Skip to main content
SpringerLink
Log in

Computational Prediction of Secondary and Supersecondary Structures from Protein Sequences

  • Protocol
  • First Online:
Protein Supersecondary Structures

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1958))

Abstract

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.

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

Access this chapter

Log in via an institution
Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Pauling L, Corey RB (1951) The pleated sheet, a new layer configuration of polypeptide chains. Proc Natl Acad Sci 37(5):251–256

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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):205–211

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Anfinsen CB (1973) Principles that govern the folding of protein chains. Science 181(4096):223–230

    Article  CAS  PubMed  Google Scholar 

  4. Berman HM (2000) The protein data bank. Nucleic Acids Res 28(1):235–242

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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 1607:627–641

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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–D36

    Article  CAS  PubMed  Google Scholar 

  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–D745

    Article  PubMed  CAS  Google Scholar 

  8. Gronwald W, Kalbitzer HR (2010) Automated protein NMR structure determination in solution, Methods in molecular biology. Humana Press, Totowa

    Book  Google Scholar 

  9. Chayen NE (2009) High-throughput protein crystallization. Adv Protein Chem Struct Biol 77:1–22

    Article  CAS  PubMed  Google Scholar 

  10. Zhang Y (2009) Protein structure prediction: when is it useful? Curr Opin Struct Biol 19(2):145–155

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Ginalski K (2006) Comparative modeling for protein structure prediction. Curr Opin Struct Biol 16(2):172–177

    Article  CAS  PubMed  Google Scholar 

  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):2781–2793

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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):200–210

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Grabowski M, Niedzialkowska E, Zimmerman MD, Minor W (2016) The impact of structural genomics: the first quindecennial. J Struct Funct Genom 17(1):1–16

    Article  CAS  Google Scholar 

  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):2076–2082

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Roy A, Kucukural A, Zhang Y (2010) I-TASSER: a unified platform for automated protein structure and function prediction. Nat Protoc 5(4):725–738

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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):1515–1527

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Wu S, Zhang Y (2008) MUSTER: improving protein sequence profile-profile alignments by using multiple sources of structure information. Proteins 72(2):547–556

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Zhou H, Skolnick J (2007) Ab initio protein structure prediction using chunk-TASSER. Biophys J 93(5):1510–1518

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Skolnick J (2006) In quest of an empirical potential for protein structure prediction. Curr Opin Struct Biol 16(2):166–171

    Article  CAS  PubMed  Google Scholar 

  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):76–86

    Article  PubMed  CAS  Google Scholar 

  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–W309

    Article  PubMed  PubMed Central  Google Scholar 

  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):672–688

    Article  CAS  PubMed  Google Scholar 

  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):482–494

    PubMed  Google Scholar 

  25. Pei J, Grishin NV (2007) PROMALS: towards accurate multiple sequence alignments of distantly related proteins. Bioinformatics 23(7):802–808

    Article  CAS  PubMed  Google Scholar 

  26. Mizianty MJ, Kurgan L (2011) Sequence-based prediction of protein crystallization, purification and production propensity. Bioinformatics 27(13):i24–i33

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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):3403–3405

    Article  CAS  PubMed  Google Scholar 

  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. https://doi.org/10.1093/bib/bbx1018

  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):609–628

    Article  CAS  PubMed  Google Scholar 

  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):e84

    PubMed  PubMed Central  Google Scholar 

  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):88–105

    Article  CAS  PubMed  Google Scholar 

  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):e121

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  33. Pulim V, Bienkowska J, Berger B (2008) LTHREADER: prediction of extracellular ligand-receptor interactions in cytokines using localized threading. Protein Sci 17(2):279–292

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Fischer JD, Mayer CE, Söding J (2008) Prediction of protein functional residues from sequence by probability density estimation. Bioinformatics 24(5):613–620

