1932

Abstract

In the last decade, newly developed experimental methods have made it possible to highlight that macromolecules in the cell milieu physically interact to support physiology. This has shifted the problem of protein–protein interaction from a microscopic, electron-density scale to a mesoscopic one. Further, nowadays there is increasing evidence that proteins in the nucleus and in the cytoplasm can aggregate in membraneless organelles for different physiological reasons. In this scenario, it is urgent to face the problem of biomolecule functional annotation with efficient computational methods, suited to extract knowledge from reliable data and transfer information across different domains of investigation. Here, we revise the present state of the art of our knowledge of protein–protein interaction and the computational methods that differently implement it. Furthermore, we explore experimental and computational features of a set of proteins involved in phase separation.

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2020-07-20
2024-04-19
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Literature Cited

  1. 1. 
    Rivas G, Minton AP. 2016. Macromolecular crowding in vitro, in vivo, and in between. Trends Biochem. Sci. 41:970–81
    [Google Scholar]
  2. 2. 
    Shin Y, Brangwynne CP. 2017. Liquid phase condensation in cell physiology and disease. Science 357:1253–65
    [Google Scholar]
  3. 3. 
    Rivas G, Minton AP. 2018. Toward an understanding of biochemical equilibria within living cells. Biophys. Rev. 10:241–53
    [Google Scholar]
  4. 4. 
    Jain S, Wheeler JR, Walters RW, Agrawal A, Barsic A, Parker R 2016. ATPase-modulated stress granules contain a diverse proteome and substructure. Cell 164:487–98
    [Google Scholar]
  5. 5. 
    Putnam A, Cassani M, Smith J, Seydoux G 2019. A gel phase promotes condensation of liquid P granules in Caenorhabditis elegans embryos. Nat. Struct. Mol. Biol. 26:220–26
    [Google Scholar]
  6. 6. 
    Schuster BS, Reed EH, Parthasarathy R, Jahnke CN, Caldwell RM et al. 2018. Controllable protein phase separation and modular recruitment to form responsive membraneless organelles. Nat. Commun. 9:2985–97
    [Google Scholar]
  7. 7. 
    Neugebauer KM. 2017. Special focus on the Cajal body. RNA Biol 14:6669–70
    [Google Scholar]
  8. 8. 
    Alberti S, Gladfelter A, Mittag T 2019. Considerations and challenges in studying liquid-liquid phase separation and biomolecular condensates. Cell 176:419–34
    [Google Scholar]
  9. 9. 
    Alberti S. 2017. Phase separation in biology. Curr. Biol. 27:R1089–107
    [Google Scholar]
  10. 10. 
    Boeynaems S, Alberti S, Fawzi NL, Mittag T, Polymenidou M et al. 2018. Protein phase separation: a new phase in cell biology. Trends Cell Biol 28:420–35
    [Google Scholar]
  11. 11. 
    Alberti S, Dormann D. 2019. Liquid–liquid phase separation in disease. Annu. Rev. Genet. 53:171–94
    [Google Scholar]
  12. 12. 
    Banani SF, Lee HO, Hyman AA, Rosen MK 2017. Biomolecular condensates: organizers of cellular biochemistry. Nat. Rev. Mol. Cell Biol. 18:285–98
    [Google Scholar]
  13. 13. 
    Baldi P. 2018. Deep learning in biomedical data science. Annu. Rev. Biomed. Data Sci. 1:181–205
    [Google Scholar]
  14. 14. 
    Shahid S, Hassan MI, Islam A, Ahmad F 2017. Size-dependent studies of macromolecular crowding on the thermodynamic stability, structure and functional activity of proteins: in vitro and in silico approaches. Biochim. Biophys. Acta Gen. Subj 1861:2178–97
    [Google Scholar]
  15. 15. 
    Jankauskaite J, Jiménez-García B, Dapkunas J, Fernández-Recio J, Moal IH 2019. SKEMPI 2.0: an updated benchmark of changes in protein-protein binding energy, kinetics and thermodynamics upon mutation. Bioinformatics 35:3462–69
    [Google Scholar]
  16. 16. 
    Wang R, Fang X, Lu Y, Yang CY, Wang S 2005. The PDBbind database: methodologies and updates. J. Med. Chem. 48:124111–19
    [Google Scholar]
  17. 17. 
