-772.

    Article  CAS  PubMed  Google Scholar 

  • Keskin O, Gursoy A, Ma B, Nussinov RJCr. (2008). Principles of protein− protein interactions: What are the preferred ways for proteins to interact? Chem Rev, 108, 1225-1244.

    Google Scholar 

  • Keskin O, Ma B, Nussinov R. (2005). Hot regions in protein–protein interactions: the organization and contribution of structurally conserved hot spot residues. Journal of molecular biology, 345, 1281-1294.

    Article  CAS  PubMed  Google Scholar 

  • Keskin O, Tsai CJ, Wolfson H, Nussinov R. (2004). A new, structurally nonredundant, diverse data set of protein–protein interfaces and its implications. Protein Science, 13, 1043-1055.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Keskin O, Tuncbag N, Gursoy A. (2016). Predicting protein–protein interactions from the molecular to the proteome level. Chemical reviews, 116, 4884-4909.

    Article  CAS  PubMed  Google Scholar 

  • Kiefer F, Arnold K, Kunzli M, Bordoli L, Schwede T. (2009). The SWISS-MODEL Repository and associated resources. Nucleic acids research, 37, D387-392.

    Article  CAS  PubMed  Google Scholar 

  • Kim DE, Chivian D, Baker D. (2004). Protein structure prediction and analysis using the Robetta server. Nucleic acids research, 32, W526-W531.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Kim WK, Park J, Suh JK. (2002). Large scale statistical prediction of protein-protein interaction by potentially interacting domain (PID) pair. Genome informatics. International Conference on Genome Informatics, 13, 42-50.

    Google Scholar 

  • Kobe B et al. (2008). Crystallography and protein–protein interactions: biological interfaces and crystal contacts. Biochemical Society Transactions, 36, 1438-1441.

    Article  CAS  PubMed  Google Scholar 

  • Kortemme T, Baker D. (2002). A simple physical model for binding energy hot spots in protein–protein complexes. Proceedings of the National Academy of Sciences, 99, 14116-14121.

    Google Scholar 

  • 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, D536-541.

    Article  CAS  PubMed  Google Scholar 

  • Kozakov D, Brenke R, Comeau SR, Vajda S. (2006). PIPER: an FFT-based protein docking program with pairwise potentials. Proteins: Structure, Function, and Bioinformatics, 65, 392-406.

    Article  CAS  Google Scholar 

  • Kozakov D et al. (2015). The FTMap family of web servers for determining and characterizing ligand-binding hot spots of proteins. Nature protocols, 10, 733-755.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Krüger DM, Gohlke H. (2010). DrugScorePPI webserver: fast and accurate in silico alanine scanning for scoring protein–protein interactions. Nucleic acids research, 38, W480-W486.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Krissinel E, Henrick K. (2007). Inference of macromolecular assemblies from crystalline state. Journal of molecular biology, 372, 774-797.

    Article  CAS  PubMed  Google Scholar 

  • Kuchaiev O, Rasajski M, Higham DJ, Przulj N. (2009). Geometric de-noising of protein-protein interaction networks. PLoS computational biology, 5, e1000454.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Kuhn M, Szklarczyk D, Franceschini A, von Mering C, Jensen LJ, Bork P. (2012). STITCH 3: zooming in on protein-chemical interactions. Nucleic acids research, 40, D876-880.

    Article  CAS  PubMed  Google Scholar 

  • Kuiken C, Korber B, Shafer RW. (2003). HIV sequence databases. AIDS reviews, 5, 52-61.

    PubMed  PubMed Central  Google Scholar 

  • Kumar MS, Gromiha MM. (2006). PINT: protein–protein interactions thermodynamic database. Nucleic acids research, 34, D195-D198.

    Article  CAS  PubMed  Google Scholar 

  • Kundrotas PJ, Alexov E. (2007). PROTCOM: searchable database of protein complexes enhanced with domain-domain structures. Nucleic acids research, 35, D575-579.

    Article  CAS  PubMed  Google Scholar 

  • Kundrotas PJ, Anishchenko I, Dauzhenka T, Kotthoff I, Mnevets D, Copeland MM, Vakser IA. (2018). Dockground: a comprehensive data resource for modeling of protein complexes. Protein Science, 27, 172-181.

    Article  CAS  PubMed  Google Scholar 

  • Kundrotas PJ, Zhu Z, Janin J, Vakser IA. (2012). Templates are available to model nearly all complexes of structurally characterized proteins. Proceedings of the National Academy of Sciences, 109, 9438-9441.

    Google Scholar 

  • Kwon D et al. (2012). A comprehensive manually curated protein–protein interaction database for the Death Domain superfamily. Nucleic acids research, 40, D331-D336.

    Article  CAS  PubMed  Google Scholar 

  • Laskowski RA, Jablonska J, Pravda L, Varekova RS, Thornton JM. (2018). PDBsum: Structural summaries of PDB entries. Protein science : a publication of the Protein Society, 27, 129-134.

    Article  CAS  Google Scholar 

  • Leader DP, Krause SA, Pandit A, Davies SA, Dow JAT. (2018). FlyAtlas 2: a new version of the Drosophila melanogaster expression atlas with RNA-Seq, miRNA-Seq and sex-specific data. Nucleic acids research, 46, D809-D815.

    Article  CAS  PubMed  Google Scholar 

  • Lechner M et al. (2012). CIDeR: multifactorial interaction networks in human diseases. Genome biology, 13, R62.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Lee S-A, Chan C-h, Tsai C-H, Lai J-M, Wang F-S, Kao C-Y, Huang C-YF. (2008). Ortholog-based protein-protein interaction prediction and its application to inter-species interactions. BMC bioinformatics, 9, S11.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Lei C, Ruan J A random walk based approach for improving protein-protein interaction network and protein complex prediction. In: 2012 IEEE International Conference on Bioinformatics and Biomedicine, 2012. IEEE, pp 1-6

    Google Scholar 

  • Letamendia A, Labbe E, Attisano L. (2001). Transcriptional regulation by Smads: crosstalk between the TGF-beta and Wnt pathways. The Journal of bone and joint surgery. American volume, 83-A Suppl 1, S31-39.