    Article  CAS  PubMed  Google Scholar 

  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):331–341

    Article  PubMed  CAS  Google Scholar 

  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):e7072

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  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):388

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  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 9:430

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  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):414

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  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):226

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  41. Chen K, Kurgan L (2007) PFRES: protein fold classification by using evolutionary information and predicted secondary structure. Bioinformatics 23(21):2843–2850

    Article  CAS  PubMed  Google Scholar 

  42. Kong L, Zhang L (2014) Novel structure-driven features for accurate prediction of protein structural class. Genomics 103(4):292–297

    Article  CAS  PubMed  Google Scholar 

  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):551–564

    Article  CAS  PubMed  Google Scholar 

  44. Xue B, Faraggi E, Zhou Y (2009) Predicting residue-residue contact maps by a two-layer, integrated neural-network method. Proteins 76(1):176–183

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Cheng J, Baldi P (2007) Improved residue contact prediction using support vector machines and a large feature set. BMC Bioinformatics 8(1):113

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  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–i496

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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):245

    Article  PubMed  PubMed Central  Google Scholar 

  48. Schlessinger A, Punta M, Yachdav G, Kajan L, Rost B (2009) Improved disorder prediction by combination of orthogonal approaches. PLoS One 4(2):e4433

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  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):e24428

    Article  PubMed  PubMed Central  Google Scholar 

  50. Mizianty MJ, Uversky V, Kurgan L (2014) Prediction of intrinsic disorder in proteins using MFDp2. Methods Mol Biol 1137:147–162

    Article  CAS  PubMed  Google Scholar 

  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–W196

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Meng F, Kurgan L (2016) DFLpred: high-throughput prediction of disordered flexible linker regions in protein sequences. Bioinformatics 32(12):i341–i350

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Yan J, Dunker AK, Uversky VN, Kurgan L (2016) Molecular recognition features (MoRFs) in three domains of life. Mol BioSyst 12(3):697–710

    Article  CAS  PubMed  Google Scholar 

  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):1850–1858

    Article  CAS  PubMed  Google Scholar 

  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):271–283

    Article  PubMed  CAS  Google Scholar 

  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):2114–2130

    CAS  PubMed  Google Scholar 

  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):772–783

    Article  CAS  PubMed  Google Scholar 

  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–W38

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Kurgan L, Miri Disfani F (2011) Structural protein descriptors in 1-dimension and their sequence-based predictions. Curr Protein Pept Sci 12(6):470–489

    Article  CAS  PubMed  Google Scholar 

  60. Jones DT (1999) Protein secondary structure prediction based on position-specific scoring matrices. J Mol Biol 292(2):195–202

    Article  CAS  PubMed  Google Scholar 

  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–W568

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Rost B (1996) PHD: predicting one-dimensional protein structure by profile-based neural networks. Methods Enzymol 266:525–539

    Article  CAS  PubMed  Google Scholar 

  63. Rost B, Yachdav G, Liu J (2004) The PredictProtein server. Nucleic Acids Res 32(Web Server):W321–W326

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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–i42

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  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):e1000333

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  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):14819–14824

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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):5053–5064

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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):765–769

    Article  CAS  PubMed  Google Scholar 

  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):1623–1627

    Article  CAS  PubMed  Google Scholar 

  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):480–493

    Article  CAS  PubMed  Google Scholar 

  71. Gerstein M, Hegyi H (1998) Comparing genomes in terms of protein structure: surveys of a finite parts list. FEMS Microbiol Rev 22(4):277–304

    Article  CAS  PubMed  Google Scholar 

  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):281

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  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):11157–11162

    Article  PubMed  PubMed Central  Google Scholar 

  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–W159

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Barton GJ (1995) Protein secondary structure prediction. Curr Opin Struct Biol 5(3):372–376

    Article  CAS  PubMed  Google Scholar 

  76. Heringa J (2000) Computational methods for protein secondary structure prediction using multiple sequence alignments. Curr Protein Pept Sci 1(3):273–301