    Levy ED. 2010. A simple definition of structural regions in proteins and its use in analyzing interface evolution. J. Mol. Biol. 403:660–70
    [Google Scholar]
  18. 18. 
    Pandav G, Pryamitsyn VA, Errington JE, Ganesan V 2015. Multibody interactions, phase behavior and clustering in nanoparticle-polyelectrolyte mixtures. J. Phys. Chem. B 119:14536–50
    [Google Scholar]
  19. 19. 
    Samanta R, Ganesan V. 2018. Influence of protein charge patches on the structure of protein–polyelectrolyte complexes. Soft Matter 14:3748–59
    [Google Scholar]
  20. 20. 
    Rost B, Sander C. 1994. Conservation and prediction of solvent accessibility in protein families. Proteins 20:216–26
    [Google Scholar]
  21. 21. 
    Cazals F. 2010. Revisiting the Voronoi description of protein–protein interfaces. Pattern Recognit. Bioinform. 6282:419–30
    [Google Scholar]
  22. 22. 
    Ezkurdia I, Bartoli L, Fariselli P, Casadio R, Valencia A, Tress ML 2009. Progress and challenges in predicting protein–protein interaction sites. Brief. Bioinform. 10:3233–46
    [Google Scholar]
  23. 23. 
    de Vries SJ, Bonvin AM 2008. How proteins get in touch: interface prediction in the study of biomolecular complexes. Curr. Protein Pept. Sci. 9:4394–406
    [Google Scholar]
  24. 24. 
    Kabsch W, Sander C. 1983. Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 22:2577–637
    [Google Scholar]
  25. 25. 
    Janin J, Bahadur RP, Chakrabarti P 2008. Protein–protein interaction and quaternary structure. Q. Rev. Biophys. 41:2133–80
    [Google Scholar]
  26. 26. 
    Bradford JR, Westhead DR. 2005. Improved prediction of protein–protein binding sites using a support vector machines approach. Bioinformatics 21:81487–94
    [Google Scholar]
  27. 27. 
    Liang S, Zhang C, Liu S, Zhou Y 2006. Protein binding site prediction using an empirical scoring function. Nucleic Acids Res 34:133698–707
    [Google Scholar]
  28. 28. 
    de Vries SJ, van Dijk AD, Bonvin AM 2006. WHISCY: What information does surface conservation yield? Application to data-driven docking. Proteins 63:3479–89
    [Google Scholar]
  29. 29. 
    Zellner H, Staudigel M, Trenner T, Bittkowski M, Wolowski V et al. 2012. PresCont: predicting protein-protein interfaces utilizing four residue properties. Proteins 80:1154–68
    [Google Scholar]
  30. 30. 
    Savojardo C, Fariselli P, Martelli PL, Casadio R 2017. ISPRED4: interaction sites PREDiction in protein structures with a refining grammar model. Bioinformatics 33:111656–63
    [Google Scholar]
  31. 31. 
    Capra JA, Singh M. 2007. Predicting functionally important residues from sequence conservation. Bioinformatics 23:151875–82
    [Google Scholar]
  32. 32. 
    Neuvirth H, Raz R, Schreiber G 2004. ProMate: a structure based prediction program to identify the location of protein–protein binding sites. J. Mol. Biol. 338:1181–99
    [Google Scholar]
  33. 33. 
    Chen H, Zhou HX. 2005. Prediction of interface residues in protein–protein complexes by a consensus neural network method: test against NMR data. Proteins 61:121–35
    [Google Scholar]
  34. 34. 
    Ofran Y, Rost B. 2007. ISIS: interaction sites identified from sequence. Bioinformatics 23:2e13–16
    [Google Scholar]
  35. 35. 
    Murakami Y, Mizuguchi K. 2010. Applying the Naïve Bayes classifier with kernel density estimation to the prediction of protein–protein interaction sites. Bioinformatics 26:151841–48
    [Google Scholar]
  36. 36. 
    Li BQ, Feng KY, Chen L, Huang T, Cai YD 2012. Prediction of protein-protein interaction sites by random forest algorithm with mRMR and IFS. PLOS ONE 7:8e43927
    [Google Scholar]
  37. 37. 