    Google Scholar 

  • Letunic I, Doerks T, Bork P. (2009). SMART 6: recent updates and new developments. Nucleic acids research, 37, D229-232.

    Article  CAS  PubMed  Google Scholar 

  • Li D et al. (2006). Protein interaction networks of Saccharomyces cerevisiae, Caenorhabditis elegans and Drosophila melanogaster: large-scale organization and robustness. Proteomics, 6, 456-461.

    Article  CAS  PubMed  Google Scholar 

  • Li H, Yang S, Wang C, Zhou Y, Zhang Z. (2016). AraPPISite: a database of fine-grained protein-protein interaction site annotations for Arabidopsis thaliana. Plant molecular biology, 92, 105-116.

    Article  CAS  PubMed  Google Scholar 

  • Li X, Keskin O, Ma B, Nussinov R, Liang J. (2004). Protein–protein interactions: hot spots and structurally conserved residues often locate in complemented pockets that pre-organized in the unbound states: implications for docking. Journal of molecular biology, 344, 781-795.

    Article  CAS  PubMed  Google Scholar 

  • Li X, Yang L, Zhang X, Jiao X. (2019). Prediction of Protein-Protein Interactions Based on Domain. Computational and mathematical methods in medicine, 2019, 5238406.

    Article  PubMed  PubMed Central  Google Scholar 

  • Lin J-S, Lai E-M (2017) Protein–protein interactions: co-immunoprecipitation. In: Bacterial Protein Secretion Systems. Springer, pp 211-219

    Google Scholar 

  • Lin T-W, Wu J-W, Chang DT-H. (2013). Combining phylogenetic profiling-based and machine learning-based techniques to predict functional related proteins. PloS one, 8, e75940.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Liu G, Li J, Wong L. (2008). Assessing and predicting protein interactions using both local and global network topological metrics. Genome informatics. International Conference on Genome Informatics, 21, 138-149.

    Google Scholar 

  • Liu T, Lin Y, Wen X, Jorissen RN, Gilson MK. (2007). BindingDB: a web-accessible database of experimentally determined protein-ligand binding affinities. Nucleic acids research, 35, D198-201.

    Article  CAS  PubMed  Google Scholar 

  • Lo Conte L, Chothia C, Janin J. (1999). The atomic structure of protein-protein recognition sites. J Mol Biol, 285, 2177-2198.

    Article  CAS  PubMed  Google Scholar 

  • Lopes CT, Franz M, Kazi F, Donaldson SL, Morris Q, Bader GD. (2010). Cytoscape Web: an interactive web-based network browser. Bioinformatics, 26, 2347-2348.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Lopez Y, Nakai K, Patil A. (2015). HitPredict version 4: comprehensive reliability scoring of physical protein-protein interactions from more than 100 species. Database : the journal of biological databases and curation, 2015.

    Google Scholar 

  • Lu CT et al. (2013). DbPTM 3.0: an informative resource for investigating substrate site specificity and functional association of protein post-translational modifications. Nucleic acids research, 41, D295-305.

    Article  CAS  PubMed  Google Scholar 

  • Lua RC, Marciano DC, Katsonis P, Adikesavan AK, Wilkins AD, Lichtarge O. (2014). Prediction and redesign of protein–protein interactions. Progress in biophysics and molecular biology, 116, 194-202.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Ma B, Elkayam T, Wolfson H, Nussinov R. (2003). Protein-protein interactions: structurally conserved residues distinguish between binding sites and exposed protein surfaces. Proceedings of the National Academy of Sciences of the United States of America, 100, 5772-5777.

    Google Scholar 

  • Macindoe G, Mavridis L, Venkatraman V, Devignes M-D, Ritchie DW. (2010). HexServer: an FFT-based protein docking server powered by graphics processors. Nucleic acids research, 38, W445-W449.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Marcotte EM, Pellegrini M, Ng H-L, Rice DW, Yeates TO, Eisenberg D. (1999). Detecting protein function and protein-protein interactions from genome sequences. Science, 285, 751-753.

    Article  CAS  PubMed  Google Scholar 

  • Marsh JA, Hernandez H, Hall Z, Ahnert SE, Perica T, Robinson CV, Teichmann SA. (2013). Protein complexes are under evolutionary selection to assemble via ordered pathways. Cell, 153, 461-470.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Martin S, Roe D, Faulon J-L. (2005). Predicting protein–protein interactions using signature products. Bioinformatics, 21, 218-226.

    Article  CAS  PubMed  Google Scholar 

  • Mashiach E, Nussinov R, Wolfson HJ. (2010). FiberDock: flexible induced-fit backbone refinement in molecular docking. Proteins: Structure, Function, and Bioinformatics, 78, 1503-1519.

    Article  CAS  Google Scholar 

  • Matthews LR et al. (2001). Identification of potential interaction networks using sequence-based searches for conserved protein-protein interactions or "interologs". Genome research, 11, 2120-2126.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • McDowall MD et al. (2015). PomBase 2015: updates to the fission yeast database. Nucleic acids research, 43, D656-661.

    Article  CAS  PubMed  Google Scholar 

  • McDowall MD, Scott MS, Barton GJ. (2009). PIPs: human protein-protein interaction prediction database. Nucleic acids research, 37, D651-656.

    Article  CAS  PubMed  Google Scholar 

  • Meireles LM, Dömling AS, Camacho CJ. (2010). ANCHOR: a web server and database for analysis of protein–protein interaction binding pockets for drug discovery. Nucleic acids research, 38, W407-W411.

    Google Scholar 

  • Merico D, Gfeller D, Bader GD. (2009). How to visually interpret biological data using networks. Nature biotechnology, 27, 921-924.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Mi H, Dong Q, Muruganujan A, Gaudet P, Lewis S, Thomas PD. (2010). PANTHER version 7: improved phylogenetic trees, orthologs and collaboration with the Gene Ontology Consortium. Nucleic acids research, 38, D204-210.