    Article  CAS  PubMed  Google Scholar 

  77. Rost B (2001) Protein secondary structure prediction continues to rise. J Struct Biol 134(2–3):204–218

    Article  CAS  PubMed  Google Scholar 

  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):459–462

    Article  CAS  Google Scholar 

  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):36–51

    Article  CAS  PubMed  Google Scholar 

  80. Rost B (2009) Prediction of protein structure in 1D—secondary structure, membrane regions, and solvent accessibility. Structural bioinformatics, 2nd edn. Wiley, New York

    Google Scholar 

  81. Pirovano W, Heringa J (2010) Protein secondary structure prediction. Methods Mol Biol 609:327–348

    Article  CAS  PubMed  Google Scholar 

  82. Meng F, Kurgan L (2016) Computational prediction of protein secondary structure from sequence. Curr Protoc Protein Sci 86:2.3.1–2.3.10

    Article  Google Scholar 

  83. Singh M (2006) Predicting protein secondary and supersecondary structure, Chapman & Hall/CRC Computer & Information Science Series. Chapman and Hall/CRC, New York

    Google Scholar 

  84. Gruber M, Söding J, Lupas AN (2006) Comparative analysis of coiled-coil prediction methods. J Struct Biol 155(2):140–145

    Article  CAS  PubMed  Google Scholar 

  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):270–282

    Article  CAS  PubMed  Google Scholar 

  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 932:87–106

    Article  CAS  PubMed  Google Scholar 

  87. Chen K, Kurgan L (2013) Computational prediction of secondary and supersecondary structures. Methods Mol Biol 932:63–86

    Article  CAS  PubMed  Google Scholar 

  88. Kolodny R, Honig B (2006) VISTAL—a new 2D visualization tool of protein 3D structural alignments. Bioinformatics 22(17):2166–2167

    Article  CAS  PubMed  Google Scholar 

  89. Moreland JL, Gramada A, Buzko OV, Zhang Q, Bourne PE (2005) BMC Bioinformatics 6(1):21

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  90. Porollo AA, Adamczak R, Meller J (2004) POLYVIEW: a flexible visualization tool for structural and functional annotations of proteins. Bioinformatics 20(15):2460–2462

    Article  CAS  PubMed  Google Scholar 

  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):536–540

    CAS  PubMed  Google Scholar 

  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):1093–1109

    Article  CAS  PubMed  Google Scholar 

  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–D425

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  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–D426

    Article  CAS  PubMed  Google Scholar 

  95. Sillitoe I, Dawson N, Thornton J, Orengo C (2015) The history of the CATH structural classification of protein domains. Biochimie 119:209–217

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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–D425

    CAS  PubMed  Google Scholar 

  97. Levitt M, Greer J (1977) Automatic identification of secondary structure in globular proteins. J Mol Biol 114(2):181–239

    Article  CAS  PubMed  Google Scholar 

  98. Kabsch W, Sander C (1983) Dictionary of protein secondary structure: Pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 22(12):2577–2637

    Article  CAS  PubMed  Google Scholar 

  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):71–84

    Article  CAS  PubMed  Google Scholar 

  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):46–60

    Article  CAS  PubMed  Google Scholar 

  101. Frishman D, Argos P (1995) Knowledge-based protein secondary structure assignment. Proteins Struct Funct Genet 23(4):566–579

    Article  CAS  PubMed  Google Scholar 

  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):291–295

    Article  CAS  Google Scholar 

  103. King SM, Johnson WC (1999) Assigning secondary structure from protein coordinate data. Proteins Struct Funct Genet 35(3):313–320

    Article  CAS  PubMed  Google Scholar 

  104. Fodje MN, Al-Karadaghi S (2002) Occurrence, conformational features and amino acid propensities for the π-helix. Protein Eng Des Sel 15(5):353–358