    Altschul SF, Madden TL, Schäffer AA, Zhang J, Zhang Z et al. 1997. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 25:173389–402
    [Google Scholar]
  38. 38. 
    Remmert M, Biegert A, Hauser A, Söding J 2011. HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment. Nat. Methods 9:2173–75
    [Google Scholar]
  39. 39. 
    Sander C, Schneider R. 1991. Database of homology-derived protein structures and the structural meaning of sequence alignment. Proteins 9:156–68
    [Google Scholar]
  40. 40. 
    Suzek BE, Huang H, McGarvey P, Mazumder R, Wu CH 2007. UniRef: comprehensive and non-redundant UniProt reference clusters. Bioinformatics 23:1282–88
    [Google Scholar]
  41. 41. 
    Caffrey DR, Somaroo S, Hughes JD, Mintseris J, Huang ES 2004. Are protein-protein interfaces more conserved in sequence than the rest of the protein surface. ? Protein Sci 13:1190–202
    [Google Scholar]
  42. 42. 
    Sanchez-Garcia R, Sorzano COS, Carazo JM, Segura J 2019. BIPSPI: a method for the prediction of partner-specific protein–protein interfaces. Bioinformatics 35:3470–77
    [Google Scholar]
  43. 43. 
    Minhas Fu, Geiss BJ, Ben-Hur A 2014. PAIRpred: partner-specific prediction of interacting residues from sequence and structure. Proteins 82:71142–55
    [Google Scholar]
  44. 44. 
    Gallet X, Charloteaux B, Thomas A, Brasseur R 2000. A fast method to predict protein interaction sites from sequences. J. Mol. Biol. 302:4917–26
    [Google Scholar]
  45. 45. 
    Xue LC, Dobbs D, Honavar V 2011. HomPPI: a class of sequence homology based protein-protein interface prediction methods. BMC Bioinform 12:244
    [Google Scholar]
  46. 46. 
    Zhang QC, Deng L, Fisher M, Guan J, Honig B, Petrey D 2011. PredUs: a web server for predicting protein interfaces using structural neighbors. Nucleic Acids Res 39:W283–87
    [Google Scholar]
  47. 47. 
    Jordan RA, El-Manzalawy Y, Dobbs D, Honavar V 2012. Predicting protein-protein interface residues using local surface structural similarity. BMC Bioinform 13:41
    [Google Scholar]
  48. 48. 
    Jelínek J, Škoda P, Hoksza D 2017. Utilizing knowledge base of amino acids structural neighborhoods to predict protein-protein interaction sites. BMC Bioinform 18:Suppl. 15492
    [Google Scholar]
  49. 49. 
    Jones S, Thornton JM. 1997. Prediction of protein-protein interaction sites using patch analysis. J. Mol. Biol. 272:1133–43
    [Google Scholar]
  50. 50. 
    Koike A, Takagi T. 2004. Prediction of protein–protein interaction sites using support vector machines. Protein Eng. Des. Sel. 17:2165–73
    [Google Scholar]
  51. 51. 
    Li MH, Lin L, Wang XL, Liu T 2007. Protein–protein interaction site prediction based on conditional random fields. Bioinformatics 23:5597–604
    [Google Scholar]
  52. 52. 
    Liu B, Wang X, Lin L, Tang B, Dong Q, Wang X 2009. Prediction of protein binding sites in protein structures using hidden Markov support vector machine. BMC Bioinform 10:381
    [Google Scholar]
  53. 53. 
    Porollo A, Meller J. 2007. Prediction-based fingerprints of protein–protein interactions. Proteins 66:3630–45
    [Google Scholar]
  54. 54. 
    Sikić M, Tomić S, Vlahovicek K 2009. Prediction of protein-protein interaction sites in sequences and 3D structures by random forests. PLOS Comput. Biol. 5:1e1000278
    [Google Scholar]
  55. 55. 
    Dong Z, Wang K, Dang TK, Gültas M, Welter M et al. 2014. CRF-based models of protein surfaces improve protein-protein interaction site predictions. BMC Bioinform 15:277
    [Google Scholar]
  56. 56. 
    Fariselli P, Pazos F, Valencia A, Casadio R 2002. Prediction of protein–protein interaction sites in heterocomplexes with neural networks. Eur. J. Biochem. 269:51356–61
    [Google Scholar]
  57. 57. 