    Article  CAS  PubMed  Google Scholar 

  • Mintseris J, Weng Z. (2005). Structure, function, and evolution of transient and obligate protein–protein interactions. Proceedings of the National Academy of Sciences, 102, 10930-10935.

    Google Scholar 

  • Mishra GR et al. (2006). Human protein reference database--2006 update. Nucleic acids research, 34, D411-414.

    Article  CAS  PubMed  Google Scholar 

  • Moal IH, Fernández-Recio J. (2012). SKEMPI: a Structural Kinetic and Energetic database of Mutant Protein Interactions and its use in empirical models. Bioinformatics, 28, 2600-2607.

    Article  CAS  PubMed  Google Scholar 

  • Moreira IS, Fernandes PA, Ramos MJ. (2007). Hot spots—A review of the protein–protein interface determinant amino-acid residues. Proteins: Structure, Function, and Bioinformatics, 68, 803-812.

    Article  CAS  Google Scholar 

  • Moreira IS et al. (2017). SpotOn: high accuracy identification of protein-protein interface hot-spots. Scientific reports, 7, 1-11.

    Article  CAS  Google Scholar 

  • Morilla I, Lees JG, Reid AJ, Orengo C, Ranea JA. (2010). Assessment of protein domain fusions in human protein interaction networks prediction: application to the human kinetochore model. New biotechnology, 27, 755-765.

    Article  CAS  PubMed  Google Scholar 

  • Mosca R, Ceol A, Aloy P. (2013). Interactome3D: adding structural details to protein networks. Nature methods, 10, 47-53.

    Article  CAS  PubMed  Google Scholar 

  • Mosca R, Ceol A, Stein A, Olivella R, Aloy P. (2014). 3did: a catalog of domain-based interactions of known three-dimensional structure. Nucleic acids research, 42, D374-D379.

    Article  CAS  PubMed  Google Scholar 

  • Mostafavi S, Ray D, Warde-Farley D, Grouios C, Morris Q. (2008). GeneMANIA: a real-time multiple association network integration algorithm for predicting gene function. Genome Biol, 9 Suppl 1, S4.

    Google Scholar 

  • Mrvar A, Batagelj V. (2016). Analysis and visualization of large networks with program package Pajek. Complex Adaptive Systems Modeling, 4, 6.

    Article  Google Scholar 

  • Muley VY, Ranjan A. (2013). Evaluation of physical and functional protein-protein interaction prediction methods for detecting biological pathways. PLoS One, 8, e54325.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Murali T, Pacifico S, Yu J, Guest S, Roberts GG, 3rd, Finley RL, Jr. (2011). DroID 2011: a comprehensive, integrated resource for protein, transcription factor, RNA and gene interactions for Drosophila. Nucleic acids research, 39, D736-743.

    Article  CAS  PubMed  Google Scholar 

  • Ng SK, Zhang Z, Tan SH, Lin K. (2003). InterDom: a database of putative interacting protein domains for validating predicted protein interactions and complexes. Nucleic acids research, 31, 251-254.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Ngounou Wetie AG, Sokolowska I, Woods AG, Roy U, Loo JA, Darie CC. (2013). Investigation of stable and transient protein-protein interactions: Past, present, and future. Proteomics, 13, 538-557.

    Article  CAS  PubMed  Google Scholar 

  • Nikolovska-Coleska Z (2015) Studying protein-protein interactions using surface plasmon resonance. In: Protein-Protein Interactions. Springer, pp 109-138

    Book  Google Scholar 

  • Nishi H, Hashimoto K, Panchenko AR. (2011). Phosphorylation in protein-protein binding: effect on stability and function. Structure, 19, 1807-1815.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Nooren IM, Thornton JM. (2003a). Diversity of protein–protein interactions. The EMBO journal, 22, 3486-3492.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Nooren IM, Thornton JM. (2003b). Structural characterisation and functional significance of transient protein-protein interactions. J Mol Biol, 325, 991-1018.

    Article  CAS  PubMed  Google Scholar 

  • Nowotka MM, Gaulton A, Mendez D, Bento AP, Hersey A, Leach A. (2017). Using ChEMBL web services for building applications and data processing workflows relevant to drug discovery. Expert opinion on drug discovery, 12, 757-767.

    PubMed  PubMed Central  Google Scholar 

  • Ofran Y, Rost B. (2007). Protein–protein interaction hotspots carved into sequences. PLoS computational biology, 3, e119.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Ohue M, Shimoda T, Suzuki S, Matsuzaki Y, Ishida T, Akiyama Y. (2014). MEGADOCK 4.0: an ultra–high-performance protein–protein docking software for heterogeneous supercomputers. Bioinformatics, 30, 3281-3283.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Okamura Y, Aoki Y, Obayashi T, Tadaka S, Ito S, Narise T, Kinoshita K. (2015). COXPRESdb in 2015: coexpression database for animal species by DNA-microarray and RNAseq-based expression data with multiple quality assessment systems. Nucleic acids research, 43, D82-86.

    Article  CAS  PubMed  Google Scholar 

  • Olmsted S, Erlandsen S, Dunny GM, Wells CL. (1993). High-resolution visualization by field emission scanning electron microscopy of Enterococcus faecalis surface proteins encoded by the pheromone-inducible conjugative plasmid pCF10. Journal of bacteriology, 175, 6229-6237.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Orchard S, Hermjakob H. (2008). The HUPO proteomics standards initiative--easing communication and minimizing data loss in a changing world. Brief Bioinform, 9, 166-173.

    Article  CAS  PubMed  Google Scholar 

  • Orchard S et al. (2012). Protein interaction data curation: the International Molecular Exchange (IMEx) consortium. Nature methods, 9, 345-350.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Orlev N, Shamir R, Shiloh Y. (2004). PIVOT: protein interacions visualizatiOn tool. Bioinformatics, 20, 424-425.