    Article  CAS  Google Scholar 

  105. Martin J, Letellier G, Marin A, Taly J-F, de Brevern AG, Gibrat J-F (2005) BMC Struct Biol 5(1):17

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  106. Cubellis M, Cailliez F, Lovell SC (2005) Secondary structure assignment that accurately reflects physical and evolutionary characteristics. BMC Bioinformatics 6(Suppl 4):S8

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  107. Majumdar I, Krishna SS, Grishin NV (2005) PALSSE: a program to delineate linear secondary structural elements from protein structures. BMC Bioinformatics 6(1):202

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  108. Zhang W, Dunker AK, Zhou Y (2008) Assessing secondary structure assignment of protein structures by using pairwise sequence-alignment benchmarks. Proteins 71(1):61–67

    Article  CAS  PubMed  Google Scholar 

  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):406–411

    Article  CAS  PubMed  Google Scholar 

  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):118–122

    Article  CAS  PubMed  Google Scholar 

  111. Zacharias J, Knapp EW (2014) Protein secondary structure classification revisited: processing DSSP information with PSSC. J Chem Inf Model 54(7):2166–2179

    Article  CAS  PubMed  Google Scholar 

  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):1757–1761

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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):333

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  114. Klose DP, Wallace BA, Janes RW (2010) 2Struc: the secondary structure server. Bioinformatics 26(20):2624–2625

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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–iv

    Article  CAS  PubMed  Google Scholar 

  116. Koh IYY (2003) EVA: evaluation of protein structure prediction servers. Nucleic Acids Res 31(13):3311–3315

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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):258–269

    Article  CAS  PubMed  Google Scholar 

  118. Truebestein L, Leonard TA (2016) Coiled-coils: the long and short of it. BioEssays 38(9):903–916

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  119. Pellegrini-Calace M (2005) Detecting DNA-binding helix-turn-helix structural motifs using sequence and structure information. Nucleic Acids Res 33(7):2129–2140

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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):231–262

    Article  CAS  PubMed  Google Scholar 

  121. Hutchinson EG, Thornton JM (1996) PROMOTIF-A program to identify and analyze structural motifs in proteins. Protein Sci 5(2):212–220

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  122. Walshaw J, Woolfson DN (2001) SOCKET: a program for identifying and analysing coiled-coil motifs within protein structures. J Mol Biol 307(5):1427–1450

    Article  CAS  PubMed  Google Scholar 

  123. Testa OD, Moutevelis E, Woolfson DN (2009) CC+: a relational database of coiled-coil structures. Nucleic Acids Res 37(Database):D315–D322

    Article  CAS  PubMed  Google Scholar 

  124. Michalopoulos I (2004) TOPS: an enhanced database of protein structural topology. Nucleic Acids Res 32(90001):D251–D254

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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):7558–7562

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  126. Altschul S (1997) Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 25(17):3389–3402

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  127. Fang C, Shang Y, Xu D (2018) MUFOLD-SS: new deep inception-inside-inception networks for protein secondary structure prediction. Proteins 86(5):592–598

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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):2842–2849

    Article  CAS  PubMed  Google Scholar 

  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–W435

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  130. Wang S, Peng J, Ma J, Xu J (2016) Protein secondary structure prediction using deep convolutional neural fields. Sci Rep 6:18962

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  131. Cole C, Barber JD, Barton GJ (2008) The Jpred 3 secondary structure prediction server. Nucleic Acids Res 36(Web Server):W197–W201

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  132. Cuff JA, Clamp ME, Siddiqui AS, Finlay M, Barton GJ (1998) JPred: a consensus secondary structure prediction server. Bioinformatics 14(10):892–893

    Article  CAS  PubMed  Google Scholar 

  133. Cuff JA, Barton GJ (2000) Application of multiple sequence alignment profiles to improve protein secondary structure prediction. Proteins Struct Funct Genet 40(3):502–511

    Article  CAS  PubMed  Google Scholar 

  134. Drozdetskiy A, Cole C, Procter J, Barton GJ (2015) JPred4: a protein secondary structure prediction server. Nucleic Acids Res 43(W1):W389–W394

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  135. Yaseen A, Li Y (2014) Context-based features enhance protein secondary structure prediction accuracy. J Chem Inf Model 54(3):992–1002

    Article  CAS  PubMed  Google Scholar 

  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–W357

    Article  PubMed  PubMed Central  Google Scholar 

  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):201

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  138. Pollastri G, McLysaght A (2005) Porter: a new, accurate server for protein secondary structure prediction. Bioinformatics 21(8):1719–1720

    Article  CAS  PubMed  Google Scholar 

  139. Mirabello C, Pollastri G (2013) Porter, PaleAle 4.0: high-accuracy prediction of protein secondary structure and relative solvent accessibility. Bioinformatics 29(16):2056–2058

    Article  CAS  PubMed  Google Scholar 

  140. Bettella F, Rasinski D, Knapp EW (2012) Protein secondary structure prediction with SPARROW. J Chem Inf Model 52(2):545–556

    Article  CAS  PubMed  Google Scholar 

  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):470–477

    Article  CAS  PubMed  Google Scholar 

  142. Kountouris P, Hirst JD (2009) Prediction of backbone dihedral angles and protein secondary structure using support vector machines. BMC Bioinformatics 10(1):437

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  143. Green JR, Korenberg MJ, Aboul-Magd MO (2009) PCI-SS: MISO dynamic nonlinear protein secondary structure prediction. BMC Bioinformatics 10:222–222

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  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–W209

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  145. Montgomerie S, Sundararaj S, Gallin WJ, Wishart DS (2006) Improving the accuracy of protein secondary structure prediction using structural alignment. BMC Bioinformatics 7:301

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  146. Martin J, Gibrat JF, Rodolphe F (2006) Analysis of an optimal hidden Markov model for secondary structure prediction. BMC Struct Biol 6:25

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  147. Karypis G (2006) YASSPP: better kernels and coding schemes lead to improvements in protein secondary structure prediction. Proteins 64(3):575–586

    Article  CAS  PubMed  Google Scholar 

  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):152–159

    Article  CAS  PubMed  Google Scholar 

  149. Adamczak R, Porollo A, Meller J (2005) Combining prediction of secondary structure and solvent accessibility in proteins. Proteins 59(3):467–475

    Article  PubMed  CAS  Google Scholar 

  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–W76

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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):228–235

    Article  CAS  PubMed  Google Scholar 

  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):596–602

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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 1484:55–63

    Article  CAS  PubMed  Google Scholar 

  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 5:11476

    Article  PubMed  PubMed Central  Google Scholar 

  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):259–267

    Article  CAS  PubMed  Google Scholar 

  156. Schuster M, Paliwal KK (1997) Bidirectional recurrent neural networks. IEEE Trans Signal Process 45(11):2673–2681

    Article  Google Scholar 

  157. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780

    Article  CAS  PubMed  Google Scholar 

  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):173–175

    Article  PubMed  CAS  Google Scholar 

  159. Lupas A, Van Dyke M, Stock J (1991) Predicting coiled coils from protein sequences. Science 252(5009):1162–1164

    Article  CAS  PubMed  Google Scholar 

  160. Eddy SR (1998) Profile hidden Markov models. Bioinformatics 14(9):755–763

    Article  CAS  PubMed  Google Scholar 

  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):402

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  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):181–190

    Article  CAS  PubMed  Google Scholar 

  163. Cuff JA, Barton GJ (2000) Application of multiple sequence alignment profiles to improve protein secondary structure prediction. Proteins 40(3):502–511

