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Supersecondary Structures and Fragment Libraries

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Protein Supersecondary Structures

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

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Abstract

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.

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References

  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):662–666

    Article  CAS  Google Scholar 

  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):355–373

    Article  CAS  Google Scholar 

  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):297–307

    Article  CAS  Google Scholar 

  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):11703–11708

    Article  CAS  Google Scholar 

  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 16:14

    Article  Google Scholar 

  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 3–35

    Chapter  Google Scholar 

  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–D426

    Article  CAS  Google Scholar 

  8. Grant A, Lee D, Orengo C (2004) Progress towards mapping the universe of protein folds. Genome Biol 5:107

    Article  Google Scholar 

  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–D425

    Article  CAS  Google Scholar 

  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 111:3733–3738

    Article  CAS  Google Scholar 

  11. Chothia C, Lesk AM (1986) The relation between the divergence of sequence and structure in proteins. EMBO J 5:823–826

    Article  CAS  Google Scholar 

  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 77:499–508

    Article  Google Scholar 

  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–D222

    Article  CAS  Google Scholar 

  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–D319

    Article  CAS  Google Scholar 

  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):164–170

    Article  CAS  Google Scholar 

  16. Buchan DWA, Jones DT (2017) Eigenthreader: analogous protein fold recognition by efficient contact map threading. Bioinformatics (Oxford, England) 33:2684–2690

    Article  CAS  Google Scholar 

  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, Cham

    Google Scholar 

  18. Cavasotto CN, Phatak SS (2009) Homology modeling in drug discovery: current trends and applications. Drug Discov Today 14:676–683

    Article  CAS  Google Scholar 

  19. Schmidt T, Bergner A, Schwede T (2014) Modelling three-dimensional protein structures for applications in drug design. Drug Discov Today 19:890–897

    Article  CAS  Google Scholar 

  20. França TCC (2015) Homology modeling: an important tool for the drug discovery. J Biomol Struct Dyn 33:1780–1793

    Article  Google Scholar 

  21. Moult J, Fidelis K, Kryshtafovych A, Schwede T, Tramontano A (2018) Critical assessment of methods of protein structure prediction (CASP)-round XII. Proteins 86:7–15

    Article  CAS  Google Scholar 

  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 41–53

    Chapter  Google Scholar 

  23. Bradley P, Misura KM, Baker D (2005) Toward high-resolution de novo structure prediction for small proteins. Science 309(5742):1868–1871

    Article  CAS  Google Scholar 

  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 84:4–14

    Article  Google Scholar 

  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 24:98–105

    Article  CAS  Google Scholar 

  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):251–256

    Article  CAS  Google Scholar 

  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):205–211

    Article  CAS  Google Scholar 

  28. Venkatachalam CM (1968) Stereochemical criteria for polypeptides and proteins. v. conformation of a system of three linked peptide units. Biopolymers 6(10):1425–1436

    Article  CAS  Google Scholar 

  29. Richardson JS (1981) The anatomy and taxonomy of protein structure. Adv Protein Chem 34:167–339

    Article  CAS  Google Scholar 

  30. Jones TA, Thirup S (1986) Using known substructures in protein model building and crystallography. EMBO J 5(4):819–822

    Article  CAS  Google Scholar 

  31. Han KF, Baker D (1995) Recurring local sequence motifs in proteins. J Mol Biol 251(1):176–187

    Article  CAS  Google Scholar 

  32. Wu S, Skolnick J, Zhang Y (2007) Ab initio modeling of small proteins by iterative tasser simulations. BMC Biol 5:17

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  34. Zhang Y (2008) I-tasser server for protein 3d structure prediction. BMC Bioinformatics 9:40

    Article  Google Scholar 

  35. Rohl CA, Strauss CEM, Misura KMS, Baker D (2004) Protein structure prediction using rosetta. Methods Enzymol 383:66–93

    Article  CAS  Google Scholar 

  36. Xu D, Zhang Y (2012) Ab initio protein structure assembly using continuous structure fragments and optimized knowledge-based force field. Proteins 80(7):1715–1735

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Levitt M (1992) Accurate modeling of protein conformation by automatic segment matching. J Mol Biol 226(2):507–533

    Article  CAS  Google Scholar 

  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 528:580–584

    Article  CAS  Google Scholar 

  39. Li W, Kinch LN, Karplus PA, Grishin NV (2015) Chseq: a database of chameleon sequences. Protein Sci 24:1075–1086

    Article  CAS  Google Scholar 

  40. Bonneau R, Baker D (2001) Ab initio protein structure prediction: progress and prospects. Annu Rev Biophys Biomol Struct 30:173–189

    Article  CAS  Google Scholar 

  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):452–459

    Article  CAS  Google Scholar 

  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):559–568

    Article  Google Scholar 

  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:e1004419

    Article  Google Scholar 

  44. Fernandez-Fuentes N, Dybas JM, Fiser A (2010) Structural characteristics of novel protein folds. PLoS Comput Biol 6:e1000750

    Article  Google Scholar 

  45. Fernandez-Fuentes N, Fiser A (2006) Saturating representation of loop conformational fragments in structure databanks. BMC Struct Biol 6:15

    Article  Google Scholar 

  46. Koga N, Tatsumi-Koga R, Liu G, Xiao R, Acton TB, Montelione GT, Baker D (2012) Principles for designing ideal protein structures. Nature 491:222–227

    Article  CAS  Google Scholar 

  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):490–504

    Article  CAS  Google Scholar 

  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):e1000083

    Article  Google Scholar 

  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–D442

    Article  CAS  Google Scholar 

  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 38

    Google Scholar 

  51. Wang G, Dunbrack RL (2003) Pisces: a protein sequence culling server. Bioinformatics 19(12):1589–1591

    Article  CAS  Google Scholar 

  52. McGuffin LJ, Bryson K, Jones DT (2000) The psipred protein structure prediction server. Bioinformatics 16(4):404–405

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  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):91–107

    Article  Google Scholar 

  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):3389–3402

    Article  CAS  Google Scholar 

  56. Holmes JB, Tsai J (2004) Some fundamental aspects of building protein structures from fragment libraries. Protein Sci 13(6):1636–1650

    Article  CAS  Google Scholar 

  57. Trevizani R, Custódio FL, dos Santos KB, Dardenne LE (2017) Critical features of fragment libraries for protein structure prediction. PLoS One 12(1):e0170131

    Article  Google Scholar 

  58. Kalev I, Habeck M (2011) Hhfrag: Hmm-based fragment detection using hhpred. Bioinformatics 27(22):3110–3116

    Article  CAS  Google Scholar 

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Author information

Authors and Affiliations

  1. Fiocruz—Fundação Oswaldo Cruz, Eusébio, Brazil

    Raphael Trevizani

  2. LNCC—Laboratório Nacional de Computação Científica, Petrópolis, Brazil

    Fábio Lima Custódio

Authors
  1. Raphael Trevizani
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  2. Fábio Lima Custódio
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Corresponding author

Correspondence to Raphael Trevizani .

Editor information

Editors and Affiliations

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

    Alexander E. Kister

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Trevizani, R., Custódio, F.L. (2019). Supersecondary Structures and Fragment Libraries. 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_14

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  • DOI: https://doi.org/10.1007/978-1-4939-9161-7_14

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-9160-0

  • Online ISBN: 978-1-4939-9161-7

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