    Article  CAS  PubMed  Google Scholar 

  • Oughtred R et al. (2019). The BioGRID interaction database: 2019 update. Nucleic acids research, 47, D529-D541.

    Article  CAS  PubMed  Google Scholar 

  • Ovchinnikov S, Kamisetty H, Baker D. (2014). Robust and accurate prediction of residue–residue interactions across protein interfaces using evolutionary information. Elife, 3, e02030.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Overbeek R, Fonstein M, D’souza M, Pusch GD, Maltsev N. (1999). The use of gene clusters to infer functional coupling. Proceedings of the National Academy of Sciences, 96, 2896-2901.

    Google Scholar 

  • Overington J. (2009). ChEMBL. An interview with John Overington, team leader, chemogenomics at the European Bioinformatics Institute Outstation of the European Molecular Biology Laboratory (EMBL-EBI). Interview by Wendy A. Warr. Journal of computer-aided molecular design, 23, 195-198.

    Article  PubMed  CAS  Google Scholar 

  • Panchaud A, Singh P, Shaffer SA, Goodlett DR. (2010). xComb: a cross-linked peptide database approach to protein-protein interaction analysis. Journal of proteome research, 9, 2508-2515.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Park D, Singh R, Baym M, Liao CS, Berger B. (2011). IsoBase: a database of functionally related proteins across PPI networks. Nucleic acids research, 39, D295-300.

    Article  CAS  PubMed  Google Scholar 

  • Pathan M et al. (2015). FunRich: An open access standalone functional enrichment and interaction network analysis tool. Proteomics, 15, 2597-2601.

    Article  CAS  PubMed  Google Scholar 

  • Pavlopoulos GA, Moschopoulos CN, Hooper SD, Schneider R, Kossida S. (2009). jClust: a clustering and visualization toolbox. Bioinformatics, 25, 1994-1996.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Paz A et al. (2011). SPIKE: a database of highly curated human signaling pathways. Nucleic acids research, 39, D793-799.

    Article  CAS  PubMed  Google Scholar 

  • Pazos F, Ranea JA, Juan D, Sternberg MJ. (2005). Assessing protein coevolution in the context of the tree of life assists in the prediction of the interactome. Journal of molecular biology, 352, 1002-1015.

    Article  CAS  PubMed  Google Scholar 

  • Pazos F, Valencia A. (2001). Similarity of phylogenetic trees as indicator of protein–protein interaction. Protein engineering, 14, 609-614.

    Article  CAS  PubMed  Google Scholar 

  • Pazos F, Valencia A. (2002). In silico two-hybrid system for the selection of physically interacting protein pairs. Proteins: Structure, Function, and Bioinformatics, 47, 219-227.

    Article  CAS  Google Scholar 

  • Pellegrini M, Marcotte EM, Thompson MJ, Eisenberg D, Yeates TO. (1999). Assigning protein functions by comparative genome analysis: protein phylogenetic profiles. Proceedings of the National Academy of Sciences, 96, 4285-4288.

    Google Scholar 

  • Peri S et al. (2003). Development of human protein reference database as an initial platform for approaching systems biology in humans. Genome research, 13, 2363-2371.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Persico M, Ceol A, Gavrila C, Hoffmann R, Florio A, Cesareni G. (2005). HomoMINT: an inferred human network based on orthology mapping of protein interactions discovered in model organisms. BMC bioinformatics, 6 Suppl 4, S21.

    Google Scholar 

  • Phan HT, Sternberg MJ. (2012). PINALOG: a novel approach to align protein interaction networks—implications for complex detection and function prediction. Bioinformatics, 28, 1239-1245.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Piehler J. (2005). New methodologies for measuring protein interactions in vivo and in vitro. Current opinion in structural biology, 15, 4-14.

    Article  CAS  PubMed  Google Scholar 

  • Pierce BG, Wiehe K, Hwang H, Kim B-H, Vreven T, Weng Z. (2014). ZDOCK server: interactive docking prediction of protein–protein complexes and symmetric multimers. Bioinformatics, 30, 1771-1773.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Pinney JW, Shirley MW, McConkey GA, Westhead DR. (2005). metaSHARK: software for automated metabolic network prediction from DNA sequence and its application to the genomes of Plasmodium falciparum and Eimeria tenella. Nucleic acids research, 33, 1399-1409.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Pitre S et al. (2012). Short co-occurring polypeptide regions can predict global protein interaction maps. Scientific reports, 2, 239.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Planas-Iglesias J, Marin-Lopez MA, Bonet J, Garcia-Garcia J, Oliva B. (2013). iLoops: a protein–protein interaction prediction server based on structural features. Bioinformatics, 29, 2360-2362.

    Article  CAS  PubMed  Google Scholar 

  • Powell S et al. (2012). eggNOG v3.0: orthologous groups covering 1133 organisms at 41 different taxonomic ranges. Nucleic acids research, 40, D284-289.

    Article  CAS  PubMed  Google Scholar 

  • Pruitt KD, Tatusova T, Maglott DR. (2007). NCBI reference sequences (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins. Nucleic acids research, 35, D61-65.

    Article  CAS  PubMed  Google Scholar 

  • Pržulj N, Wigle DA, Jurisica I. (2004). Functional topology in a network of protein interactions. Bioinformatics, 20, 340-348.

    Article  PubMed  CAS  Google Scholar 

  • Qin S, Zhou HX. (2007). meta-PPISP: a meta web server for protein-protein interaction site prediction. Bioinformatics, 23, 3386-3387.

    Article  CAS  PubMed  Google Scholar 

  • Ramani AK, Marcotte EM. (2003). Exploiting the coevolution of interacting proteins to discover interaction specificity. Journal of molecular biology, 327, 273-284.

    Article  CAS  PubMed  Google Scholar 

  • Ramírez-Aportela E, López-Blanco JR, Chacón P. (2016). FRODOCK 2.0: fast protein–protein docking server. Bioinformatics, 32, 2386-2388.