    Article  CAS  PubMed  Google Scholar 

  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):1242–1243

    Article  CAS  PubMed  Google Scholar 

  165. Jia S-C, Hu X-Z (2011) Using random forest algorithm to predict β-hairpin motifs. Protein Pept Lett 18(6):609–617

    Article  CAS  PubMed  Google Scholar 

  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):1123–1128

    Article  CAS  PubMed  Google Scholar 

  167. Zou D, He Z, He J (2009) β-Hairpin prediction with quadratic discriminant analysis using diversity measure. J Comput Chem 30(14):2277–2284

    CAS  PubMed  Google Scholar 

  168. Hu XZ, Li QZ (2008) Prediction of the β-hairpins in proteins using support vector machine. Protein J 27(2):115–122

    Article  CAS  PubMed  Google Scholar 

  169. Kuhn M, Meiler J, Baker D (2004) Strand-loop-strand motifs: Prediction of hairpins and diverging turns in proteins. Proteins 54(2):282–288

    Article  CAS  PubMed  Google Scholar 

  170. Singh H, Raghava GPS (2016) BLAST-based structural annotation of protein residues using Protein Data Bank. Biol Direct 11:4

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  171. Bartoli L, Fariselli P, Krogh A, Casadio R (2009) CCHMM_PROF: a HMM-based coiled-coil predictor with evolutionary information. Bioinformatics 25(21):2757–2763

    Article  CAS  PubMed  Google Scholar 

  172. McDonnell AV, Jiang T, Keating AE, Berger B (2006) Paircoil2: improved prediction of coiled coils from sequence. Bioinformatics 22(3):356–358

    Article  CAS  PubMed  Google Scholar 

  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):8989–8994

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  174. Gruber M, Soding J, Lupas AN (2005) REPPER—repeats and their periodicities in fibrous proteins. Nucleic Acids Res 33(Web Server):W239–W243

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  175. Delorenzi M, Speed T (2002) An HMM model for coiled-coil domains and a comparison with PSSM-based predictions. Bioinformatics 18(4):617–625

    Article  CAS  PubMed  Google Scholar 

  176. Dodd IB, Egan JB (1990) Improved detection of helix-turn-helix DNA-binding motifs in protein sequences. Nucleic Acids Res 18(17):5019–5026

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  177. Narasimhan G, Bu C, Gao Y, Wang X, Xu N, Mathee K (2002) Mining protein sequences for motifs. J Comput Biol 9(5):707–720

    Article  CAS  PubMed  Google Scholar 

  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):5632–5640

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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):e23519

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  180. Wolf E, Kim PS, Berger B (1997) MultiCoil: a program for predicting two-and three-stranded coiled coils. Protein Sci 6(6):1179–1189

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  181. Ahmad S, Gromiha MM (2002) NETASA: neural network based prediction of solvent accessibility. Bioinformatics 18(6):819–824

    Article  CAS  PubMed  Google Scholar 

  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):8259–8263

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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 3:209–217

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgments

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

Author information

Authors and Affiliations

  1. Department of Computer Science, College of Engineering, Virginia Commonwealth University, Richmond, VA, USA

    Christopher J. Oldfield & Lukasz Kurgan

  2. School of Computer Science and Software Engineering, Tianjin Polytechnic University, Tianjin, People’s Republic of China

    Ke Chen

Authors
  1. Christopher J. Oldfield
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ke Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lukasz Kurgan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lukasz Kurgan .

Editor information

Editors and Affiliations

  1. Department of Mathematics, Rutgers University, Piscataway, NJ, USA

    Alexander E. Kister

Rights and permissions

Reprints and permissions

Copyright information

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

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Oldfield, C.J., Chen, K., Kurgan, L. (2019). Computational Prediction of Secondary and Supersecondary Structures from Protein Sequences. In: Kister, A. (eds) Protein Supersecondary Structures. Methods in Molecular Biology, vol 1958. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-9161-7_4

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-9161-7_4

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-9160-0

  • Online ISBN: 978-1-4939-9161-7

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation