Abstract
In the last two decades it has become increasingly evident that a large number of proteins adopt either a fully or a partially disordered conformation. Intrinsically disordered proteins are ubiquitous proteins that fulfill essential biological functions while lacking a stable 3D structure. Their conformational heterogeneity is encoded by the amino acid sequence, thereby allowing intrinsically disordered proteins or regions to be recognized based on their sequence properties. The identification of disordered regions facilitates the functional annotation of proteins and is instrumental for delineating boundaries of protein domains amenable to crystallization. This chapter focuses on the methods currently employed for predicting protein disorder and identifying intrinsically disordered binding sites.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Peng Z, Yan J, Fan X, Mizianty MJ, Xue B, Wang K, Hu G, Uversky VN, Kurgan L (2015) Exceptionally abundant exceptions: comprehensive characterization of intrinsic disorder in all domains of life. Cell Mol Life Sci 72(1):137–151. https://doi.org/10.1007/s00018-014-1661-9
Ward JJ, Sodhi JS, McGuffin LJ, Buxton BF, Jones DT (2004) Prediction and functional analysis of native disorder in proteins from the three kingdoms of life. J Mol Biol 337(3):635–645
Bogatyreva NS, Finkelstein AV, Galzitskaya OV (2006) Trend of amino acid composition of proteins of different taxa. J Bioinforma Comput Biol 4(2):597–608
Dunker AK, Babu MM, Barbar E, Blackledge M, Bondos SE, Dosztányi Z, Dyson HJ, Forman-Kay J, Fuxreiter M, Gsponer J, Han K-H, Jones DT, Longhi S, Metallo SJ, Nishikawa K, Nussinov R, Obradovic Z, Pappu RV, Rost B, Selenko P, Subramaniam V, Sussman JL, Tompa P, Uversky VN (2013) What’s in a name? Why these proteins are intrinsically disordered. Intrinsically Disord Proteins 1:e24157
Uversky VN (2015) The multifaceted roles of intrinsic disorder in protein complexes. FEBS Lett. https://doi.org/10.1016/j.febslet.2015.06.004
Haynes C, Oldfield CJ, Ji F, Klitgord N, Cusick ME, Radivojac P, Uversky VN, Vidal M, Iakoucheva LM (2006) Intrinsic disorder is a common feature of hub proteins from four eukaryotic interactomes. PLoS Comput Biol 2(8):e100
Habchi J, Tompa P, Longhi S, Uversky VN (2014) Introducing protein intrinsic disorder. Chem Rev 114(13):6561–6588. https://doi.org/10.1021/cr400514h
Babu MM (2016) The contribution of intrinsically disordered regions to protein function, cellular complexity, and human disease. Biochem Soc Trans 44(5):1185–1200. https://doi.org/10.1042/BST20160172
Uversky VN (2019) Intrinsically disordered proteins and their “Mysterious” (meta)physics. Front Phys 7(10). https://doi.org/10.3389/fphy.2019.00010
Uversky VN (2017) Intrinsically disordered proteins in overcrowded milieu: membrane-less organelles, phase separation, and intrinsic disorder. Curr Opin Struct Biol 44:18–30. https://doi.org/10.1016/j.sbi.2016.10.015
Banani SF, Lee HO, Hyman AA, Rosen MK (2017) Biomolecular condensates: organizers of cellular biochemistry. Nat Rev Mol Cell Biol 18(5):285–298. https://doi.org/10.1038/nrm.2017.7
Shin Y, Brangwynne CP (2017) Liquid phase condensation in cell physiology and disease. Science 357(6357). https://doi.org/10.1126/science.aaf4382
Boeynaems S, Alberti S, Fawzi NL, Mittag T, Polymenidou M, Rousseau F, Schymkowitz J, Shorter J, Wolozin B, Van Den Bosch L, Tompa P, Fuxreiter M (2018) Protein phase separation: a new phase in cell biology. Trends Cell Biol 28(6):420–435. https://doi.org/10.1016/j.tcb.2018.02.004
Alberti S, Hyman AA (2021) Biomolecular condensates at the nexus of cellular stress, protein aggregation disease and ageing. Nature reviews Mol Cell Biol 22(3):196–213. https://doi.org/10.1038/s41580-020-00326-6
Lobley A, Swindells MB, Orengo CA, Jones DT (2007) Inferring function using patterns of native disorder in proteins. PLoS Comput Biol 3(8):e162
Ferron F, Longhi S, Canard B, Karlin D (2006) A practical overview of protein disorder prediction methods. Proteins 65(1):1–14