    Article  PubMed  CAS  Google Scholar 

  • Rao VS, Srinivas K, Sujini G, Kumar G. (2014). Protein-protein interaction detection: methods and analysis. International journal of proteomics, 2014.

    Google Scholar 

  • Razick S, Magklaras G, Donaldson IM. (2008). iRefIndex: a consolidated protein interaction database with provenance. BMC bioinformatics, 9, 405.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Reid AJ, Ranea JA, Clegg AB, Orengo CA. (2010). CODA: accurate detection of functional associations between proteins in eukaryotic genomes using domain fusion. PloS one, 5, e10908.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Rhead B et al. (2010). The UCSC Genome Browser database: update 2010. Nucleic acids research, 38, D613-619.

    Article  CAS  PubMed  Google Scholar 

  • Rid R et al. (2013). PRIMOS: an integrated database of reassessed protein-protein interactions providing web-based access to in silico validation of experimentally derived data. Assay and drug development technologies, 11, 333-346.

    Article  CAS  PubMed  Google Scholar 

  • Ripoche H, Laine E, Ceres N, Carbone A. (2017). JET2 Viewer: a database of predicted multiple, possibly overlapping, protein-protein interaction sites for PDB structures. Nucleic acids research, 45, 4278.

    Article  CAS  PubMed  Google Scholar 

  • Rodionov A, Bezginov A, Rose J, Tillier ER. (2011). A new, fast algorithm for detecting protein coevolution using maximum compatible cliques. Algorithms for molecular biology, 6, 1-9.

    Article  Google Scholar 

  • Romero P, Wagg J, Green ML, Kaiser D, Krummenacker M, Karp PD. (2005). Computational prediction of human metabolic pathways from the complete human genome. Genome Biol, 6, R2.

    Article  PubMed  Google Scholar 

  • Ruepp A et al. (2010). CORUM: the comprehensive resource of mammalian protein complexes—2009. Nucleic acids research, 38, D497-D501.

    Article  CAS  PubMed  Google Scholar 

  • Safran M et al. (2010). GeneCards Version 3: the human gene integrator. Database : the journal of biological databases and curation, 2010, baq020.

    Google Scholar 

  • Sahu SS, Weirick T, Kaundal R. (2014). Predicting genome-scale Arabidopsis-Pseudomonas syringae interactome using domain and interolog-based approaches. BMC bioinformatics, 15 Suppl 11, S13.

    Google Scholar 

  • Salazar GA, Meintjes A, Mazandu GK, Rapanoël HA, Akinola RO, Mulder NJ. (2014). A web-based protein interaction network visualizer. BMC bioinformatics, 15, 1-8.

    Article  CAS  Google Scholar 

  • Salwinski L, Eisenberg D. (2007). The MiSink Plugin: Cytoscape as a graphical interface to the Database of Interacting Proteins. Bioinformatics, 23, 2193-2195.

    Article  CAS  PubMed  Google Scholar 

  • Salwinski L, Miller CS, Smith AJ, Pettit FK, Bowie JU, Eisenberg D. (2004). The Database of Interacting Proteins: 2004 update. Nucleic acids research, 32, D449-451.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Saraf MC, Moore GL, Maranas CD. (2003). Using multiple sequence correlation analysis to characterize functionally important protein regions. Protein Engineering, 16, 397-406.

    Article  CAS  PubMed  Google Scholar 

  • Sarkar S, Gulati K, Kairamkonda M, Mishra A, Poluri KM. (2018). Elucidating protein-protein interactions through computational approaches and designing small molecule inhibitors against them for various diseases. Current topics in medicinal chemistry, 18, 1719-1736.

    Article  CAS  PubMed  Google Scholar 

  • Sato T, Yamanishi Y, Horimoto K, Kanehisa M, Toh H. (2006). Partial correlation coefficient between distance matrices as a new indicator of protein–protein interactions. Bioinformatics, 22, 2488-2492.

    Article  CAS  PubMed  Google Scholar 

  • Sato T, Yamanishi Y, Kanehisa M, Toh H. (2005). The inference of protein–protein interactions by coevolutionary analysis is improved by excluding the information about the phylogenetic relationships. Bioinformatics, 21, 3482-3489.

    Article  CAS  PubMed  Google Scholar 

  • Sayers EW et al. (2010). Database resources of the National Center for Biotechnology Information. Nucleic acids research, 38, D5-16.

    Article  CAS  PubMed  Google Scholar 

  • Schaefer CF, Anthony K, Krupa S, Buchoff J, Day M, Hannay T, Buetow KH. (2009). PID: the Pathway Interaction Database. Nucleic acids research, 37, D674-679.

    Article  CAS  PubMed  Google Scholar 

  • Schaefer MH, Fontaine JF, Vinayagam A, Porras P, Wanker EE, Andrade-Navarro MA. (2012). HIPPIE: Integrating protein interaction networks with experiment based quality scores. PLoS One, 7, e31826.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Schneidman-Duhovny D, Hammel M, Sali A. (2011). Macromolecular docking restrained by a small angle X-ray scattering profile. Journal of structural biology, 173, 461-471.

    Article  CAS  PubMed  Google Scholar 

  • Schneidman-Duhovny D, Inbar Y, Nussinov R, Wolfson HJ. (2005). PatchDock and SymmDock: servers for rigid and symmetric docking. Nucleic acids research, 33, W363-W367.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Schymkowitz J, Borg J, Stricher F, Nys R, Rousseau F, Serrano L. (2005). The FoldX web server: an online force field. Nucleic acids research, 33, W382-W388.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Scott MS, Barton GJ. (2007). Probabilistic prediction and ranking of human protein-protein interactions. BMC bioinformatics, 8, 239.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Seet BT, Dikic I, Zhou MM, Pawson T. (2006). Reading protein modifications with interaction domains. Nature reviews. Molecular cell biology, 7, 473-483.

    Article  CAS  PubMed  Google Scholar 

  • Segura J, Fernandez-Fuentes N. (2011). PCRPi-DB: a database of computationally annotated hot spots in protein interfaces. Nucleic acids research, 39, D755-760.

