Computational Prediction of Secondary and Supersecondary Structures from Protein Sequences

  • Christopher J. Oldfield
  • Ke Chen
  • Lukasz KurganEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 1958)


Many new methods for the sequence-based prediction of the secondary and supersecondary structures have been developed over the last several years. These and older sequence-based predictors are widely applied for the characterization and prediction of protein structure and function. These efforts have produced countless accurate predictors, many of which rely on state-of-the-art machine learning models and evolutionary information generated from multiple sequence alignments. We describe and motivate both types of predictions. We introduce concepts related to the annotation and computational prediction of the three-state and eight-state secondary structure as well as several types of supersecondary structures, such as β hairpins, coiled coils, and α-turn-α motifs. We review 34 predictors focusing on recent tools and provide detailed information for a selected set of 14 secondary structure and 3 supersecondary structure predictors. We conclude with several practical notes for the end users of these predictive methods.

Key words

Secondary structure prediction Supersecondary structure prediction Beta hairpins Coiled coils Helix-turn-helix Greek key Multiple sequence alignment 



This work was supported by the Qimonda Endowment funds to L.K.


  1. 1.
    Pauling L, Corey RB (1951) The pleated sheet, a new layer configuration of polypeptide chains. Proc Natl Acad Sci 37(5):251–256PubMedCrossRefGoogle Scholar
  2. 2.
    Pauling L, Corey RB, Branson HR (1951) The structure of proteins: two hydrogen-bonded helical configurations of the polypeptide chain. Proc Natl Acad Sci 37(4):205–211PubMedCrossRefGoogle Scholar
  3. 3.
    Anfinsen CB (1973) Principles that govern the folding of protein chains. Science 181(4096):223–230PubMedCrossRefGoogle Scholar
  4. 4.
    Berman HM (2000) The protein data bank. Nucleic Acids Res 28(1):235–242PubMedPubMedCentralCrossRefGoogle Scholar
  5. 5.
    Burley SK, Berman HM, Kleywegt GJ, Markley JL, Nakamura H, Velankar S (2017) Protein data bank (PDB): the single global macromolecular structure archive. Methods Mol Biol 1607:627–641PubMedPubMedCentralCrossRefGoogle Scholar
  6. 6.
    Pruitt KD, Tatusova T, Klimke W, Maglott DR (2009) NCBI Reference Sequences: current status, policy and new initiatives. Nucleic Acids Res 37(Database):D32–D36PubMedCrossRefGoogle Scholar
  7. 7.
    O’Leary NA, Wright MW, Brister JR, Ciufo S, Haddad D, McVeigh R, Rajput B, Robbertse B, Smith-White B, Ako-Adjei D, Astashyn A, Badretdin A, Bao Y, Blinkova O, Brover V, Chetvernin V, Choi J, Cox E, Ermolaeva O, Farrell CM, Goldfarb T, Gupta T, Haft D, Hatcher E, Hlavina W, Joardar VS, Kodali VK, Li W, Maglott D, Masterson P, McGarvey KM, Murphy MR, O’Neill K, Pujar S, Rangwala SH, Rausch D, Riddick LD, Schoch C, Shkeda A, Storz SS, Sun H, Thibaud-Nissen F, Tolstoy I, Tully RE, Vatsan AR, Wallin C, Webb D, Wu W, Landrum MJ, Kimchi A, Tatusova T, DiCuccio M, Kitts P, Murphy TD, Pruitt KD (2016) Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation. Nucleic Acids Res 44(D1):D733–D745PubMedCrossRefGoogle Scholar
  8. 8.
    Gronwald W, Kalbitzer HR (2010) Automated protein NMR structure determination in solution, Methods in molecular biology. Humana Press, TotowaCrossRefGoogle Scholar
  9. 9.
    Chayen NE (2009) High-throughput protein crystallization. Adv Protein Chem Struct Biol 77:1–22PubMedCrossRefGoogle Scholar
  10. 10.
    Zhang Y (2009) Protein structure prediction: when is it useful? Curr Opin Struct Biol 19(2):145–155PubMedPubMedCentralCrossRefGoogle Scholar
  11. 11.
    Ginalski K (2006) Comparative modeling for protein structure prediction. Curr Opin Struct Biol 16(2):172–177PubMedCrossRefGoogle Scholar
  12. 12.
    Mizianty MJ, Fan X, Yan J, Chalmers E, Woloschuk C, Joachimiak A, Kurgan L (2014) Covering complete proteomes with X-ray structures: a current snapshot. Acta Crystallogr D Biol Crystallogr 70(Pt 11):2781–2793PubMedPubMedCentralCrossRefGoogle Scholar
  13. 13.
    Gao J, Wu Z, Hu G, Wang K, Song J, Joachimiak A, Kurgan L (2018) Survey of predictors of propensity for protein production and crystallization with application to predict resolution of crystal structures. Curr Protein Pept Sci 19(2):200–210PubMedGoogle Scholar
  14. 14.
    Grabowski M, Niedzialkowska E, Zimmerman MD, Minor W (2016) The impact of structural genomics: the first quindecennial. J Struct Funct Genom 17(1):1–16CrossRefGoogle Scholar
  15. 15.
    Yang Y, Faraggi E, Zhao H, Zhou Y (2011) Improving protein fold recognition and template-based modeling by employing probabilistic-based matching between predicted one-dimensional structural properties of query and corresponding native properties of templates. Bioinformatics 27(15):2076–2082PubMedPubMedCentralCrossRefGoogle Scholar
  16. 16.
    Roy A, Kucukural A, Zhang Y (2010) I-TASSER: a unified platform for automated protein structure and function prediction. Nat Protoc 5(4):725–738PubMedPubMedCentralCrossRefGoogle Scholar
  17. 17.
    Faraggi E, Yang Y, Zhang S, Zhou Y (2009) Predicting continuous local structure and the effect of its substitution for secondary structure in fragment-free protein structure prediction. Structure 17(11):1515–1527PubMedPubMedCentralCrossRefGoogle Scholar
  18. 18.