    Daberdaku S, Ferrari C. 2018. Exploring the potential of 3D Zernike descriptors and SVM for protein–protein interface prediction. BMC Bioinform 19:135
    [Google Scholar]
  58. 58. 
    Vreven T, Moal IH, Vangone A, Pierce BG, Kastritis PL et al. 2015. Updates to the integrated protein–protein interaction benchmarks: docking benchmark version 5 and affinity benchmark version 2. J. Mol. Biol. 427:193031–41
    [Google Scholar]
  59. 59. 
    Lensink MF, Velankar S, Wodak SJ 2017. Modeling protein–protein and protein–peptide complexes: CAPRI 6th edition. Proteins 85:3359–77
    [Google Scholar]
  60. 60. 
    Aumentado-Armstrong TT, Istrate B, Murgita RA 2015. Algorithmic approaches to protein-protein interaction site prediction. Algorithms Mol. Biol. 10:7
    [Google Scholar]
  61. 61. 
    Esmaielbeiki R, Krawczyk K, Knapp B, Nebel JC, Deane CM 2016. Progress and challenges in predicting protein interfaces. Brief. Bioinform. 17:1117–31
    [Google Scholar]
  62. 62. 
    Xue LC, Dobbs D, Bonvin AM, Honavar V 2015. Computational prediction of protein interfaces: a review of data driven methods. FEBS Lett 589:233516–26
    [Google Scholar]
  63. 63. 
    Wei Z, Han K, Yang J, Shen H, Yu D 2016. Protein–protein interaction sites prediction by ensembling SVM and sample-weighted random forests. Neurocomputing 193:201–12
    [Google Scholar]
  64. 64. 
    Hou Q, De Geest PFG, Vranken WF, Heringa J, Feenstra KA 2017. Seeing the trees through the forest: sequence-based homo- and heteromeric protein-protein interaction sites prediction using random forest. Bioinformatics 33:101479–87
    [Google Scholar]
  65. 65. 
    Zhang J, Kurgan L. 2019. SCRIBER: accurate and partner type-specific prediction of protein-binding residues from proteins sequences. Bioinformatics 35:14i343–53
    [Google Scholar]
  66. 66. 
    Chen P, Li J. 2010. Sequence-based identification of interface residues by an integrative profile combining hydrophobic and evolutionary information. BMC Bioinform 11:402
    [Google Scholar]
  67. 67. 
    Yan C, Dobbs D, Honavar V 2004. A two-stage classifier for identification of protein–protein interface residues. Bioinformatics 20:Suppl. 1i371–78
    [Google Scholar]
  68. 68. 
    Res I, Mihalek I, Lichtarge O 2005. An evolution based classifier for prediction of protein interfaces without using protein structures. Bioinformatics 21:102496–501
    [Google Scholar]
  69. 69. 
    Dhole K, Singh G, Pai PP, Mondal S 2014. Sequence-based prediction of protein–protein interaction sites with L1-logreg classifier. J. Theor. Biol. 348:47–54
    [Google Scholar]
  70. 70. 
    Pazos F, Helmer-Citterich M, Ausiello G, Valencia A 1997. Correlated mutations contain information about protein-protein interaction. J. Mol. Biol. 271:4511–23
    [Google Scholar]
  71. 71. 
    Ovchinnikov S, Kamisetty H, Baker D 2014. Robust and accurate prediction of residue–residue interactions across protein interfaces using evolutionary information. eLife 3:e02030
    [Google Scholar]
  72. 72. 
    Weigt M, White RA, Szurmant H, Hoch JA, Hwa T 2009. Identification of direct residue contacts in protein–protein interaction by message passing. PNAS 106:167–72
    [Google Scholar]
  73. 73. 
    Hopf TA, Schärfe CP, Rodrigues JP, Green AG, Kohlbacher O et al. 2014. Sequence co-evolution gives 3D contacts and structures of protein complexes. eLife 3:e03430
    [Google Scholar]
  74. 74. 
    Zhang J, Kurgan L. 2018. Review and comparative assessment of sequence-based predictors of protein-binding residues. Brief. Bioinform. 19:5821–37
    [Google Scholar]
  75. 75. 
    Hou Q, De Geest P, Griffioen CJ, Abeln S, Heringa J, Feenstra KA 2019. SeRenDIP: sequential remastering to derive profiles for fast and accurate predictions of PPI interface positions. Bioinformatics 35:4794–96
    [Google Scholar]
  76. 76. 
    Gromiha M, Yugandhar K, Jemimah S 2017. Protein–protein interactions: scoring schemes and binding affinity. Curr. Opin. Struct. 44:31–38
    [Google Scholar]
  77. 77. 
    Keskin O, Tuncbag N, Gursoy A 2016. Predicting protein–protein interactions from the molecular to the proteome level. Chem. Rev. 116:4884–909
    [Google Scholar]
  78. 78. 
    Snider J, Kotlyar M, Saraon P, Yao Z, Jurisica I, Stagljar I 2015. Fundamentals of protein interaction network mapping. Mol. Syst. Biol. 11:848
    [Google Scholar]
  79. 79. 
    Cafarelli TM, Desbuleux A, Wang Y, Choi SG, De Ridder D, Vidal M 2017. Mapping, modeling, and characterization of protein-protein interactions on a proteomic scale. Curr. Opin. Struct. Biol. 44:201–10
    [Google Scholar]
  80. 80. 
    Jensen LJ, Bork P. 2008. Not comparable, but complementary. Science 322:56–57
    [Google Scholar]
  81. 81. 
    De Las Rivas J, Fontanillo C 2012. Protein–protein interaction networks: unraveling the wiring of molecular machines within the cell. Brief. Funct. Genom. 11:489–96
    [Google Scholar]
  82. 82. 
    Luck K, Sheynkman GM, Zhang I, Vidal M 2017. Proteome-scale human interactomics. Trends Biochem. Sci. 42:342–54
    [Google Scholar]
  83. 83. 
    Fields S, Song O. 1989. A novel genetic system to detect protein–protein interactions. Nature 340:245–46
    [Google Scholar]
  84. 84. 
    Luo Y, Batalao A, Zhou H, Zhu L 1997. Mammalian two-hybrid system: a complementary approach to the yeast two-hybrid system. Biotechniques 22:350–52
    [Google Scholar]
  85. 85. 
    Brückner A, Polge C, Lentze N, Auerbach D, Schlattner U 2009. Yeast two-hybrid, a powerful tool for systems biology. Int. J. Mol. Sci. 10:2763–88
    [Google Scholar]
  86. 86. 
    Dunham WH, Mullin M, Gingras AC 2012. Affinity-purification coupled to mass spectrometry: basic principles and strategies. Proteomics 12:1576–90
    [Google Scholar]
  87. 87. 
    Havugimana PC, Hart GT, Nepusz T, Yang H, Turinsky AL et al. 2012. A census of human soluble protein complexes. Cell 150:1068–81
    [Google Scholar]
  88. 88. 
    Luck K, Kim DK, Lambourne L, Spirohn K, Begg BE et al. 2020. A reference map of the human binary protein interactome. Nature 580:402–8
    [Google Scholar]
  89. 89. 
    Huttlin EL, Bruckner RJ, Paulo JA, Cannon JR, Ting L et al. 2017. Architecture of the human interactome defines protein communities and disease networks. Nature 545:505–9
    [Google Scholar]
  90. 90. 
    Wodak SJ, Vlasblom J, Turinsky AL, Pu S 2013. Protein–protein interaction networks: the puzzling riches. Curr. Opin. Struct. Biol. 23:941–53
    [Google Scholar]
  91. 91. 
    Szklarczyk D, Jensen LJ. 2015. Protein-protein interaction databases. Methods Mol. Biol. 1278:39–56
    [Google Scholar]
  92. 92. 
    Miryala SK, Anbarasu A, Ramaiah S 2018. Discerning molecular interactions: a comprehensive review on biomolecular interaction databases and network analysis tools. Gene 642:84–94
    [Google Scholar]
  93. 93. 
    Orchard S, Ammari M, Aranda B, Breuza L, Briganti L et al. 2014. The MIntAct project—IntAct as a common curation platform for 11 molecular interaction databases. Nucleic Acids Res 42:D358–63
    [Google Scholar]
  94. 94. 
    Oughtred R, Stark C, Breitkreutz BJ, Rust J, Boucher L et al. 2019. The BioGRID interaction database: 2019 update. Nucleic Acids Res 47:D529–41
    [Google Scholar]
  95. 95. 
    Stumpf MP, Thorne T, de Silva E, Stewart R, An HJ et al. 2008. Estimating the size of the human interactome. PNAS 105:6959–64
    [Google Scholar]
  96. 96. 
    Tompa P, Davey NE, Gibson TJ, Babu MM 2014. A million peptide motifs for the molecular biologist. Mol. Cell 55:161–69
    [Google Scholar]
  97. 97. 
    Mosca R, Céol A, Aloy P 2013. Interactome3D: adding structural details to protein networks. Nat. Methods 10:47–53
    [Google Scholar]
  98. 98. 
    Dapkunas J, Timinskas A, Olechnovic K, Margelevicius M, Diciunas R, Venclovas C 2017. The PPI3D web server for searching, analyzing and modeling protein-protein interactions in the context of 3D structures. Bioinformatics 33:935–37
    [Google Scholar]
  99. 99. 
    Pellegrini M, Marcotte EM, Thompson MJ, Eisenberg D, Yeates TO 1999. Assigning protein functions by comparative genome analysis: protein phylogenetic profiles. PNAS 96:4285–88
    [Google Scholar]
  100. 100. 
    Enright AJ, Iliopoulos I, Kyrpides NC, Ouzounis CA 1999. Protein interaction maps for complete genomes based on gene fusion events. Nature 402:86–90
    [Google Scholar]
  101. 101. 
    Jansen R, Greenbaum D, Gerstein M 2002. Relating whole-genome expression data with protein-protein interactions. Genome Res 12:37–46
    [Google Scholar]
  102. 102. 
    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 Res 40:W147–51
    [Google Scholar]
  103. 103. 
    Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S et al. 2019. STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res 47:D607–13
    [Google Scholar]
  104. 104. 
    Hosur R, Peng J, Vinayagam A, Stelzl U, Xu J et al. 2012. A computational framework for boosting confidence in high-throughput protein-protein interaction datasets. Genome Biol 13:R76
    [Google Scholar]
  105. 105. 
    Cong Q, Anishchenko I, Ovchinnikov S, Baker D 2019. Protein interaction networks revealed by proteome coevolution. Science 365:185–89
    [Google Scholar]
  106. 106. 
    Blohm P, Frishman G, Smialowski P, Goebels F, Wachinger B et al. 2014. Negatome 2.0: a database of non-interacting proteins derived by literature mining, manual annotation and protein structure analysis. Nucleic Acids Res 42:D396–400
    [Google Scholar]
  107. 107. 
    Hashemifar S, Neyshabur B, Khan AA, Xu J 2018. Predicting protein–protein interactions through sequence-based deep learning. Bioinformatics 34:i802–10
    [Google Scholar]
  108. 108. 
    Romero-Molina S, Ruiz-Blanco YB, Harms M, Münch J, Sanchez-Garcia E 2019. PPI-Detect: a support vector machine model for sequence-based prediction of protein-protein interactions. J. Comput. Chem. 40:1233–42
    [Google Scholar]
  109. 109. 
    Yao Y, Du X, Diao Y, Zhu H 2019. An integration of deep learning with feature embedding for protein–protein interaction prediction. PeerJ 7:e7126
    [Google Scholar]
  110. 110. 
    Li Y, Ilie L. 2017. SPRINT: ultrafast protein-protein interaction prediction of the entire human interactome. BMC Bioinform 18:485
    [Google Scholar]
  111. 111. 
    Kovács IA, Luck K, Spirohn K, Wang Y, Pollis C et al. 2019. Network-based prediction of protein interactions. Nat. Commun. 10:11240
    [Google Scholar]
  112. 112. 
    Piovesan D, Tabaro F, Paladin L, Necci M, Micetic I et al. 2018. MobiDB 3.0: more annotations for intrinsic disorder, conformational diversity and interactions in proteins. Nucleic Acids Res 46:D471–76
    [Google Scholar]
  113. 113. 
    Bartoli L, Martelli PL, Rossi I, Fariselli P, Casadio R 2010. The prediction of protein-protein interacting sites in genome-wide protein interaction networks: the test case of the human cell cycle. Curr. Protein Pept. Sci. 11:601–8
    [Google Scholar]
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