    Article  CAS  PubMed  Google Scholar 

  • Senachak J, Cheevadhanarak S, Hongsthong A. (2015). SpirPro: A Spirulina proteome database and web-based tools for the analysis of protein-protein interactions at the metabolic level in Spirulina (Arthrospira) platensis C1. BMC bioinformatics, 16, 233.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Shannon P et al. (2003). Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome research, 13, 2498-2504.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Sharan R et al. (2005). Conserved patterns of protein interaction in multiple species. Proceedings of the National Academy of Sciences of the United States of America, 102, 1974-1979.

    Google Scholar 

  • Sheinerman FB, Norel R, Honig B. (2000). Electrostatic aspects of protein–protein interactions. Current opinion in structural biology, 10, 153-159.

    Article  CAS  PubMed  Google Scholar 

  • Shen J et al. (2007). Predicting protein–protein interactions based only on sequences information. Proceedings of the National Academy of Sciences, 104, 4337-4341.

    Google Scholar 

  • Shin YC, Shin SY, So I, Kwon D, Jeon JH. (2011). TRIP Database: a manually curated database of protein-protein interactions for mammalian TRP channels. Nucleic acids research, 39, D356-361.

    Article  CAS  PubMed  Google Scholar 

  • Shoemaker BA et al. (2012). IBIS (Inferred Biomolecular Interaction Server) reports, predicts and integrates multiple types of conserved interactions for proteins. Nucleic acids research, 40, D834-D840.

    Article  CAS  PubMed  Google Scholar 

  • Shuai K. (2000). Modulation of STAT signaling by STAT-interacting proteins. Oncogene, 19, 2638-2644.

    Article  CAS  PubMed  Google Scholar 

  • Shulman-Peleg A, Shatsky M, Nussinov R, Wolfson HJ. (2008). MultiBind and MAPPIS: webservers for multiple alignment of protein 3D-binding sites and their interactions. Nucleic acids research, 36, W260-W264.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Sigrist CJ, Cerutti L, de Castro E, Langendijk-Genevaux PS, Bulliard V, Bairoch A, Hulo N. (2010). PROSITE, a protein domain database for functional characterization and annotation. Nucleic acids research, 38, D161-166.

    Article  CAS  PubMed  Google Scholar 

  • Singer MS, Vriend G, Bywater RP. (2002). Prediction of protein residue contacts with a PDB-derived likelihood matrix. Protein Engineering, 15, 721-725.

    Article  CAS  PubMed  Google Scholar 

  • Singh A. (2019). PPI discovery using proteome coevolution. Nature methods, 16, 804.

    Article  PubMed  Google Scholar 

  • Singhal M, Resat H. (2007). A domain-based approach to predict protein-protein interactions. BMC bioinformatics, 8, 199.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Smoot ME, Ono K, Ruscheinski J, Wang PL, Ideker T. (2011). Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics, 27, 431-432.

    Article  CAS  PubMed  Google Scholar 

  • Stark C et al. (2011). The BioGRID Interaction Database: 2011 update. Nucleic acids research, 39, D698-704.

    Article  CAS  PubMed  Google Scholar 

  • Stein A, Ceol A, Aloy P. (2011). 3did: identification and classification of domain-based interactions of known three-dimensional structure. Nucleic acids research, 39, D718-723.

    Article  CAS  PubMed  Google Scholar 

  • Sun J, Li Y, Zhao Z. (2007). Phylogenetic profiles for the prediction of protein–protein interactions: how to select reference organisms? Biochemical and Biophysical Research Communications, 353, 985-991.

    Article  CAS  PubMed  Google Scholar 

  • Sun J, Xu J, Liu Z, Liu Q, Zhao A, Shi T, Li Y. (2005). Refined phylogenetic profiles method for predicting protein–protein interactions. Bioinformatics, 21, 3409-3415.

    Article  CAS  PubMed  Google Scholar 

  • Sun T, Zhou B, Lai L, Pei J. (2017). Sequence-based prediction of protein protein interaction using a deep-learning algorithm. BMC bioinformatics, 18, 1-8.

    Article  CAS  Google Scholar 

  • Szilagyi A, Zhang Y. (2014). Template-based structure modeling of protein-protein interactions. Current opinion in structural biology, 24, 10-23.

    Article  CAS  PubMed  Google Scholar 

  • Szklarczyk D et al. (2011). The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored. Nucleic acids research, 39, D561-568.

    Article  CAS  PubMed  Google Scholar 

  • Szklarczyk D et al. (2015). STRING v10: protein–protein interaction networks, integrated over the tree of life. Nucleic acids research, 43, D447-D452.

    Article  CAS  PubMed  Google Scholar 

  • Szklarczyk D et al. (2019). STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic acids research, 47, D607-D613.

    Article  CAS  PubMed  Google Scholar 

  • Szklarczyk D et al. (2017). The STRING database in 2017: quality-controlled protein-protein association networks, made broadly accessible. Nucleic acids research, 45, D362-D368.

    Article  CAS  PubMed  Google Scholar 

  • Taylor IW et al. (2009). Dynamic modularity in protein interaction networks predicts breast cancer outcome. Nature biotechnology, 27, 199-204.

    Article  CAS  PubMed  Google Scholar 

  • Theocharidis A, van Dongen S, Enright AJ, Freeman TC. (2009). Network visualization and analysis of gene expression data using BioLayout Express(3D). Nat Protoc, 4, 1535-1550.

    Article  CAS  PubMed  Google Scholar 

  • Thorn KS, Bogan AA. (2001). ASEdb: a database of alanine mutations and their effects on the free energy of binding in protein interactions. Bioinformatics, 17, 284-285.

    Article  CAS  PubMed  Google Scholar 

  • Tillier ER, Biro L, Li G, Tillo D. (2006). Codep: maximizing co-evolutionary interdependencies to discover interacting proteins. Proteins: Structure, Function, and Bioinformatics, 63, 822-831.