    Wu S, Zhang Y (2008) MUSTER: improving protein sequence profile-profile alignments by using multiple sources of structure information. Proteins 72(2):547–556PubMedPubMedCentralCrossRefGoogle Scholar
  19. 19.
    Zhou H, Skolnick J (2007) Ab initio protein structure prediction using chunk-TASSER. Biophys J 93(5):1510–1518PubMedPubMedCentralCrossRefGoogle Scholar
  20. 20.
    Skolnick J (2006) In quest of an empirical potential for protein structure prediction. Curr Opin Struct Biol 16(2):166–171PubMedCrossRefGoogle Scholar
  21. 21.
    Zhang W, Yang J, He B, Walker SE, Zhang H, Govindarajoo B, Virtanen J, Xue Z, Shen HB, Zhang Y (2016) Integration of QUARK and I-TASSER for ab initio protein structure prediction in CASP11. Proteins 84(Suppl 1):76–86PubMedCrossRefGoogle Scholar
  22. 22.
    Czaplewski C, Karczynska A, Sieradzan AK, Liwo A (2018) UNRES server for physics-based coarse-grained simulations and prediction of protein structure, dynamics and thermodynamics. Nucleic Acids Res 46(W1):W304–W309PubMedPubMedCentralCrossRefGoogle Scholar
  23. 23.
    Zhang H, Zhang T, Chen K, Kedarisetti KD, Mizianty MJ, Bao Q, Stach W, Kurgan L (2011) Critical assessment of high-throughput standalone methods for secondary structure prediction. Brief Bioinform 12(6):672–688PubMedCrossRefGoogle Scholar
  24. 24.
    Yang Y, Gao J, Wang J, Heffernan R, Hanson J, Paliwal K, Zhou Y (2018) Sixty-five years of the long march in protein secondary structure prediction: the final stretch? Brief Bioinform 19(3):482–494PubMedGoogle Scholar
  25. 25.
    Pei J, Grishin NV (2007) PROMALS: towards accurate multiple sequence alignments of distantly related proteins. Bioinformatics 23(7):802–808PubMedCrossRefGoogle Scholar
  26. 26.
    Mizianty MJ, Kurgan L (2011) Sequence-based prediction of protein crystallization, purification and production propensity. Bioinformatics 27(13):i24–i33PubMedPubMedCentralCrossRefGoogle Scholar
  27. 27.
    Slabinski L, Jaroszewski L, Rychlewski L, Wilson IA, Lesley SA, Godzik A (2007) XtalPred: a web server for prediction of protein crystallizability. Bioinformatics 23(24):3403–3405PubMedCrossRefPubMedCentralGoogle Scholar
  28. 28.
    Wang H, Feng L, Webb GI, Kurgan L, Song J, Lin D (2017) Critical evaluation of bioinformatics tools for the prediction of protein crystallization propensity. Brief Bioinform.
  29. 29.
    Zhang T, Zhang H, Chen K, Ruan J, Shen S, Kurgan L (2010) Analysis and prediction of RNA-binding residues using sequence, evolutionary conservation, and predicted secondary structure and solvent accessibility. Curr Protein Pept Sci 11(7):609–628PubMedCrossRefGoogle Scholar
  30. 30.
    Yan J, Kurgan L (2017) DRNApred, fast sequence-based method that accurately predicts and discriminates DNA- and RNA-binding residues. Nucleic Acids Res 45(10):e84PubMedPubMedCentralGoogle Scholar
  31. 31.
    Yan J, Friedrich S, Kurgan L (2016) A comprehensive comparative review of sequence-based predictors of DNA- and RNA-binding residues. Brief Bioinform 17(1):88–105PubMedCrossRefGoogle Scholar
  32. 32.
    Peng Z, Kurgan L (2015) High-throughput prediction of RNA, DNA and protein binding regions mediated by intrinsic disorder. Nucleic Acids Res 43(18):e121PubMedPubMedCentralCrossRefGoogle Scholar
  33. 33.
    Pulim V, Bienkowska J, Berger B (2008) LTHREADER: prediction of extracellular ligand-receptor interactions in cytokines using localized threading. Protein Sci 17(2):279–292PubMedPubMedCentralCrossRefGoogle Scholar
  34. 34.
    Fischer JD, Mayer CE, Söding J (2008) Prediction of protein functional residues from sequence by probability density estimation. Bioinformatics 24(5):613–620PubMedCrossRefGoogle Scholar
  35. 35.
    Chen K, Mizianty MJ, Kurgan L (2012) Prediction and analysis of nucleotide-binding residues using sequence and sequence-derived structural descriptors. Bioinformatics 28(3):331–341PubMedCrossRefGoogle Scholar
  36. 36.
    Song J, Tan H, Mahmood K, Law RHP, Buckle AM, Webb GI, Akutsu T, Whisstock JC (2009) Prodepth: predict residue depth by support vector regression approach from protein sequences only. PLoS One 4(9):e7072PubMedPubMedCentralCrossRefGoogle Scholar
  37. 37.
    Zhang H, Zhang T, Chen K, Shen S, Ruan J, Kurgan L (2008) Sequence based residue depth prediction using evolutionary information and predicted secondary structure. BMC Bioinformatics 9(1):388PubMedPubMedCentralCrossRefGoogle Scholar
  38. 38.
    Zheng C, Kurgan L (2008) Prediction of beta-turns at over 80% accuracy based on an ensemble of predicted secondary structures and multiple alignments. BMC Bioinformatics 9:430PubMedPubMedCentralCrossRefGoogle Scholar
  39. 39.
    Mizianty MJ, Kurgan L (2009) Modular prediction of protein structural classes from sequences of twilight-zone identity with predicting sequences. BMC Bioinformatics 10(1):414PubMedPubMedCentralCrossRefGoogle Scholar
  40. 40.