Ferron F, Rancurel C, Longhi S, Cambillau C, Henrissat B, Canard B (2005) VaZyMolO: a tool to define and classify modularity in viral proteins. J Gen Virol 86(Pt 3):743–749
Lieutaud P, Ferron F, Habchi J, Canard B, Longhi S (2013) Predicting protein disorder and induced folding : a practical approach. In: Dunn B (ed) Advances in protein and peptide sciences, vol 1. Bentham Science Publishers, pp 441–492. (452)
Bourhis JM, Canard B, Longhi S (2007) Predicting protein disorder and induced folding: from theoretical principles to practical applications. Curr Protein Pept Sci 8(2):135–149
Uversky VN, Radivojac P, Iakoucheva LM, Obradovic Z, Dunker AK (2007) Prediction of intrinsic disorder and its use in functional proteomics. Methods Mol Biol 408:69–92
He B, Wang K, Liu Y, Xue B, Uversky VN, Dunker AK (2009) Predicting intrinsic disorder in proteins: an overview. Cell Res. https://doi.org/10.1038/cr.2009.87
Longhi S, Lieutaud P, Canard B (2010) Conformational disorder. Methods Mol Biol 609:307–325
Meng F, Uversky VN, Kurgan L (2017) Comprehensive review of methods for prediction of intrinsic disorder and its molecular functions. Cell Mol Life Sci 74(17):3069–3090. https://doi.org/10.1007/s00018-017-2555-4
Liu Y, Wang X, Liu B (2019) A comprehensive review and comparison of existing computational methods for intrinsically disordered protein and region prediction. Brief Bioinformatics 20(1):330–346. https://doi.org/10.1093/bib/bbx126
Necci M, Piovesan D, Tosatto SCE (2021) Critical assessment of protein intrinsic disorder prediction. Nat Methods 18(5):472–481. https://doi.org/10.1038/s41592-021-01117-3
Katuwawala A, Peng Z, Yang J, Kurgan L (2019) Computational prediction of MoRFs, Short disorder-to-order transitioning protein binding regions. Comput Struct Biotechnol J 17:454–462. https://doi.org/10.1016/j.csbj.2019.03.013
Monastyrskyy B, Kryshtafovych A, Moult J, Tramontano A, Fidelis K (2014) Assessment of protein disorder region predictions in CASP10. Proteins 82(Suppl. 2):127–137. https://doi.org/10.1002/prot.24391
Ishida T, Kinoshita K (2008) Prediction of disordered regions in proteins based on the meta approach. Bioinformatics 24(11):1344–1348. https://doi.org/10.1093/bioinformatics/btn195
Lieutaud P, Canard B, Longhi S (2008) MeDor: a metaserver for predicting protein disorder. BMC Genomics 9(Suppl. 2):S25
Lang B, Babu MM (2021) A community effort to bring structure to disorder. Nat Methods 18(5):454–455. https://doi.org/10.1038/s41592-021-01123-5
Brown CJ, Johnson AK, Dunker AK, Daughdrill GW (2011) Evolution and disorder. Curr Opin Struct Biol 21(3):441–446. https://doi.org/10.1016/j.sbi.2011.02.005
Oates ME, Romero P, Ishida T, Ghalwash M, Mizianty MJ, Xue B, Dosztanyi Z, Uversky VN, Obradovic Z, Kurgan L, Dunker AK, Gough J (2013) D(2)P(2): database of disordered protein predictions. Nucleic Acids Res 41(Database issue):D508–D516. https://doi.org/10.1093/nar/gks1226
Pandurangan AP, Stahlhacke J, Oates ME, Smithers B, Gough J (2019) The SUPERFAMILY 2.0 database: a significant proteome update and a new webserver. Nucleic Acids Res 47(D1):D490–D494. https://doi.org/10.1093/nar/gky1130
Hornbeck PV, Zhang B, Murray B, Kornhauser JM, Latham V, Skrzypek E (2015) PhosphoSitePlus, 2014: mutations, PTMs and recalibrations. Nucleic Acids Res 43(Database issue):D512–D520. https://doi.org/10.1093/nar/gku1267
Potenza E, Di Domenico T, Walsh I, Tosatto SC (2015) MobiDB 2.0: an improved database of intrinsically disordered and mobile proteins. Nucleic Acids Res 43(Database issue):D315–D320. https://doi.org/10.1093/nar/gku982
Piovesan D, Necci M, Escobedo N, Monzon AM, Hatos A, Micetic I, Quaglia F, Paladin L, Ramasamy P, Dosztanyi Z, Vranken WF, Davey NE, Parisi G, Fuxreiter M, Tosatto SCE (2021) MobiDB: intrinsically disordered proteins in 2021. Nucleic Acids Res 49(D1):D361–D367. https://doi.org/10.1093/nar/gkaa1058
Sickmeier M, Hamilton JA, LeGall T, Vacic V, Cortese MS, Tantos A, Szabo B, Tompa P, Chen J, Uversky VN, Obradovic Z, Dunker AK (2007) DisProt: the database of disordered proteins. Nucleic Acids Res 35(Database issue):D786–D793
Hatos A, Hajdu-Soltesz B, Monzon AM, Palopoli N, Alvarez L, Aykac-Fas B, Bassot C, Benitez GI, Bevilacqua M, Chasapi A, Chemes L, Davey NE, Davidovic R, Dunker AK, Elofsson A, Gobeill J, Foutel NSG, Sudha G, Guharoy M, Horvath T, Iglesias V, Kajava AV, Kovacs OP, Lamb J, Lambrughi M, Lazar T, Leclercq JY, Leonardi E, Macedo-Ribeiro S, Macossay-Castillo M, Maiani E, Manso JA, Marino-Buslje C, Martinez-Perez E, Meszaros B, Micetic I, Minervini G, Murvai N, Necci M, Ouzounis CA, Pajkos M, Paladin L, Pancsa R, Papaleo E, Parisi G, Pasche E, Barbosa Pereira PJ, Promponas VJ, Pujols J, Quaglia F, Ruch P, Salvatore M, Schad E, Szabo B, Szaniszlo T, Tamana S, Tantos A, Veljkovic N, Ventura S, Vranken W, Dosztanyi Z, Tompa P, Tosatto SCE, Piovesan D (2020) DisProt: intrinsic protein disorder annotation in 2020. Nucleic Acids Res 48(D1):D269–D276. https://doi.org/10.1093/nar/gkz975
Fukuchi S, Amemiya T, Sakamoto S, Nobe Y, Hosoda K, Kado Y, Murakami SD, Koike R, Hiroaki H, Ota M (2014) IDEAL in 2014 illustrates interaction networks composed of intrinsically disordered proteins and their binding partners. Nucleic Acids Res 42(Database issue):D320–D325. https://doi.org/10.1093/nar/gkt1010
Zhao B, Katuwawala A, Oldfield CJ, Dunker AK, Faraggi E, Gsponer J, Kloczkowski A, Malhis N, Mirdita M, Obradovic Z, Soding J, Steinegger M, Zhou Y, Kurgan L (2021) DescribePROT: database of amino acid-level protein structure and function predictions. Nucleic Acids Res 49(D1):D298–D308. https://doi.org/10.1093/nar/gkaa931
Varadi M, Kosol S, Lebrun P, Valentini E, Blackledge M, Dunker AK, Felli IC, Forman-Kay JD, Kriwacki RW, Pierattelli R, Sussman J, Svergun DI, Uversky VN, Vendruscolo M, Wishart D, Wright PE, Tompa P (2014) pE-DB: a database of structural ensembles of intrinsically disordered and of unfolded proteins. Nucleic Acids Res 42(Database issue):D326–D335. https://doi.org/10.1093/nar/gkt960
Lazar T, Martinez-Perez E, Quaglia F, Hatos A, Chemes LB, Iserte JA, Mendez NA, Garrone NA, Saldano TE, Marchetti J, Rueda AJV, Bernado P, Blackledge M, Cordeiro TN, Fagerberg E, Forman-Kay JD, Fornasari MS, Gibson TJ, Gomes GW, Gradinaru CC, Head-Gordon T, Jensen MR, Lemke EA, Longhi S, Marino-Buslje C, Minervini G, Mittag T, Monzon AM, Pappu RV, Parisi G, Ricard-Blum S, Ruff KM, Salladini E, Skepo M, Svergun D, Vallet SD, Varadi M, Tompa P, Tosatto SCE, Piovesan D (2021) PED in 2021: a major update of the protein ensemble database for intrinsically disordered proteins. Nucleic Acids Res 49(D1):D404–D411. https://doi.org/10.1093/nar/gkaa1021
Schad E, Ficho E, Pancsa R, Simon I, Dosztanyi Z, Meszaros B (2018) DIBS: a repository of disordered binding sites mediating interactions with ordered proteins. Bioinformatics 34(3):535–537. https://doi.org/10.1093/bioinformatics/btx640
Ficho E, Remenyi I, Simon I, Meszaros B (2017) MFIB: a repository of protein complexes with mutual folding induced by binding. Bioinformatics 33(22):3682–3684. https://doi.org/10.1093/bioinformatics/btx486
Miskei M, Antal C, Fuxreiter M (2017) FuzDB: database of fuzzy complexes, a tool to develop stochastic structure-function relationships for protein complexes and higher-order assemblies. Nucleic Acids Res 45(D1):D228–D235. https://doi.org/10.1093/nar/gkw1019
Vucetic S, Brown C, Dunker K, Obradovic Z (2003) Flavors of protein disorder. Proteins 52:573–584
Karlin D, Ferron F, Canard B, Longhi S (2003) Structural disorder and modular organization in Paramyxovirinae N and P. J Gen Virol 84(Pt 12):3239–3252
Severson W, Xu X, Kuhn M, Senutovitch N, Thokala M, Ferron F, Longhi S, Canard B, Jonsson CB (2005) Essential amino acids of the hantaan virus N protein in its interaction with RNA. J Virol 79(15):10032–10039
Llorente MT, Barreno-Garcia B, Calero M, Camafeita E, Lopez JA, Longhi S, Ferron F, Varela PF, Melero JA (2006) Structural analysis of the human respiratory syncitial virus phosphoprotein: characterization of an a-helical domain involved in oligomerization. J Gen Virol 87:159–169
