Supersecondary Structures and Fragment Libraries

  • Raphael TrevizaniEmail author
  • Fábio Lima Custódio
Part of the Methods in Molecular Biology book series (MIMB, volume 1958)


The use of smotifs and fragment libraries has proven useful to both simplify and increase the quality of protein models. Here, we present Profrager, a tool that automatically generates putative structural fragments to reproduce local motifs of proteins given a target sequence. Profrager is highly customizable, allowing the user to select the number of fragments per library, the ranking method is able to generate fragments of all sizes, and it was recently modified to include the possibility of output exclusively smotifs.

Key words

Smotifs Supersecondary structures Fragment library Protein motifs Protein structure prediction 


  1. 1.
    Kendrew JC, Bodo G, Dintzis HM, Parrish RG, Wyckoff H, Phillips DC (1958) A three-dimensional model of the myoglobin molecule obtained by X-ray analysis. Nature 181(4610):662–666CrossRefGoogle Scholar
  2. 2.
    Unger R, Harel D, Wherland S, Sussman JL (1989) A 3d building blocks approach to analyzing and predicting structure of proteins. Proteins 5(4):355–373CrossRefGoogle Scholar
  3. 3.
    Kolodny R, Koehl P, Guibas L, Levitt M (2002) Small libraries of protein fragments model native protein structures accurately. J Mol Biol 323(2):297–307CrossRefGoogle Scholar
  4. 4.
    Nepomnyachiy S, Ben-Tal N, Kolodny R (2017) Complex evolutionary footprints revealed in an analysis of reused protein segments of diverse lengths. Proc Natl Acad U S A 114(44):11703–11708CrossRefGoogle Scholar
  5. 5.
    Xie ZR, Chen J, Zhao Y, Wu Y (2015) Decomposing the space of protein quaternary structures with the interface fragment pair library. BMC Bioinformatics 16:14CrossRefGoogle Scholar
  6. 6.
    Lee J, Freddolino PL, Zhang Y (2017) Ab initio protein structure prediction. In: Rigden DJ (ed) From protein structure to function with bioinformatics. Springer, Dordrecht, pp 3–35CrossRefGoogle Scholar
  7. 7.
    Cuff AL, Sillitoe I, Lewis T, Clegg AB, Rentzsch R, Furnham N, PellegriniCalace 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:D420–D426CrossRefGoogle Scholar
  8. 8.
    Grant A, Lee D, Orengo C (2004) Progress towards mapping the universe of protein folds. Genome Biol 5:107CrossRefGoogle Scholar
  9. 9.
    Andreeva A, Howorth D, Chandonia JM, Brenner SE, Hubbard TJP, Chothia C, Murzin AG (2008) Data growth and its impact on the scop database: new developments. Nucleic Acids Res 36:D419–D425CrossRefGoogle Scholar
  10. 10.
    Khafizov K, Madrid-Aliste C, Almo SC, Fiser A (2014) Trends in structural coverage of the protein universe and the impact of the protein structure initiative. Proc Natl Acad Sci U S A 111:3733–3738CrossRefGoogle Scholar
  11. 11.
    Chothia C, Lesk AM (1986) The relation between the divergence of sequence and structure in proteins. EMBO J 5:823–826CrossRefGoogle Scholar
  12. 12.
    Illergård K, Ardell DH, Elofsson A (2009) Structure is three to ten times more conserved than sequence—a study of structural response in protein cores. Proteins 77:499–508CrossRefGoogle Scholar
  13. 13.
    Pieper U, Eswar N, Braberg H, Madhusudhan MS, Davis FP, Stuart AC, Mirkovic N, Rossi A, Marti-Renom MA, Fiser A, Webb B, Greenblatt D, Huang CC, Ferrin TE, Sali A (2004) Modbase, a database of annotated comparative protein structure models, and associated resources. Nucleic Acids Res 32:D217–D222CrossRefGoogle Scholar
  14. 14.
    Bienert S, Waterhouse A, de Beer TAP, Tauriello G, Studer G, Bordoli L, Schwede T (2017) The swiss-model repository-new features and functionality. Nucleic Acids Res 45:D313–D319CrossRefGoogle Scholar
  15. 15.
    Bowie JU, Lüthy R, Eisenberg D (1991) A method to identify protein sequences that fold into a known three-dimensional structure. Science 253(5016):164–170CrossRefGoogle Scholar
  16. 16.
    Buchan DWA, Jones DT (2017) Eigenthreader: analogous protein fold recognition by efficient contact map threading. Bioinformatics (Oxford, England) 33:2684–2690CrossRefGoogle Scholar
  17. 17.
    Maldonado-Nava FG, Frausto-Solís J, Sánchez-Hernández JP, González Barbosa JJ, Liñán-García E (2018) Comparative study of computational strategies for protein structure prediction. In: Castillo O, Melin P, Kacprzyk J (eds) Fuzzy logic augmentation of neural and optimization algorithms: theoretical aspects and real applications, Studies in computational intelligence, vol 749. Springer, ChamGoogle Scholar
  18. 18.
    Cavasotto CN, Phatak SS (2009) Homology modeling in drug discovery: current trends and applications. Drug Discov Today 14:676–683CrossRefGoogle Scholar
  19. 19.
    Schmidt T, Bergner A, Schwede T (2014) Modelling three-dimensional protein structures for applications in drug design. Drug Discov Today 19:890–897CrossRefGoogle Scholar
  20. 20.
    França TCC (2015) Homology modeling: an important tool for the drug discovery. J Biomol Struct Dyn 33:1780–1793CrossRefGoogle Scholar
  21. 21.
    Moult J, Fidelis K, Kryshtafovych A, Schwede T, Tramontano A (2018) Critical assessment of methods of protein structure prediction (CASP)-round XII. Proteins 86:7–15CrossRefGoogle Scholar
  22. 22.
    Shaw DE, Grossman J, Bank JA, Batson B, Butts JA, Chao JC, Deneroff MM, Dror RO, Even A, Fenton CH et al (2014) Anton 2: raising the bar for performance and programmability in a special-purpose molecular dynamics supercomputer. In: Proceedings of the international conference for high performance computing, networking, storage and analysis. IEEE Press, Piscataway, NJ, pp 41–53CrossRefGoogle Scholar
  23. 23.
    Bradley P, Misura KM, Baker D (2005) Toward high-resolution de novo structure prediction for small proteins. Science 309(5742):1868–1871CrossRefGoogle Scholar
  24. 24.
    Moult J, Fidelis K, Kryshtafovych A, Schwede T, Tramontano A (2016) Critical assessment of methods of protein structure prediction: progress and new directions in round XI. Proteins 84:4–14CrossRefGoogle Scholar
  25. 25.
    Piana S, Klepeis JL, Shaw DE (2014) Assessing the accuracy of physical models used in protein-folding simulations: quantitative evidence from long molecular dynamics simulations. Curr Opin Struct Biol 24:98–105CrossRefGoogle Scholar
  26. 26.
    Pauling L, Corey RB (1951) The pleated sheet, a new layer configuration of polypeptide chains. Proc Natl Acad Sci U S A 37(5):251–256CrossRefGoogle Scholar
  27. 27.
    Pauling L, Corey RB, Branson HR (1951) The structure of proteins; two hydrogen-bonded helical configurations of the polypeptide chain. Proc Natl Acad Sci U S A 37(4):205–211CrossRefGoogle Scholar
  28. 28.
    Venkatachalam CM (1968) Stereochemical criteria for polypeptides and proteins. v. conformation of a system of three linked peptide units. Biopolymers 6(10):1425–1436CrossRefGoogle Scholar
  29. 29.
    Richardson JS (1981) The anatomy and taxonomy of protein structure. Adv Protein Chem 34:167–339CrossRefGoogle Scholar
  30. 30.
    Jones TA, Thirup S (1986) Using known substructures in protein model building and crystallography. EMBO J 5(4):819–822CrossRefGoogle Scholar
  31. 31.
    Han KF, Baker D (1995) Recurring local sequence motifs in proteins. J Mol Biol 251(1):176–187CrossRefGoogle Scholar
  32. 32.
    Wu S, Skolnick J, Zhang Y (2007) Ab initio modeling of small proteins by iterative tasser simulations. BMC Biol 5:17CrossRefGoogle Scholar
  33. 33.
    Roy A, Kucukural A, Zhang Y (2010) I-tasser: a unified platform for automated protein structure and function prediction. Nat Protoc 5(4):725–738CrossRefGoogle Scholar
  34. 34.
    Zhang Y (2008) I-tasser server for protein 3d structure prediction. BMC Bioinformatics 9:40CrossRefGoogle Scholar
  35. 35.
    Rohl CA, Strauss CEM, Misura KMS, Baker D (2004) Protein structure prediction using rosetta. Methods Enzymol 383:66–93CrossRefGoogle Scholar
  36. 36.
    Xu D, Zhang Y (2012) Ab initio protein structure assembly using continuous structure fragments and optimized knowledge-based force field. Proteins 80(7):1715–1735PubMedPubMedCentralGoogle Scholar
  37. 37.
    Levitt M (1992) Accurate modeling of protein conformation by automatic segment matching. J Mol Biol 226(2):507–533CrossRefGoogle Scholar
  38. 38.
    Brunette TJ, Parmeggiani F, Huang PS, Bhabha G, Ekiert DC, Tsutakawa SE, Hura GL, Tainer JA, Baker D (2015) Exploring the repeat protein universe through computational protein design. Nature 528:580–584CrossRefGoogle Scholar
  39. 39.
    Li W, Kinch LN, Karplus PA, Grishin NV (2015) Chseq: a database of chameleon sequences. Protein Sci 24:1075–1086CrossRefGoogle Scholar
  40. 40.
    Bonneau R, Baker D (2001) Ab initio protein structure prediction: progress and prospects. Annu Rev Biophys Biomol Struct 30:173–189CrossRefGoogle Scholar
  41. 41.
    Verschueren E, Vanhee P, van der Sloot AM, Serrano L, Rousseau F, Schymkowitz J (2011) Protein design with fragment databases. Curr Opin Struct Biol 21(4):452–459CrossRefGoogle Scholar
  42. 42.
    Pilla KB, Otting G, Huber T (2017) Protein structure determination by assembling super-secondary structure motifs using pseudocontact shifts. Structure (London, England) 1993(25):559–568CrossRefGoogle Scholar
  43. 43.
    Vallat B, Madrid-Aliste C, Fiser A (2015) Modularity of protein folds as a tool for template-free modeling of structures. PLoS Comput Biol 11:e1004419CrossRefGoogle Scholar
  44. 44.
    Fernandez-Fuentes N, Dybas JM, Fiser A (2010) Structural characteristics of novel protein folds. PLoS Comput Biol 6:e1000750CrossRefGoogle Scholar
  45. 45.
    Fernandez-Fuentes N, Fiser A (2006) Saturating representation of loop conformational fragments in structure databanks. BMC Struct Biol 6:15CrossRefGoogle Scholar
  46. 46.
    Koga N, Tatsumi-Koga R, Liu G, Xiao R, Acton TB, Montelione GT, Baker D (2012) Principles for designing ideal protein structures. Nature 491:222–227CrossRefGoogle Scholar
  47. 47.
    Handl J, Knowles J, Vernon R, Baker D, Lovell SC (2012) The dual role of fragments in fragment-assembly methods for de novo protein structure prediction. Proteins 80(2):490–504CrossRefGoogle Scholar
  48. 48.
    Baeten L, Reumers J, Tur V, Stricher F, Lenaerts T, Serrano L, Rousseau F, Schymkowitz J (2008) Reconstruction of protein backbones from the brix collection of canonical protein fragments. PLoS Comput Biol 4(5):e1000083CrossRefGoogle Scholar
  49. 49.
    Vanhee P, Verschueren E, Baeten L, Stricher F, Serrano L, Rousseau F, Schymkowitz J (2011) Brix: a database of protein building blocks for structural analysis, modeling and design. Nucleic Acids Res 39(Database issue):D435–D442CrossRefGoogle Scholar
  50. 50.
    Santos KB, Trevizani R, Custodio FL, Dardenne LE (2015) Profrager web server: fragment libraries generation for protein structure prediction. In: Proceedings of the international conference on Bioinformatics & Computational Biology (BIOCOMP). The Steering Committee of the World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp), p 38Google Scholar
  51. 51.
    Wang G, Dunbrack RL (2003) Pisces: a protein sequence culling server. Bioinformatics 19(12):1589–1591CrossRefGoogle Scholar
  52. 52.
    McGuffin LJ, Bryson K, Jones DT (2000) The psipred protein structure prediction server. Bioinformatics 16(4):404–405CrossRefGoogle Scholar
  53. 53.
    Frishman D, Argos P (1995) Knowledge-based protein secondary structure assignment. Proteins 23(4):566–579CrossRefGoogle Scholar
  54. 54.
    Charnes A, Cooper WW, Golany B, Seiford L, Stutz J (1985) Foundations of data envelopment analysis for pareto-koopmans efficient empirical production functions. J Econ 30(1–2):91–107CrossRefGoogle Scholar
  55. 55.
    Altschul SF, Madden TL, Schäffer 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):3389–3402CrossRefGoogle Scholar
  56. 56.
    Holmes JB, Tsai J (2004) Some fundamental aspects of building protein structures from fragment libraries. Protein Sci 13(6):1636–1650CrossRefGoogle Scholar
  57. 57.
    Trevizani R, Custódio FL, dos Santos KB, Dardenne LE (2017) Critical features of fragment libraries for protein structure prediction. PLoS One 12(1):e0170131CrossRefGoogle Scholar
  58. 58.
    Kalev I, Habeck M (2011) Hhfrag: Hmm-based fragment detection using hhpred. Bioinformatics 27(22):3110–3116CrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  1. 1.Fiocruz—Fundação Oswaldo CruzEusébioBrazil
  2. 2.LNCC—Laboratório Nacional de Computação CientíficaPetrópolisBrazil

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