    Kurgan L, Cios K, Chen K (2008) SCPRED: accurate prediction of protein structural class for sequences of twilight-zone similarity with predicting sequences. BMC Bioinformatics 9(1):226PubMedPubMedCentralCrossRefGoogle Scholar
  41. 41.
    Chen K, Kurgan L (2007) PFRES: protein fold classification by using evolutionary information and predicted secondary structure. Bioinformatics 23(21):2843–2850PubMedCrossRefGoogle Scholar
  42. 42.
    Kong L, Zhang L (2014) Novel structure-driven features for accurate prediction of protein structural class. Genomics 103(4):292–297PubMedCrossRefGoogle Scholar
  43. 43.
    Kurgan LA, Zhang T, Zhang H, Shen S, Ruan J (2008) Secondary structure-based assignment of the protein structural classes. Amino Acids 35(3):551–564PubMedCrossRefGoogle Scholar
  44. 44.
    Xue B, Faraggi E, Zhou Y (2009) Predicting residue-residue contact maps by a two-layer, integrated neural-network method. Proteins 76(1):176–183PubMedPubMedCentralCrossRefGoogle Scholar
  45. 45.
    Cheng J, Baldi P (2007) Improved residue contact prediction using support vector machines and a large feature set. BMC Bioinformatics 8(1):113PubMedPubMedCentralCrossRefGoogle Scholar
  46. 46.
    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–i496PubMedPubMedCentralCrossRefGoogle Scholar
  47. 47.
    Mizianty MJ, Zhang T, Xue B, Zhou Y, Dunker A, Uversky VN, Kurgan L (2011) In-silico prediction of disorder content using hybrid sequence representation. BMC Bioinformatics 12(1):245PubMedPubMedCentralCrossRefGoogle Scholar
  48. 48.
    Schlessinger A, Punta M, Yachdav G, Kajan L, Rost B (2009) Improved disorder prediction by combination of orthogonal approaches. PLoS One 4(2):e4433PubMedPubMedCentralCrossRefGoogle Scholar
  49. 49.
    Mizianty MJ, Peng ZL, Kurgan L (2013) MFDp2: Accurate predictor of disorder in proteins by fusion of disorder probabilities, content and profiles. Intrinsically Disord Proteins 1(1):e24428PubMedPubMedCentralCrossRefGoogle Scholar
  50. 50.
    Mizianty MJ, Uversky V, Kurgan L (2014) Prediction of intrinsic disorder in proteins using MFDp2. Methods Mol Biol 1137:147–162PubMedCrossRefGoogle Scholar
  51. 51.
    Walsh I, Martin AJ, Di Domenico T, Vullo A, Pollastri G, Tosatto SC (2011) CSpritz: accurate prediction of protein disorder segments with annotation for homology, secondary structure and linear motifs. Nucleic Acids Res 39(Web Server issue):W190–W196PubMedPubMedCentralCrossRefGoogle Scholar
  52. 52.
    Meng F, Kurgan L (2016) DFLpred: high-throughput prediction of disordered flexible linker regions in protein sequences. Bioinformatics 32(12):i341–i350PubMedPubMedCentralCrossRefGoogle Scholar
  53. 53.
    Yan J, Dunker AK, Uversky VN, Kurgan L (2016) Molecular recognition features (MoRFs) in three domains of life. Mol BioSyst 12(3):697–710PubMedCrossRefGoogle Scholar
  54. 54.
    Sharma R, Raicar G, Tsunoda T, Patil A, Sharma A (2018) OPAL: prediction of MoRF regions in intrinsically disordered protein sequences. Bioinformatics 34(11):1850–1858PubMedCrossRefGoogle Scholar
  55. 55.
    Zhang H, Zhang T, Gao J, Ruan J, Shen S, Kurgan L (2010) Determination of protein folding kinetic types using sequence and predicted secondary structure and solvent accessibility. Amino Acids 42(1):271–283PubMedCrossRefGoogle Scholar
  56. 56.
    Gao J, Zhang T, Zhang H, Shen S, Ruan J, Kurgan L (2010) Accurate prediction of protein folding rates from sequence and sequence-derived residue flexibility and solvent accessibility. Proteins 78(9):2114–2130PubMedGoogle Scholar
  57. 57.
    Jiang Y, Iglinski P, Kurgan L (2009) Prediction of protein folding rates from primary sequences using hybrid sequence representation. J Comput Chem 30(5):772–783PubMedCrossRefGoogle Scholar
  58. 58.
    Bryson K, McGuffin LJ, Marsden RL, Ward JJ, Sodhi JS, Jones DT (2005) Protein structure prediction servers at University College London. Nucleic Acids Res 33(Web Server):W36–W38PubMedPubMedCentralCrossRefGoogle Scholar
  59. 59.
    Kurgan L, Miri Disfani F (2011) Structural protein descriptors in 1-dimension and their sequence-based predictions. Curr Protein Pept Sci 12(6):470–489PubMedCrossRefGoogle Scholar
  60. 60.
    Jones DT (1999) Protein secondary structure prediction based on position-specific scoring matrices. J Mol Biol 292(2):195–202PubMedCrossRefGoogle Scholar
  61. 61.
    Buchan DWA, Ward SM, Lobley AE, Nugent TCO, Bryson K, Jones DT (2010) Protein annotation and modelling servers at University College London. Nucleic Acids Res 38(Web Server):W563–W568PubMedPubMedCentralCrossRefGoogle Scholar
  62. 62.
    Rost B (1996) PHD: predicting one-dimensional protein structure by profile-based neural networks. Methods Enzymol 266:525–539PubMedCrossRefGoogle Scholar
  63. 63.
    Rost B, Yachdav G, Liu J (2004) The PredictProtein server. Nucleic Acids Res 32(Web Server):W321–W326PubMedPubMedCentralCrossRefGoogle Scholar
  64. 64.
    O’Donnell CW, Waldispühl J, Lis M, Halfmann R, Devadas S, Lindquist S, Berger B (2011) A method for probing the mutational landscape of amyloid structure. Bioinformatics 27(13):i34–i42PubMedPubMedCentralCrossRefGoogle Scholar
  65. 65.
    Bryan AW, Menke M, Cowen LJ, Lindquist SL, Berger B (2009) BETASCAN: probable β-amyloids identified by pairwise probabilistic analysis. PLoS Comput Biol 5(3):e1000333PubMedPubMedCentralCrossRefGoogle Scholar
  66. 66.
    Bradley P, Cowen L, Menke M, King J, Berger B (2001) BETAWRAP: successful prediction of parallel β-helices from primary sequence reveals an association with many microbial pathogens. Proc Natl Acad Sci 98(26):14819–14824PubMedCrossRefGoogle Scholar
  67. 67.
    Hornung T, Volkov OA, Zaida TMA, Delannoy S, Wise JG, Vogel PD (2008) Structure of the cytosolic part of the subunit b-dimer of Escherichia coli F0F1-ATP synthase. Biophys J 94(12):5053–5064PubMedPubMedCentralCrossRefGoogle Scholar
  68. 68.
    Sun ZR, Cui Y, Ling LJ, Guo Q, Chen RS (1998) Molecular dynamics simulation of protein folding with supersecondary structure constraints. J Protein Chem 17(8):765–769PubMedCrossRefPubMedCentralGoogle Scholar
  69. 69.
    Szappanos B, Süveges D, Nyitray L, Perczel A, Gáspári Z (2010) Folded-unfolded cross-predictions and protein evolution: the case study of coiled-coils. FEBS Lett 584(8):1623–1627PubMedCrossRefPubMedCentralGoogle Scholar
  70. 70.
    Rackham OJL, Madera M, Armstrong CT, Vincent TL, Woolfson DN, Gough J (2010) The evolution and structure prediction of coiled coils across all genomes. J Mol Biol 403(3):480–493PubMedCrossRefPubMedCentralGoogle Scholar
  71. 71.
    Gerstein M, Hegyi H (1998) Comparing genomes in terms of protein structure: surveys of a finite parts list. FEMS Microbiol Rev 22(4):277–304PubMedCrossRefPubMedCentralGoogle Scholar
  72. 72.
    Reddy CCS, Shameer K, Offmann BO, Sowdhamini R (2008) PURE: a webserver for the prediction of domains in unassigned regions in proteins. BMC Bioinformatics 9(1):281PubMedPubMedCentralCrossRefGoogle Scholar
  73. 73.
    de la Cruz X, Hutchinson EG, Shepherd A, Thornton JM (2002) Toward predicting protein topology: an approach to identifying β hairpins. Proc Natl Acad Sci 99(17):11157–11162PubMedCrossRefGoogle Scholar
  74. 74.
    Kumar M, Bhasin M, Natt NK, Raghava GPS (2005) BhairPred: prediction of β-hairpins in a protein from multiple alignment information using ANN and SVM techniques. Nucleic Acids Res 33(Web Server):W154–W159PubMedPubMedCentralCrossRefGoogle Scholar
  75. 75.
    Barton GJ (1995) Protein secondary structure prediction. Curr Opin Struct Biol 5(3):372–376PubMedCrossRefGoogle Scholar
  76. 76.
    Heringa J (2000) Computational methods for protein secondary structure prediction using multiple sequence alignments. Curr Protein Pept Sci 1(3):273–301PubMedCrossRefGoogle Scholar
  77. 77.
    Rost B (2001) Protein secondary structure prediction continues to rise. J Struct Biol 134(2–3):204–218PubMedCrossRefGoogle Scholar
  78. 78.
    Albrecht M, Tosatto SCE, Lengauer T, Valle G (2003) Simple consensus procedures are effective and sufficient in secondary structure prediction. Protein Eng Des Sel 16(7):459–462CrossRefGoogle Scholar
  79. 79.
    Yan J, Marcus M, Kurgan L (2014) Comprehensively designed consensus of standalone secondary structure predictors improves Q3 by over 3%. J Biomol Struct Dyn 32(1):36–51PubMedCrossRefGoogle Scholar
  80. 80.
    Rost B (2009) Prediction of protein structure in 1D—secondary structure, membrane regions, and solvent accessibility. Structural bioinformatics, 2nd edn. Wiley, New YorkGoogle Scholar
  81. 81.
    Pirovano W, Heringa J (2010) Protein secondary structure prediction. Methods Mol Biol 609:327–348PubMedCrossRefGoogle Scholar
  82. 82.
    Meng F, Kurgan L (2016) Computational prediction of protein secondary structure from sequence. Curr Protoc Protein Sci 86:2.3.1–2.3.10CrossRefGoogle Scholar
  83. 83.
    Singh M (2006) Predicting protein secondary and supersecondary structure, Chapman & Hall/CRC Computer & Information Science Series. Chapman and Hall/CRC, New YorkGoogle Scholar
  84. 84.
    Gruber M, Söding J, Lupas AN (2006) Comparative analysis of coiled-coil prediction methods. J Struct Biol 155(2):140–145PubMedCrossRefGoogle Scholar
  85. 85.
    Li C, Ching Han Chang C, Nagel J, Porebski BT, Hayashida M, Akutsu T, Song J, Buckle AM (2016) Critical evaluation of in silico methods for prediction of coiled-coil domains in proteins. Brief Bioinform 17(2):270–282PubMedCrossRefGoogle Scholar
  86. 86.
    Ho HK, Zhang L, Ramamohanarao K, Martin S (2013) A survey of machine learning methods for secondary and supersecondary protein structure prediction. Methods Mol Biol 932:87–106PubMedCrossRefGoogle Scholar
  87. 87.
    Chen K, Kurgan L (2013) Computational prediction of secondary and supersecondary structures. Methods Mol Biol 932:63–86PubMedCrossRefGoogle Scholar
  88. 88.
    Kolodny R, Honig B (2006) VISTAL—a new 2D visualization tool of protein 3D structural alignments. Bioinformatics 22(17):2166–2167PubMedCrossRefGoogle Scholar
  89. 89.
    Moreland JL, Gramada A, Buzko OV, Zhang Q, Bourne PE (2005) BMC Bioinformatics 6(1):21PubMedPubMedCentralCrossRefGoogle Scholar
  90. 90.
    Porollo AA, Adamczak R, Meller J (2004) POLYVIEW: a flexible visualization tool for structural and functional annotations of proteins. Bioinformatics 20(15):2460–2462PubMedCrossRefGoogle Scholar
  91. 91.
    Murzin AG, Brenner SE, Hubbard T, Chothia C (1995) SCOP: A structural classification of proteins database for the investigation of sequences and structures. J Mol Biol 247(4):536–540PubMedGoogle Scholar
  92. 92.
    Orengo CA, Michie AD, Jones S, Jones DT, Swindells MB, Thornton JM (1997) CATH—a hierarchic classification of protein domain structures. Structure 5(8):1093–1109PubMedCrossRefGoogle Scholar
  93. 93.
    Andreeva A, Howorth D, Chandonia JM, Brenner SE, Hubbard TJP, Chothia C, Murzin AG (2007) Data growth and its impact on the SCOP database: new developments. Nucleic Acids Res 36(Database):D419–D425PubMedPubMedCentralCrossRefGoogle Scholar
  94. 94.
    Cuff AL, Sillitoe I, Lewis T, Clegg AB, Rentzsch R, Furnham N, Pellegrini-Calace M, Jones D, Thornton J, Orengo CA (2011) Extending CATH: increasing coverage of the protein structure universe and linking structure with function. Nucleic Acids Res 39(Database):D420–D426PubMedCrossRefGoogle Scholar
  95. 95.
    Sillitoe I, Dawson N, Thornton J, Orengo C (2015) The history of the CATH structural classification of protein domains. Biochimie 119:209–217PubMedPubMedCentralCrossRefGoogle Scholar
  96. 96.
    Andreeva A, Howorth D, Chandonia JM, Brenner SE, Hubbard TJ, Chothia C, Murzin AG (2008) Data growth and its impact on the SCOP database: new developments. Nucleic Acids Res 36(Database issue):D419–D425PubMedGoogle Scholar
  97. 97.
    Levitt M, Greer J (1977) Automatic identification of secondary structure in globular proteins. J Mol Biol 114(2):181–239PubMedCrossRefGoogle Scholar
  98. 98.
    Kabsch W, Sander C (1983) Dictionary of protein secondary structure: Pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 22(12):2577–2637CrossRefGoogle Scholar
  99. 99.
    Richards FM, Kundrot CE (1988) Identification of structural motifs from protein coordinate data: secondary structure and first-level supersecondary structure. Proteins Struct Funct Genet 3(2):71–84PubMedCrossRefGoogle Scholar
  100. 100.
    Sklenar H, Etchebest C, Lavery R (1989) Describing protein structure: a general algorithm yielding complete helicoidal parameters and a unique overall axis. Proteins Struct Funct Genet 6(1):46–60PubMedCrossRefGoogle Scholar
  101. 101.
    Frishman D, Argos P (1995) Knowledge-based protein secondary structure assignment. Proteins Struct Funct Genet 23(4):566–579PubMedCrossRefGoogle Scholar
  102. 102.
    Labesse G, Colloc'h N, Pothier J, Mornon JP (1997) P-SEA: a new efficient assignment of secondary structure from Cα trace of proteins. Bioinformatics 13(3):291–295CrossRefGoogle Scholar
  103. 103.
    King SM, Johnson WC (1999) Assigning secondary structure from protein coordinate data. Proteins Struct Funct Genet 35(3):313–320PubMedCrossRefGoogle Scholar
  104. 104.
    Fodje MN, Al-Karadaghi S (2002) Occurrence, conformational features and amino acid propensities for the π-helix. Protein Eng Des Sel 15(5):353–358CrossRefGoogle Scholar
  105. 105.
    Martin J, Letellier G, Marin A, Taly J-F, de Brevern AG, Gibrat J-F (2005) BMC Struct Biol 5(1):17PubMedPubMedCentralCrossRefGoogle Scholar
  106. 106.
    Cubellis M, Cailliez F, Lovell SC (2005) Secondary structure assignment that accurately reflects physical and evolutionary characteristics. BMC Bioinformatics 6(Suppl 4):S8PubMedPubMedCentralCrossRefGoogle Scholar
  107. 107.
    Majumdar I, Krishna SS, Grishin NV (2005) PALSSE: a program to delineate linear secondary structural elements from protein structures. BMC Bioinformatics 6(1):202PubMedPubMedCentralCrossRefGoogle Scholar
  108. 108.
    Zhang W, Dunker AK, Zhou Y (2008) Assessing secondary structure assignment of protein structures by using pairwise sequence-alignment benchmarks. Proteins 71(1):61–67PubMedCrossRefGoogle Scholar
  109. 109.
    Hosseini S-R, Sadeghi M, Pezeshk H, Eslahchi C, Habibi M (2008) PROSIGN: A method for protein secondary structure assignment based on three-dimensional coordinates of consecutive Cα atoms. Comput Biol Chem 32(6):406–411PubMedCrossRefGoogle Scholar
  110. 110.
    Park S-Y, Yoo M-J, Shin J-M, Cho K-H (2011) SABA (secondary structure assignment program based on only alpha carbons): a novel pseudo center geometrical criterion for accurate assignment of protein secondary structures. BMB Rep 44(2):118–122PubMedCrossRefGoogle Scholar
  111. 111.
    Zacharias J, Knapp EW (2014) Protein secondary structure classification revisited: processing DSSP information with PSSC. J Chem Inf Model 54(7):2166–2179PubMedCrossRefGoogle Scholar
  112. 112.
    Law SM, Frank AT, Brooks CL 3rd (2014) PCASSO: a fast and efficient Calpha-based method for accurately assigning protein secondary structure elements. J Comput Chem 35(24):1757–1761PubMedPubMedCentralCrossRefGoogle Scholar
  113. 113.
    Cao C, Wang GS, Liu A, Xu ST, Wang LC, Zou SX (2016) A new secondary structure assignment algorithm using C-alpha backbone fragments. Int J Mol Sci 17(3):333PubMedPubMedCentralCrossRefGoogle Scholar
  114. 114.
    Klose DP, Wallace BA, Janes RW (2010) 2Struc: the secondary structure server. Bioinformatics 26(20):2624–2625PubMedPubMedCentralCrossRefGoogle Scholar
  115. 115.
    Moult J, Pedersen JT, Judson R, Fidelis K (1995) A large-scale experiment to assess protein structure prediction methods. Proteins Struct Funct Genet 23(3):ii–ivPubMedCrossRefGoogle Scholar
  116. 116.
    Koh IYY (2003) EVA: evaluation of protein structure prediction servers. Nucleic Acids Res 31(13):3311–3315PubMedPubMedCentralCrossRefGoogle Scholar
  117. 117.
    Parry DAD, Fraser RDB, Squire JM (2008) Fifty years of coiled-coils and α-helical bundles: a close relationship between sequence and structure. J Struct Biol 163(3):258–269PubMedCrossRefGoogle Scholar
  118. 118.
    Truebestein L, Leonard TA (2016) Coiled-coils: the long and short of it. BioEssays 38(9):903–916PubMedPubMedCentralCrossRefGoogle Scholar
  119. 119.
    Pellegrini-Calace M (2005) Detecting DNA-binding helix-turn-helix structural motifs using sequence and structure information. Nucleic Acids Res 33(7):2129–2140PubMedPubMedCentralCrossRefGoogle Scholar
  120. 120.
    Aravind L, Anantharaman V, Balaji S, Babu MM, Iyer LM (2005) The many faces of the helix-turn-helix domain: transcription regulation and beyond. FEMS Microbiol Rev 29(2):231–262PubMedCrossRefGoogle Scholar
  121. 121.
    Hutchinson EG, Thornton JM (1996) PROMOTIF-A program to identify and analyze structural motifs in proteins. Protein Sci 5(2):212–220PubMedPubMedCentralCrossRefGoogle Scholar
  122. 122.
    Walshaw J, Woolfson DN (2001) SOCKET: a program for identifying and analysing coiled-coil motifs within protein structures. J Mol Biol 307(5):1427–1450PubMedCrossRefGoogle Scholar
  123. 123.
    Testa OD, Moutevelis E, Woolfson DN (2009) CC+: a relational database of coiled-coil structures. Nucleic Acids Res 37(Database):D315–D322PubMedCrossRefGoogle Scholar
  124. 124.
    Michalopoulos I (2004) TOPS: an enhanced database of protein structural topology. Nucleic Acids Res 32(90001):D251–D254PubMedPubMedCentralCrossRefGoogle Scholar
  125. 125.
    Rost B, Sander C (1993) Improved prediction of protein secondary structure by use of sequence profiles and neural networks. Proc Natl Acad Sci 90(16):7558–7562PubMedCrossRefGoogle Scholar
  126. 126.
    Altschul S (1997) Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 25(17):3389–3402PubMedPubMedCentralCrossRefGoogle Scholar
  127. 127.
    Fang C, Shang Y, Xu D (2018) MUFOLD-SS: new deep inception-inside-inception networks for protein secondary structure prediction. Proteins 86(5):592–598PubMedPubMedCentralCrossRefGoogle Scholar
  128. 128.
    Heffernan R, Yang Y, Paliwal K, Zhou Y (2017) Capturing non-local interactions by long short-term memory bidirectional recurrent neural networks for improving prediction of protein secondary structure, backbone angles, contact numbers and solvent accessibility. Bioinformatics 33(18):2842–2849PubMedCrossRefPubMedCentralGoogle Scholar
  129. 129.
    Wang S, Li W, Liu S, Xu J (2016) RaptorX-Property: a web server for protein structure property prediction. Nucleic Acids Res 44(W1):W430–W435PubMedPubMedCentralCrossRefGoogle Scholar
  130. 130.
    Wang S, Peng J, Ma J, Xu J (2016) Protein secondary structure prediction using deep convolutional neural fields. Sci Rep 6:18962PubMedPubMedCentralCrossRefGoogle Scholar
  131. 131.
    Cole C, Barber JD, Barton GJ (2008) The Jpred 3 secondary structure prediction server. Nucleic Acids Res 36(Web Server):W197–W201PubMedPubMedCentralCrossRefGoogle Scholar
  132. 132.
    Cuff JA, Clamp ME, Siddiqui AS, Finlay M, Barton GJ (1998) JPred: a consensus secondary structure prediction server. Bioinformatics 14(10):892–893PubMedCrossRefGoogle Scholar
  133. 133.
    Cuff JA, Barton GJ (2000) Application of multiple sequence alignment profiles to improve protein secondary structure prediction. Proteins Struct Funct Genet 40(3):502–511PubMedCrossRefGoogle Scholar
  134. 134.
    Drozdetskiy A, Cole C, Procter J, Barton GJ (2015) JPred4: a protein secondary structure prediction server. Nucleic Acids Res 43(W1):W389–W394PubMedPubMedCentralCrossRefGoogle Scholar
  135. 135.
    Yaseen A, Li Y (2014) Context-based features enhance protein secondary structure prediction accuracy. J Chem Inf Model 54(3):992–1002PubMedCrossRefGoogle Scholar
  136. 136.
    Buchan DW, Minneci F, Nugent TC, Bryson K, Jones DT (2013) Scalable web services for the PSIPRED Protein Analysis Workbench. Nucleic Acids Res 41(Web Server issue):W349–W357PubMedPubMedCentralCrossRefGoogle Scholar
  137. 137.
    Pollastri G, Martin AJM, Mooney C, Vullo A (2007) Accurate prediction of protein secondary structure and solvent accessibility by consensus combiners of sequence and structure information. BMC Bioinformatics 8(1):201PubMedPubMedCentralCrossRefGoogle Scholar
  138. 138.
    Pollastri G, McLysaght A (2005) Porter: a new, accurate server for protein secondary structure prediction. Bioinformatics 21(8):1719–1720PubMedCrossRefGoogle Scholar
  139. 139.
    Mirabello C, Pollastri G (2013) Porter, PaleAle 4.0: high-accuracy prediction of protein secondary structure and relative solvent accessibility. Bioinformatics 29(16):2056–2058PubMedCrossRefGoogle Scholar
  140. 140.
    Bettella F, Rasinski D, Knapp EW (2012) Protein secondary structure prediction with SPARROW. J Chem Inf Model 52(2):545–556PubMedCrossRefGoogle Scholar
  141. 141.
    Zhou T, Shu N, Hovmöller S (2010) A novel method for accurate one-dimensional protein structure prediction based on fragment matching. Bioinformatics 26(4):470–477PubMedCrossRefGoogle Scholar
  142. 142.
    Kountouris P, Hirst JD (2009) Prediction of backbone dihedral angles and protein secondary structure using support vector machines. BMC Bioinformatics 10(1):437PubMedPubMedCentralCrossRefGoogle Scholar
  143. 143.
    Green JR, Korenberg MJ, Aboul-Magd MO (2009) PCI-SS: MISO dynamic nonlinear protein secondary structure prediction. BMC Bioinformatics 10:222–222PubMedPubMedCentralCrossRefGoogle Scholar
  144. 144.
    Montgomerie S, Cruz JA, Shrivastava S, Arndt D, Berjanskii M, Wishart DS (2008) PROTEUS2: a web server for comprehensive protein structure prediction and structure-based annotation. Nucleic Acids Res 36(Web Server):W202–W209PubMedPubMedCentralCrossRefGoogle Scholar
  145. 145.
    Montgomerie S, Sundararaj S, Gallin WJ, Wishart DS (2006) Improving the accuracy of protein secondary structure prediction using structural alignment. BMC Bioinformatics 7:301PubMedPubMedCentralCrossRefGoogle Scholar
  146. 146.
    Martin J, Gibrat JF, Rodolphe F (2006) Analysis of an optimal hidden Markov model for secondary structure prediction. BMC Struct Biol 6:25PubMedPubMedCentralCrossRefGoogle Scholar
  147. 147.
    Karypis G (2006) YASSPP: better kernels and coding schemes lead to improvements in protein secondary structure prediction. Proteins 64(3):575–586PubMedCrossRefGoogle Scholar
  148. 148.
    Lin K, Simossis VA, Taylor WR, Heringa J (2005) A simple and fast secondary structure prediction method using hidden neural networks. Bioinformatics 21(2):152–159PubMedCrossRefGoogle Scholar
  149. 149.
    Adamczak R, Porollo A, Meller J (2005) Combining prediction of secondary structure and solvent accessibility in proteins. Proteins 59(3):467–475PubMedCrossRefGoogle Scholar
  150. 150.
    Cheng J, Randall AZ, Sweredoski MJ, Baldi P (2005) SCRATCH: a protein structure and structural feature prediction server. Nucleic Acids Res 33(Web Server):W72–W76PubMedPubMedCentralCrossRefGoogle Scholar
  151. 151.
    Pollastri G, Przybylski D, Rost B, Baldi P (2002) Improving the prediction of protein secondary structure in three and eight classes using recurrent neural networks and profiles. Proteins Struct Funct Genet 47(2):228–235PubMedCrossRefGoogle Scholar
  152. 152.
    Madera M, Calmus R, Thiltgen G, Karplus K, Gough J (2010) Improving protein secondary structure prediction using a simple k-mer model. Bioinformatics 26(5):596–602PubMedPubMedCentralCrossRefGoogle Scholar
  153. 153.
    Yang Y, Heffernan R, Paliwal K, Lyons J, Dehzangi A, Sharma A, Wang J, Sattar A, Zhou Y (2017) SPIDER2: a package to predict secondary structure, accessible surface area, and main-chain torsional angles by deep neural networks. Methods Mol Biol 1484:55–63PubMedCrossRefGoogle Scholar
  154. 154.
    Heffernan R, Paliwal K, Lyons J, Dehzangi A, Sharma A, Wang J, Sattar A, Yang Y, Zhou Y (2015) Improving prediction of secondary structure, local backbone angles, and solvent accessible surface area of proteins by iterative deep learning. Sci Rep 5:11476PubMedPubMedCentralCrossRefGoogle Scholar
  155. 155.
    Faraggi E, Zhang T, Yang Y, Kurgan L, Zhou Y (2012) SPINE X: improving protein secondary structure prediction by multistep learning coupled with prediction of solvent accessible surface area and backbone torsion angles. J Comput Chem 33(3):259–267PubMedCrossRefPubMedCentralGoogle Scholar
  156. 156.
    Schuster M, Paliwal KK (1997) Bidirectional recurrent neural networks. IEEE Trans Signal Process 45(11):2673–2681CrossRefGoogle Scholar
  157. 157.
    Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780PubMedCrossRefGoogle Scholar
  158. 158.
    Remmert M, Biegert A, Hauser A, Soding J (2011) HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment. Nat Methods 9(2):173–175PubMedCrossRefGoogle Scholar
  159. 159.
    Lupas A, Van Dyke M, Stock J (1991) Predicting coiled coils from protein sequences. Science 252(5009):1162–1164PubMedCrossRefGoogle Scholar
  160. 160.
    Eddy SR (1998) Profile hidden Markov models. Bioinformatics 14(9):755–763PubMedCrossRefGoogle Scholar
  161. 161.
    Baú D, Martin AJM, Mooney C, Vullo A, Walsh I, Pollastri G (2006) Distill: a suite of web servers for the prediction of one-, two- and three-dimensional structural features of proteins. BMC Bioinformatics 7(1):402PubMedPubMedCentralCrossRefGoogle Scholar
  162. 162.
    Mooney C, Pollastri G (2009) Beyond the Twilight Zone: automated prediction of structural properties of proteins by recursive neural networks and remote homology information. Proteins 77(1):181–190PubMedCrossRefGoogle Scholar
  163. 163.
    Cuff JA, Barton GJ (2000) Application of multiple sequence alignment profiles to improve protein secondary structure prediction. Proteins 40(3):502–511PubMedCrossRefGoogle Scholar
  164. 164.
    Eyrich VA, Marti-Renom MA, Przybylski D, Madhusudhan MS, Fiser A, Pazos F, Valencia A, Sali A, Rost B (2001) EVA: continuous automatic evaluation of protein structure prediction servers. Bioinformatics 17(12):1242–1243PubMedCrossRefGoogle Scholar
  165. 165.
    Jia S-C, Hu X-Z (2011) Using random forest algorithm to predict β-hairpin motifs. Protein Pept Lett 18(6):609–617PubMedCrossRefPubMedCentralGoogle Scholar
  166. 166.
    Xia J-F, Wu M, You Z-H, Zhao X-M, Li X-L (2010) Prediction of β-hairpins in proteins using physicochemical properties and structure information. Protein Pept Lett 17(9):1123–1128PubMedCrossRefGoogle Scholar
  167. 167.
    Zou D, He Z, He J (2009) β-Hairpin prediction with quadratic discriminant analysis using diversity measure. J Comput Chem 30(14):2277–2284PubMedGoogle Scholar
  168. 168.
    Hu XZ, Li QZ (2008) Prediction of the β-hairpins in proteins using support vector machine. Protein J 27(2):115–122PubMedCrossRefGoogle Scholar
  169. 169.
    Kuhn M, Meiler J, Baker D (2004) Strand-loop-strand motifs: Prediction of hairpins and diverging turns in proteins. Proteins 54(2):282–288PubMedCrossRefGoogle Scholar
  170. 170.
    Singh H, Raghava GPS (2016) BLAST-based structural annotation of protein residues using Protein Data Bank. Biol Direct 11:4PubMedPubMedCentralCrossRefGoogle Scholar
  171. 171.
    Bartoli L, Fariselli P, Krogh A, Casadio R (2009) CCHMM_PROF: a HMM-based coiled-coil predictor with evolutionary information. Bioinformatics 25(21):2757–2763PubMedCrossRefPubMedCentralGoogle Scholar
  172. 172.
    McDonnell AV, Jiang T, Keating AE, Berger B (2006) Paircoil2: improved prediction of coiled coils from sequence. Bioinformatics 22(3):356–358PubMedCrossRefGoogle Scholar
  173. 173.
    Mason JM, Schmitz MA, Muller KM, Arndt KM (2006) Semirational design of Jun-Fos coiled coils with increased affinity: universal implications for leucine zipper prediction and design. Proc Natl Acad Sci 103(24):8989–8994PubMedCrossRefGoogle Scholar
  174. 174.
    Gruber M, Soding J, Lupas AN (2005) REPPER—repeats and their periodicities in fibrous proteins. Nucleic Acids Res 33(Web Server):W239–W243PubMedPubMedCentralCrossRefGoogle Scholar
  175. 175.
    Delorenzi M, Speed T (2002) An HMM model for coiled-coil domains and a comparison with PSSM-based predictions. Bioinformatics 18(4):617–625PubMedCrossRefGoogle Scholar
  176. 176.
    Dodd IB, Egan JB (1990) Improved detection of helix-turn-helix DNA-binding motifs in protein sequences. Nucleic Acids Res 18(17):5019–5026PubMedPubMedCentralCrossRefGoogle Scholar
  177. 177.
    Narasimhan G, Bu C, Gao Y, Wang X, Xu N, Mathee K (2002) Mining protein sequences for motifs. J Comput Biol 9(5):707–720PubMedCrossRefGoogle Scholar
  178. 178.
    Xiong W, Li T, Chen K, Tang K (2009) Local combinational variables: an approach used in DNA-binding helix-turn-helix motif prediction with sequence information. Nucleic Acids Res 37(17):5632–5640PubMedPubMedCentralCrossRefGoogle Scholar
  179. 179.
    Trigg J, Gutwin K, Keating AE, Berger B (2011) Multicoil2: predicting coiled coils and their oligomerization states from sequence in the twilight zone. PLoS One 6(8):e23519PubMedPubMedCentralCrossRefGoogle Scholar
  180. 180.
    Wolf E, Kim PS, Berger B (1997) MultiCoil: a program for predicting two-and three-stranded coiled coils. Protein Sci 6(6):1179–1189PubMedPubMedCentralCrossRefGoogle Scholar
  181. 181.
    Ahmad S, Gromiha MM (2002) NETASA: neural network based prediction of solvent accessibility. Bioinformatics 18(6):819–824PubMedCrossRefGoogle Scholar
  182. 182.
    Berger B, Wilson DB, Wolf E, Tonchev T, Milla M, Kim PS (1995) Predicting coiled coils by use of pairwise residue correlations. Proc Natl Acad Sci U S A 92(18):8259–8263PubMedPubMedCentralCrossRefGoogle Scholar
  183. 183.
    Fischer D, Barret C, Bryson K, Elofsson A, Godzik A, Jones D, Karplus KJ, Kelley LA, MacCallum RM, Pawowski K, Rost B, Rychlewski L, Sternberg M (1999) CAFASP-1: critical assessment of fully automated structure prediction methods. Proteins Suppl 3:209–217PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Christopher J. Oldfield
    • 1
  • Ke Chen
    • 2
  • Lukasz Kurgan
    • 1
    Email author
  1. 1.Department of Computer Science, College of EngineeringVirginia Commonwealth UniversityRichmondUSA
  2. 2.School of Computer Science and Software EngineeringTianjin Polytechnic UniversityTianjinPeople’s Republic of China

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