Habchi J, Mamelli L, Darbon H, Longhi S (2010) Structural disorder within henipavirus nucleoprotein and phosphoprotein: from predictions to experimental assessment. PLoS One 5(7):e11684. https://doi.org/10.1371/journal.pone.0011684
Deng X, Eickholt J, Cheng J (2009) PreDisorder: ab initio sequence-based prediction of protein disordered regions. BMC Bioinformatics 10:436. https://doi.org/10.1186/1471-2105-10-436
Noivirt-Brik O, Prilusky J, Sussman JL (2009) Assessment of disorder predictions in CASP8. Proteins 77(Suppl. 9):210–216. https://doi.org/10.1002/prot.22586
Romero P, Obradovic Z, Li X, Garner EC, Brown CJ, Dunker AK (2001) Sequence complexity of disordered proteins. Proteins 42(1):38–48
Obradovic Z, Peng K, Vucetic S, Radivojac P, Dunker AK (2005) Exploiting heterogeneous sequence properties improves prediction of protein disorder. Proteins 61(Suppl. 7):176–182
Bordoli L, Kiefer F, Schwede T (2007) Assessment of disorder predictions in CASP7. Proteins 69(Suppl. 8):129–136. https://doi.org/10.1002/prot.21671
Obradovic Z, Peng K, Vucetic S, Radivojac P, Brown CJ, Dunker AK (2003) Predicting intrinsic disorder from amino acid sequence. Proteins 53(Suppl. 6):566–572
Linding R, Russell RB, Neduva V, Gibson TJ (2003) GlobPlot: exploring protein sequences for globularity and disorder. Nucleic Acids Res 31(13):3701–3708
Linding R, Jensen LJ, Diella F, Bork P, Gibson TJ, Russell RB (2003) Protein disorder prediction: implications for structural proteomics. Structure (Camb) 11(11):1453–1459
Ward JJ, McGuffin LJ, Bryson K, Buxton BF, Jones DT (2004) The DISOPRED server for the prediction of protein disorder. Bioinformatics 20(13):2138–2139
Orlando G, Raimondi D, Codice F, Tabaro F, Vranken W (2020) Prediction of disordered regions in proteins with recurrent neural networks and protein dynamics. bioRxiv 2020. https://doi.org/10.1101/2020.05.25.115253
Ramraj V (2014) Exploiting whole-PDB analysis in novel bioinformatics applications. University of Oxford
Yang ZR, Thomson R, McNeil P, Esnouf RM (2005) RONN: the bio-basis function neural network technique applied to the detection of natively disordered regions in proteins. Bioinformatics 21(16):3369–3376. https://doi.org/10.1093/bioinformatics/bti534
Lobanov MY, Galzitskaya OV (2011) The Ising model for prediction of disordered residues from protein sequence alone. Phys Biol 8(3):035004. https://doi.org/10.1088/1478-3975/8/3/035004
Lobanov MY, Sokolovskiy IV, Galzitskaya OV (2013) IsUnstruct: prediction of the residue status to be ordered or disordered in the protein chain by a method based on the Ising model. J Biomol Struct Dynam 31(10):1034–1043. https://doi.org/10.1080/07391102.2012.718529
Meng F, Kurgan L (2016) DFLpred: High-throughput prediction of disordered flexible linker regions in protein sequences. Bioinformatics 32(12):i341–i350. https://doi.org/10.1093/bioinformatics/btw280
Cheng J, Sweredoski M, Baldi P (2005) Accurate prediction of protein disordered regions by mining protein structure data. Data Mining Knowl Discov 11:213–222
Pollastri G, McLysaght A (2005) Porter: a new, accurate server for protein secondary structure prediction. Bioinformatics 21(8):1719–1720
Walsh I, Martin AJ, Di Domenico T, Tosatto SC (2012) ESpritz: accurate and fast prediction of protein disorder. Bioinformatics 28(4):503–509. https://doi.org/10.1093/bioinformatics/btr682
Ishida T, Kinoshita K (2007) PrDOS: prediction of disordered protein regions from amino acid sequence. Nucleic Acids Res 35(Web Server issue):W460–W464. https://doi.org/10.1093/nar/gkm363
Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ (1997) Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 25(17):3389–3402. https://doi.org/10.1093/nar/25.17.3389
Hanson J, Paliwal K, Litfin T, Yang Y, Zhou Y (2019) Improving prediction of protein secondary structure, backbone angles, solvent accessibility and contact numbers by using predicted contact maps and an ensemble of recurrent and residual convolutional neural networks. Bioinformatics 35(14):2403–2410. https://doi.org/10.1093/bioinformatics/bty1006
Hanson J, Paliwal KK, Litfin T, Zhou Y (2019) SPOT-Disorder2: improved protein intrinsic disorder prediction by ensembled deep learning. Genom Proteom Bioinform 17(6):645–656. https://doi.org/10.1016/j.gpb.2019.01.004
Hanson J, Paliwal K, Zhou Y (2018) Accurate single-sequence prediction of protein intrinsic disorder by an ensemble of deep recurrent and convolutional architectures. J Chem Inform Model 58(11):2369–2376. https://doi.org/10.1021/acs.jcim.8b00636
Tang YJ, Pang YH, Liu B (2020) IDP-Seq2Seq: identification of intrinsically disordered regions based on sequence to sequence learning. Bioinformatics. https://doi.org/10.1093/bioinformatics/btaa667
Wang S, Ma J, Xu J (2016) AUCpreD: proteome-level protein disorder prediction by AUC-maximized deep convolutional neural fields. Bioinformatics 32(17):i672–i679. https://doi.org/10.1093/bioinformatics/btw446
Meszaros B, Erdos G, Dosztanyi Z (2018) IUPred2A: context-dependent prediction of protein disorder as a function of redox state and protein binding. Nucleic Acids Res 46(W1):W329–W337. https://doi.org/10.1093/nar/gky384
Galzitskaya OV, Garbuzynskiy SO, Lobanov MY (2006) FoldUnfold: web server for the prediction of disordered regions in protein chain. Bioinformatics 22(23):2948–2949
Meszaros B, Simon I, Dosztanyi Z (2009) Prediction of protein binding regions in disordered proteins. PLoS Comput Biol 5(5):e1000376. https://doi.org/10.1371/journal.pcbi.1000376
Cilia E, Pancsa R, Tompa P, Lenaerts T, Vranken WF (2014) The DynaMine webserver: predicting protein dynamics from sequence. Nucleic Acids Res 42(Web Server issue):W264–W270. https://doi.org/10.1093/nar/gku270
Cilia E, Pancsa R, Tompa P, Lenaerts T, Vranken WF (2013) From protein sequence to dynamics and disorder with DynaMine. Nat Commun 4:2741. https://doi.org/10.1038/ncomms3741
Sormanni P, Camilloni C, Fariselli P, Vendruscolo M (2015) The s2D method: simultaneous sequence-based prediction of the statistical populations of ordered and disordered regions in proteins. J Mol Biol 427(4):982–996. https://doi.org/10.1016/j.jmb.2014.12.007
Necci M, Piovesan D, Clementel D, Dosztanyi Z, Tosatto SCE (2020) MobiDB-lite 3.0: fast consensus annotation of intrinsic disorder flavours in proteins. Bioinformatics. https://doi.org/10.1093/bioinformatics/btaa1045
Iqbal S, Hoque MT (2016) Estimation of position specific energy as a feature of protein residues from sequence alone for structural classification. PLoS One 11(9):e0161452. https://doi.org/10.1371/journal.pone.0161452
Faraggi E, Xue B, Zhou Y (2009) Improving the prediction accuracy of residue solvent accessibility and real-value backbone torsion angles of proteins by guided-learning through a two-layer neural network. Proteins 74(4):847–856. https://doi.org/10.1002/prot.22193
Asgari E, Mofrad MR (2015) Continuous distributed representation of biological sequences for deep proteomics and genomics. PLoS One 10(11):e0141287. https://doi.org/10.1371/journal.pone.0141287
Kim SS, Seffernick JT, Lindert S (2018) Accurately predicting disordered regions of proteins using rosetta residuedisorder application. J Phys Chem B 122(14):3920–3930. https://doi.org/10.1021/acs.jpcb.8b01763
Seffernick JT, Ren H, Kim SS, Lindert S (2019) Measuring intrinsic disorder and tracking conformational transitions using Rosetta residue disorder. J Phys Chem B 123(33):7103–7112. https://doi.org/10.1021/acs.jpcb.9b04333
Uversky VN, Gillespie JR, Fink AL (2000) Why are “natively unfolded” proteins unstructured under physiologic conditions? Proteins 41(3):415–427
Kyte J, Doolittle RF (1982) A simple method for displaying the hydropathic character of a protein. J Mol Biol 157(1):105–132. https://doi.org/10.1016/0022-2836(82)90515-0
Zeev-Ben-Mordehai T, Rydberg EH, Solomon A, Toker L, Auld VJ, Silman I, Botti S, Sussman JL (2003) The intracellular domain of the Drosophila cholinesterase-like neural adhesion protein, gliotactin, is natively unfolded. Proteins 53(3):758–767
Oldfield CJ, Cheng Y, Cortese MS, Brown CJ, Uversky VN, Dunker AK (2005) Comparing and combining predictors of mostly disordered proteins. Biochemistry 44(6):1989–2000
Xue B, Oldfield CJ, Dunker AK, Uversky VN (2009) CDF it all: consensus prediction of intrinsically disordered proteins based on various cumulative distribution functions. FEBS Lett 583(9):1469–1474. https://doi.org/10.1016/j.febslet.2009.03.070
Mohan A, Sullivan WJ Jr, Radivojac P, Dunker AK, Uversky VN (2008) Intrinsic disorder in pathogenic and non-pathogenic microbes: discovering and analyzing the unfoldomes of early-branching eukaryotes. Mol BioSyst 4(4):328–340
Bitard-Feildel T, Lamiable A, Mornon JP, Callebaut I (2018) Order in disorder as observed by the “Hydrophobic Cluster Analysis” of protein sequences. Proteomics 18(21–22):e1800054. https://doi.org/10.1002/pmic.201800054
Callebaut I, Labesse G, Durand P, Poupon A, Canard L, Chomilier J, Henrissat B, Mornon JP (1997) Deciphering protein sequence information through hydrophobic cluster analysis (HCA): current status and perspectives. Cell Mol Life Sci 53(8):621–645
Eudes R, Le Tuan K, Delettre J, Mornon JP, Callebaut I (2007) A generalized analysis of hydrophobic and loop clusters within globular protein sequences. BMC Struct Biol 7:2. https://doi.org/10.1186/1472-6807-7-2
Kozlowski LP, Bujnicki JM (2012) MetaDisorder: a meta-server for the prediction of intrinsic disorder in proteins. BMC Bioinformatics 13(1):111. https://doi.org/10.1186/1471-2105-13-111
Li J, Deng X, Eickholt J, Cheng J (2013) Designing and benchmarking the MULTICOM protein structure prediction system. BMC Struct Biol 13:2. https://doi.org/10.1186/1472-6807-13-2
Hou J, Wu T, Cao R, Cheng J (2019) Protein tertiary structure modeling driven by deep learning and contact distance prediction in CASP13. Proteins 87(12):1165–1178. https://doi.org/10.1002/prot.25697
Barik A, Katuwawala A, Hanson J, Paliwal K, Zhou Y, Kurgan L (2020) DEPICTER: intrinsic disorder and disorder function prediction server. J Mol Biol 432(11):3379–3387. https://doi.org/10.1016/j.jmb.2019.12.030
Mizianty MJ, Stach W, Chen K, Kedarisetti KD, Disfani FM, Kurgan L (2010) Improved sequence-based prediction of disordered regions with multilayer fusion of multiple information sources. Bioinformatics 26(18):i489–i496. https://doi.org/10.1093/bioinformatics/btq373
Mizianty MJ, Uversky V, Kurgan L (2014) Prediction of intrinsic disorder in proteins using MFDp2. Methods Mol Biol 1137:147–162. https://doi.org/10.1007/978-1-4939-0366-5_11
Fan X, Kurgan L (2014) Accurate prediction of disorder in protein chains with a comprehensive and empirically designed consensus. J Biomol Struct Dyn 32(3):448–464. https://doi.org/10.1080/07391102.2013.775969
Oldfield CJ, Fan X, Wang C, Dunker AK, Kurgan L (2020) Computational prediction of intrinsic disorder in protein sequences with the disCoP meta-predictor. Methods Mol Biol 2141:21–35. https://doi.org/10.1007/978-1-0716-0524-0_2
Xue B, Dunbrack RL, Williams RW, Dunker AK, Uversky VN (2010) PONDR-FIT: a meta-predictor of intrinsically disordered amino acids. Biochim Biophys Acta (BBA): Bioenergetics 1804(4):996–1010. https://doi.org/10.1016/j.bbapap.2010.01.011
Schlessinger A, Liu J, Rost B (2007) Natively unstructured loops differ from other loops. PLoS Comput Biol 3(7):e140. https://doi.org/10.1371/journal.pcbi.0030140
Schlessinger A, Punta M, Rost B (2007) Natively unstructured regions in proteins identified from contact predictions. Bioinformatics 23(18):2376–2384
Schlessinger A, Punta M, Yachdav G, Kajan L, Rost B (2009) Improved disorder prediction by combination of orthogonal approaches. PLoS One 4(2):e4433. https://doi.org/10.1371/journal.pone.0004433
Schlessinger A, Yachdav G, Rost B (2006) PROFbval: predict flexible and rigid residues in proteins. Bioinformatics 22(7):891–893. https://doi.org/10.1093/bioinformatics/btl032
Chandonia JM (2007) StrBioLib: a Java library for development of custom computational structural biology applications. Bioinformatics 23(15):2018–2020
Necci M, Piovesan D, Dosztanyi Z, Tosatto SCE (2017) MobiDB-lite: fast and highly specific consensus prediction of intrinsic disorder in proteins. Bioinformatics 33(9):1402–1404. https://doi.org/10.1093/bioinformatics/btx015
Katuwawala A, Ghadermarzi S, Hu G, Wu Z, Kurgan L (2021) QUARTERplus: accurate disorder predictions integrated with interpretable residue-level quality assessment scores. Comput Struct Biotechnol J 19:2597–2606. https://doi.org/10.1016/j.csbj.2021.04.066
Blocquel D, Habchi J, Gruet A, Blangy S, Longhi S (2012) Compaction and binding properties of the intrinsically disordered C-terminal domain of Henipavirus nucleoprotein as unveiled by deletion studies. Mol BioSyst 8(1):392–410. https://doi.org/10.1039/c1mb05401e
Uversky VN (2002) Natively unfolded proteins: a point where biology waits for physics. Protein Sci 11(4):739–756
Oldfield CJ, Cheng Y, Cortese MS, Romero P, Uversky VN, Dunker AK (2005) Coupled folding and binding with alpha-helix-forming molecular recognition elements. Biochemistry 44(37):12454–12470. https://doi.org/10.1021/bi050736e
Cheng Y, Oldfield CJ, Meng J, Romero P, Uversky VN, Dunker AK (2007) Mining alpha-helix-forming molecular recognition features with cross species sequence alignments. Biochemistry 46(47):13468–13477. https://doi.org/10.1021/bi7012273
Vacic V, Oldfield CJ, Mohan A, Radivojac P, Cortese MS, Uversky VN, Dunker AK (2007) Characterization of molecular recognition features, MoRFs, and their binding partners. J Proteome Res 6(6):2351–2366
Bourhis J, Johansson K, Receveur-Bréchot V, Oldfield CJ, Dunker AK, Canard B, Longhi S (2004) The C-terminal domain of measles virus nucleoprotein belongs to the class of intrinsically disordered proteins that fold upon binding to their physiological partner. Virus Res 99:157–167
John SP, Wang T, Steffen S, Longhi S, Schmaljohn CS, Jonsson CB (2007) Ebola virus VP30 is an RNA binding protein. J Virol 81(17):8967–8976
Meszaros B, Tompa P, Simon I, Dosztanyi Z (2007) Molecular principles of the interactions of disordered proteins. J Mol Biol 372(2):549–561
Habchi J, Blangy S, Mamelli L, Ringkjobing Jensen M, Blackledge M, Darbon H, Oglesbee M, Shu Y, Longhi S (2011) Characterization of the interactions between the nucleoprotein and the phosphoprotein of Henipaviruses. J Biol Chem 286(15):13583–13602
He H, Zhao J, Sun G (2019) Computational prediction of MoRFs based on protein sequences and minimax probability machine. BMC Bioinformatics 20(1):529. https://doi.org/10.1186/s12859-019-3111-z
Sharma R, Kumar S, Tsunoda T, Patil A, Sharma A (2016) Predicting MoRFs in protein sequences using HMM profiles. BMC Bioinformatics 17(Suppl. 19):504. https://doi.org/10.1186/s12859-016-1375-0
Sharma R, Bayarjargal M, Tsunoda T, Patil A, Sharma A (2018) MoRFPred-plus: computational identification of MoRFs in protein sequences using physicochemical properties and HMM profiles. J Theor Biol 437:9–16. https://doi.org/10.1016/j.jtbi.2017.10.015
Xue B, Dunker AK, Uversky VN (2010) Retro-MoRFs: identifying protein binding sites by normal and reverse alignment and intrinsic disorder prediction. Int J Mol Sci 11(10):3725–3747. https://doi.org/10.3390/ijms11103725
Fang C, Moriwaki Y, Zhu D, Shimizu K (2018) Identifying MoRFs in disordered proteins using enlarged conserved features. In: Paper presented at the Proceedings of the 2018 6th International Conference on Bioinformatics and Computational Biology, Chengdu, China
Fang C, Noguchi T, Tominaga D, Yamana H (2013) MFSPSSMpred: identifying short disorder-to-order binding regions in disordered proteins based on contextual local evolutionary conservation. BMC Bioinformatics 14:300. https://doi.org/10.1186/1471-2105-14-300
Hanson J, Litfin T, Paliwal K, Zhou Y (2020) Identifying molecular recognition features in intrinsically disordered regions of proteins by transfer learning. Bioinformatics 36(4):1107–1113. https://doi.org/10.1093/bioinformatics/btz691
Dosztanyi Z, Meszaros B, Simon I (2009) ANCHOR: web server for predicting protein binding regions in disordered proteins. Bioinformatics 25(20):2745–2746. https://doi.org/10.1093/bioinformatics/btp518
Schramm A, Lieutaud P, Gianni S, Longhi S, Bignon C (2017) InSiDDe: a server for designing artificial disordered proteins. Int J Mol Sci 19(1). https://doi.org/10.3390/ijms19010091
Disfani FM, Hsu WL, Mizianty MJ, Oldfield CJ, Xue B, Dunker AK, Uversky VN, Kurgan L (2012) MoRFpred, a computational tool for sequence-based prediction and characterization of short disorder-to-order transitioning binding regions in proteins. Bioinformatics 28(12):i75–i83. https://doi.org/10.1093/bioinformatics/bts209
Yan J, Dunker AK, Uversky VN, Kurgan L (2016) Molecular recognition features (MoRFs) in three domains of life. Mol BioSyst 12(3):697–710. https://doi.org/10.1039/c5mb00640f
Malhis N, Jacobson M, Gsponer J (2016) MoRFchibi SYSTEM: software tools for the identification of MoRFs in protein sequences. Nucleic Acids Res 44(W1):W488–W493. https://doi.org/10.1093/nar/gkw409
Jones DT, Cozzetto D (2015) DISOPRED3: precise disordered region predictions with annotated protein-binding activity. Bioinformatics 31(6):857–863. https://doi.org/10.1093/bioinformatics/btu744
Sharma R, Raicar G, Tsunoda T, Patil A, Sharma A (2018) OPAL: prediction of MoRF regions in intrinsically disordered protein sequences. Bioinformatics 34(11):1850–1858. https://doi.org/10.1093/bioinformatics/bty032
Sharma R, Sharma A, Raicar G, Tsunoda T, Patil A (2019) OPAL+: length-specific MoRF prediction in intrinsically disordered protein sequences. Proteomics 19(6):e1800058. https://doi.org/10.1002/pmic.201800058
Peng Z, Kurgan L (2015) High-throughput prediction of RNA, DNA and protein binding regions mediated by intrinsic disorder. Nucleic Acids Res 43(18):e121. https://doi.org/10.1093/nar/gkv585
McGuffin LJ, Bryson K, Jones DT (2000) The PSIPRED protein structure prediction server. Bioinformatics 16(4):404–405
Wootton JC (1994) Non-globular domains in protein sequences: automated segmentation using complexity measures. Comput Chem 18(3):269–285
Kall L, Krogh A, Sonnhammer EL (2007) Advantages of combined transmembrane topology and signal peptide prediction--the Phobius web server. Nucleic Acids Res 35(Web Server issue):W429–W432
Bornberg-Bauer E, Rivals E, Vingron M (1998) Computational approaches to identify leucine zippers. Nucleic Acids Res 26(11):2740–2746
Lupas A, Van Dyke M, Stock J (1991) Predicting coiled coils from protein sequences. Science 252(5009):1162–1164
Baldi P, Cheng J, Vullo A (2004) Large-scale prediction of disulphide bond connectivity. Adv Neural Inf Process Syst 17:97–104
Mistry J, Chuguransky S, Williams L, Qureshi M, Salazar Gustavo A, Sonnhammer ELL, Tosatto SCE, Paladin L, Raj S, Richardson LJ, Finn RD, Bateman A (2020) Pfam: the protein families database in 2021. Nucleic Acids Res 49(D1):D412–D419. https://doi.org/10.1093/nar/gkaa913
Sillitoe I, Bordin N, Dawson N, Waman VP, Ashford P, Scholes HM, Pang CSM, Woodridge L, Rauer C, Sen N, Abbasian M, Le Cornu S, Lam SD, Berka K, Varekova Ivana H, Svobodova R, Lees J, Orengo CA (2020) CATH: increased structural coverage of functional space. Nucleic Acids Res 49(D1):D266–D273. https://doi.org/10.1093/nar/gkaa1079
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature
About this protocol
Cite this protocol
Tamburrini, K.C. et al. (2022). Predicting Protein Conformational Disorder and Disordered Binding Sites. In: Carugo, O., Eisenhaber, F. (eds) Data Mining Techniques for the Life Sciences. Methods in Molecular Biology, vol 2449. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2095-3_4
Download citation
DOI: https://doi.org/10.1007/978-1-0716-2095-3_4
Published:
Publisher Name: Humana, New York, NY
Print ISBN: 978-1-0716-2094-6
Online ISBN: 978-1-0716-2095-3
eBook Packages: Springer Protocols