    Article  CAS  Google Scholar 

  • Tillier ER, Charlebois RL. (2009). The human protein coevolution network. Genome research, 19, 1861-1871.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Torchala M, Moal IH, Chaleil RA, Fernandez-Recio J, Bates PA. (2013). SwarmDock: a server for flexible protein–protein docking. Bioinformatics, 29, 807-809.

    Article  CAS  PubMed  Google Scholar 

  • Tormo J, Natarajan K, Margulies DH, Mariuzza RA. (1999). Crystal structure of a lectin-like natural killer cell receptor bound to its MHC class I ligand. Nature, 402, 623-631.

    Article  CAS  PubMed  Google Scholar 

  • Tovchigrechko A, Vakser IA. (2006). GRAMM-X public web server for protein–protein docking. Nucleic acids research, 34, W310-W314.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Tress ML, Valencia A. (2010). Predicted residue–residue contacts can help the scoring of 3D models. Proteins: Structure, Function, and Bioinformatics, 78, 1980-1991.

    Article  CAS  Google Scholar 

  • Tsai CJ, Lin SL, Wolfson HJ, Nussinov R. (1997). Studies of protein-protein interfaces: a statistical analysis of the hydrophobic effect. Protein science : a publication of the Protein Society, 6, 53-64.

    Article  CAS  Google Scholar 

  • Tuncbag N, Gursoy A, Keskin O. (2009a). Identification of computational hot spots in protein interfaces: combining solvent accessibility and inter-residue potentials improves the accuracy. Bioinformatics, 25, 1513-1520.

    Article  CAS  PubMed  Google Scholar 

  • Tuncbag N, Gursoy A, Keskin O. (2011a). Prediction of protein–protein interactions: unifying evolution and structure at protein interfaces. Physical biology, 8, 035006.

    Article  PubMed  CAS  Google Scholar 

  • Tuncbag N, Gursoy A, Nussinov R, Keskin O. (2011b). Predicting protein-protein interactions on a proteome scale by matching evolutionary and structural similarities at interfaces using PRISM. Nature protocols, 6, 1341.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Tuncbag N, Kar G, Keskin O, Gursoy A, Nussinov R. (2009b). A survey of available tools and web servers for analysis of protein–protein interactions and interfaces. Briefings in bioinformatics, 10, 217-232.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Tuncbag N, Keskin O, Gursoy A. (2010). HotPoint: hot spot prediction server for protein interfaces. Nucleic acids research, 38, W402-W406.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Turner B et al. (2010). iRefWeb: interactive analysis of consolidated protein interaction data and their supporting evidence. Database : the journal of biological databases and curation, 2010, baq023.

    Google Scholar 

  • Uetz P et al. (2000). A comprehensive analysis of protein-protein interactions in Saccharomyces cerevisiae. Nature, 403, 623-627.

    Article  CAS  PubMed  Google Scholar 

  • Vakser IA. (2014). Protein-protein docking: from interaction to interactome. Biophysical journal, 107, 1785-1793.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Valdar WS. (2002). Scoring residue conservation. Proteins: structure, function, and bioinformatics, 48, 227-241.

    Article  CAS  Google Scholar 

  • Vastrik I et al. (2007). Reactome: a knowledge base of biologic pathways and processes. Genome Biol, 8, R39.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Vinogradova O, Qin J (2011) NMR as a unique tool in assessment and complex determination of weak protein–protein interactions. In: NMR of Proteins and Small Biomolecules. Springer, pp 35-45

    Google Scholar 

  • von Mering C, Huynen M, Jaeggi D, Schmidt S, Bork P, Snel B. (2003). STRING: a database of predicted functional associations between proteins. Nucleic acids research, 31, 258-261.

    Article  CAS  Google Scholar 

  • Vreven T et al. (2015). Updates to the Integrated Protein-Protein Interaction Benchmarks: Docking Benchmark Version 5 and Affinity Benchmark Version 2. J Mol Biol, 427, 3031-3041.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Waese J, Provart NJ. (2017). The Bio-Analytic Resource for Plant Biology. Methods in molecular biology, 1533, 119-148.

    Article  CAS  PubMed  Google Scholar 

  • Wang L, Wang HF, Liu SR, Yan X, Song KJ. (2019). Predicting Protein-Protein Interactions from Matrix-Based Protein Sequence Using Convolution Neural Network and Feature-Selective Rotation Forest. Sci Rep, 9, 9848.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Warde-Farley D et al. (2010). The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function. Nucleic acids research, 38, W214-220.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Wheeler DL et al. (2008). Database resources of the National Center for Biotechnology Information. Nucleic acids research, 36, D13-21.

    Article  CAS  PubMed  Google Scholar 

  • Winter C, Henschel A, Kim WK, Schroeder M. (2006). SCOPPI: a structural classification of protein-protein interfaces. Nucleic acids research, 34, D310-314.

    Article  CAS  PubMed  Google Scholar 

  • Wishart DS et al. (2018). DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic acids research, 46, D1074-D1082.

    Article  CAS  PubMed  Google Scholar 

  • Wojcik J, Schachter V. (2001). Protein-protein interaction map inference using interacting domain profile pairs. Bioinformatics, 17 Suppl 1, S296-305.

    Article  PubMed  Google Scholar 

  • Wong L, You Z-H, Li S, Huang Y-A, Liu G Detection of protein-protein interactions from amino acid sequences using a rotation forest model with a novel PR-LPQ descriptor. In: International Conference on Intelligent Computing, 2015. Springer, pp 713-720

    Google Scholar 

  • Worth CL, Gong S, Blundell TL. (2009). Structural and functional constraints in the evolution of protein families. Nature Reviews Molecular Cell Biology, 10, 709-720.

    Article  CAS  PubMed  Google Scholar 

  • Wu X et al. (2006). SPIDer: Saccharomyces protein-protein interaction database. BMC bioinformatics, 7 Suppl 5, S16.

    Google Scholar 

  • Wuchty S, Barabási A-L, Ferdig MT. (2006). Stable evolutionary signal in a yeast protein interaction network. BMC evolutionary biology, 6, 8.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Xenarios I, Rice DW, Salwinski L, Baron MK, Marcotte EM, Eisenberg D. (2000). DIP: the database of interacting proteins. Nucleic acids research, 28, 289-291.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Xu Q, Canutescu AA, Wang G, Shapovalov M, Obradovic Z, Dunbrack Jr RL. (2008). Statistical analysis of interface similarity in crystals of homologous proteins. Journal of molecular biology, 381, 487-507.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Xue LC, Dobbs D, Bonvin AM, Honavar V. (2015). Computational prediction of protein interfaces: A review of data driven methods. FEBS letters, 589, 3516-3526.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Yachie N, Saito R, Sugiyama N, Tomita M, Ishihama Y. (2011). Integrative features of the yeast phosphoproteome and protein-protein interaction map. PLoS computational biology, 7, e1001064.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Yan Y, Zhang D, Zhou P, Li B, Huang S-Y. (2017). HDOCK: a web server for protein–protein and protein–DNA/RNA docking based on a hybrid strategy. Nucleic acids research, 45, W365-W373.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Yanai I, Derti A, DeLisi C. (2001). Genes linked by fusion events are generally of the same functional category: a systematic analysis of 30 microbial genomes. Proceedings of the National Academy of Sciences, 98, 7940-7945.

    Google Scholar 

  • Yang L, Xia J-F, Gui J. (2010). Prediction of protein-protein interactions from protein sequence using local descriptors. Protein and Peptide Letters, 17, 1085-1090.

    Article  CAS  PubMed  Google Scholar 

  • Yang X, Yang S, Li Q, Wuchty S, Zhang Z. (2020). Prediction of human-virus protein-protein interactions through a sequence embedding-based machine learning method. Computational and structural biotechnology journal, 18, 153-161.

    Article  CAS  PubMed  Google Scholar 

  • Yook SH, Oltvai ZN, Barabasi AL. (2004). Functional and topological characterization of protein interaction networks. Proteomics, 4, 928-942.

    Article  CAS  PubMed  Google Scholar 

  • You Z-H, Lei Y-K, Zhu L, Xia J, Wang B Prediction of protein-protein interactions from amino acid sequences with ensemble extreme learning machines and principal component analysis. In: BMC bioinformatics, 2013. vol S8. Springer, p S10

    Google Scholar 

  • Yu H, Paccanaro A, Trifonov V, Gerstein M. (2006). Predicting interactions in protein networks by completing defective cliques. Bioinformatics, 22, 823-829.

    Article  CAS  PubMed  Google Scholar 

  • Yu T, Liu Y, Zeng Y, Chen J, Yang G, Li Y. (2019). Triplet-Triplet Annihilation Upconversion for Photocatalytic Hydrogen Evolution. Chemistry, 25, 16270-16276.

    Article  CAS  PubMed  Google Scholar 

  • Yue J et al. (2017). PCPPI: a comprehensive database for the prediction of Penicillium-crop protein-protein interactions. Database : the journal of biological databases and curation, 2017.

    Google Scholar 

  • Zanzoni A, Montecchi-Palazzi L, Quondam M, Ausiello G, Helmer-Citterich M, Cesareni G. (2002). MINT: a Molecular INTeraction database. FEBS letters, 513, 135-140.

    Article  CAS  PubMed  Google Scholar 

  • Zhang QC, Petrey D, Garzón JI, Deng L, Honig B. (2012). PrePPI: a structure-informed database of protein–protein interactions. Nucleic acids research, 41, D828-D833.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Zhang X, Jiao X, Song J, Chang S. (2016). Prediction of human protein–protein interaction by a domain-based approach. Journal of Theoretical Biology, 396, 144-153.

    Article  CAS  PubMed  Google Scholar 

  • Zhao S et al. (2010). Regulation of cellular metabolism by protein lysine acetylation. Science, 327, 1000-1004.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Zhou H-X, Qin S. (2007). Interaction-site prediction for protein complexes: a critical assessment. Bioinformatics, 23, 2203-2209.

    Article  CAS  PubMed  Google Scholar 

  • Zhou YZ, Gao Y, Zheng YY (2011) Prediction of protein-protein interactions using local description of amino acid sequence. In: Advances in computer science and education applications. Springer, pp 254-262

    Google Scholar 

  • Zhu D, Qin ZS. (2005). Structural comparison of metabolic networks in selected single cell organisms. BMC bioinformatics, 6, 8.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Zhu G et al. (2016). PPIM: A Protein-Protein Interaction Database for Maize. Plant physiology, 170, 618-626.

    Article  CAS  PubMed  Google Scholar 

  • Zhu H, Domingues FS, Sommer I, Lengauer T. (2006). NOXclass: prediction of protein-protein interaction types. BMC bioinformatics, 7, 27.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Zhu H, Sommer I, Lengauer T, Domingues FS. (2008). Alignment of non-covalent interactions at protein-protein interfaces. PLoS One, 3, e1926.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Zhu X, Mitchell JC. (2011). KFC2: a knowledge-based hot spot prediction method based on interface solvation, atomic density, and plasticity features. Proteins: Structure, Function, and Bioinformatics, 79, 2671-2683.

    Article  CAS  Google Scholar 

  • Zuiderweg ER. (2002). Mapping protein-protein interactions in solution by NMR spectroscopy. Biochemistry, 41, 1-7.

    Article  CAS  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Poluri, K.M., Gulati, K., Sarkar, S. (2021). Prediction, Analysis, Visualization, and Storage of Protein–Protein Interactions Using Computational Approaches. In: Protein-Protein Interactions. Springer, Singapore. https://doi.org/10.1007/978-981-16-1594-8_6

Download citation

  • DOI: https://doi.org/10.1007/978-981-16-1594-8_6

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-1593-1

  • Online ISBN: 978-981-16-1594-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics