Advertisement

Sequence Pattern for Supersecondary Structure of Sandwich-Like Proteins

  • Alexander E. KisterEmail author
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1958)

Abstract

The goal is to define sequence characteristics of beta-sandwich proteins that are unique for the beta-sandwich supersecondary structure (SSS). Finding of the conserved residues that are critical for protein structure can often be accomplished with homology methods, but these methods are not always adequate as residues with similar structural role do not always occupy the same position as determined by sequence alignment. In this paper, we show how to identify residues that play the same structural role in the different proteins of the same SSS, even when these residue positions cannot be aligned with sequence alignment methods. The SSS characteristics are (a) a set of positions in each strand that are involved in the formation of a hydrophobic core, residue content, and correlations of residues at these key positions, (b) maximum allowable number of “low-frequency residues” for each strand, (c) minimum allowed number of “high-frequency” residues for each loop, and (d) minimum and maximum lengths of each loop. These sequence characteristics are referred to as “sequence pattern” for their respective SSS. The high specificity and sensitivity for a particular SSS are confirmed by applying this pattern to all protein structures in the SCOP data bank. We present here the pattern for one of the most common SSS of beta-sandwich proteins.

Key words

Supersecondary structure Secondary structure Supersecondary structure prediction Sequence analysis Immunoglobulin fold Sequence-structure relationship Sequence alignment Structure comparison 

References

  1. 1.
    Lazaridis T, Karplus M (2000) Effective energy functions for protein structure prediction. Curr Opin Struct Biol 10:139–145CrossRefGoogle Scholar
  2. 2.
    Huang PS, Boyken SE, Baker D (2016) The coming of age of de novo protein design. Nature 537(7620):320–327CrossRefGoogle Scholar
  3. 3.
    Adhikari B, Cheng J (2018) CONFOLD2: improved contact-driven ab initio protein structure modeling. BMC Bioinformatics 19(1):22CrossRefGoogle Scholar
  4. 4.
    Pace CN, Shirley BA, McNutt M, Gajiwala K (1996) Forces contributing to the conformational stability of proteins. FASEB J 10:75–83CrossRefGoogle Scholar
  5. 5.
    Jothi A (2012) Principles, challenges and advances in ab initio protein structure prediction. Protein Pept Lett 9:1194–1204CrossRefGoogle Scholar
  6. 6.
    Fogolari F, Corazza A, Esposito G (2018) Free energy, enthalpy and entropy from implicit solvent end-point simulations. Front Mol Biosci 5:11CrossRefGoogle Scholar
  7. 7.
    Nguyen H, Maier J, Huang H, Perrone V, Simmerling C (2014) Folding simulations for proteins with diverse topologies are accessible in days with a physics-based force field and implicit solvent. J Am Chem Soc 136(40):13959–13962CrossRefGoogle Scholar
  8. 8.
    Chatzou M, Magis C, Chang JM et al (2016) Multiple sequence alignment modeling: methods and applications. Brief Bioinform 17(6):1009–1023CrossRefGoogle Scholar
  9. 9.
    Le Q, Sievers F, Higgins DG (2017) Protein multiple sequence alignment bench-marking through secondary structure prediction. Bioinformatics 33(9):1331–1337PubMedPubMedCentralGoogle Scholar
  10. 10.
    Xiang Z (2006) Advances in homology protein structure modeling. Curr Protein Pept Sci 7(3):217–227CrossRefGoogle Scholar
  11. 11.
    Eaton KV, Anderson WJ, Dubrava MS et al (2015) Studying protein fold evolution with hybrids of differently folded homologs. Protein Eng Des Sel 28(8):241–250CrossRefGoogle Scholar
  12. 12.
    He Y, Chen Y, Alexander P et al (2008) NMR structures of two designed proteins with high sequence identity but different fold and function. Proc Natl Acad Sci U S A 105(38):14412–11417CrossRefGoogle Scholar
  13. 13.
    Schubert HL, Raux E, Wilson KS et al (1999) Common chelatase design in the branched tetrapyrrole pathways of heme and anaerobic cobalamin synthesis. Biochemistry 38(33):10660–10669CrossRefGoogle Scholar
  14. 14.
    Ma J, Wang S (2014) Algorithms, applications, and challenges of protein structure alignment. Adv Protein Chem Struct Biol 94:121–175CrossRefGoogle Scholar
  15. 15.
    Sadowski MI, Taylor WR (2012) Evolutionary inaccuracy of pairwise structural alignments. Bioinformatics 28(9):1209–1215CrossRefGoogle Scholar
  16. 16.
    Kolodny R, Pereyaslavets L, Samson AO et al (2013) On the universe of protein folds. Annu Rev Biophys 42:559–582CrossRefGoogle Scholar
  17. 17.
    Bernal J (1939) Structure of proteins. Nature 143:663–667CrossRefGoogle Scholar
  18. 18.
    Bresler SE, Talmud DL (1944) On the nature of globular proteins. II. Some consequences of a new hypothesis. Dokl Akad Nauk SSSR (in Russian) 43:326–330Google Scholar
  19. 19.
    Kauzmann W (1959) Some factors in the interpretation of protein denaturation. Adv Protein Chem 14:1–63CrossRefGoogle Scholar
  20. 20.
    Nick Pace C, Scholtz JM, Grimsley GR (2014) Forces stabilizing proteins. FEBS Lett 588(14):2177–2184CrossRefGoogle Scholar
  21. 21.
    Berman HM, Westbrook J, Feng Z et al (2000) The protein data bank. Nucleic Acids Res 28(1):235–242CrossRefGoogle Scholar
  22. 22.
    Murzin AG, Brenner SE, Hubbard T et al (1995) SCOP: a structural classification of proteins database for the investigation of sequences and structures. J Mol Biol 247:536–540PubMedGoogle Scholar
  23. 23.
    Fox NK, Brenner SE, Chandonia JM (2014) SCOPe: Structural Classification of Proteins—extended, integrating SCOP and ASTRAL data and classification of new structures. Nucleic Acids Res 42:D304–D309CrossRefGoogle Scholar
  24. 24.
    de Beer TAP, Berka K, Thornton JM et al (2014) PDBsum additions. Nucleic Acids Res 42:D292–D296CrossRefGoogle Scholar
  25. 25.
    Sobolev V, Eyal E, Gerzon S et al (2005) SPACE: a suite of tools for protein structure prediction and analysis based on complementarity and environment. Nucleic Acids Res 33:W39–W43CrossRefGoogle Scholar
  26. 26.
    Costantini S, Colonna G, Facchiano AM (2006) Amino acid propensities for secondary structures are influenced by the protein structural class. Biochem Biophys Res Commun 342(2):441–451CrossRefGoogle Scholar
  27. 27.
    Chou PY, Fasman GD (1974) Conformational parameters for amino acids in helical, beta sheet, and random coil regions calculated from proteins. Biochemistry 13(2):211–222CrossRefGoogle Scholar
  28. 28.
    Fujiwara K, Toda H, Ikeguchi M (2012) Dependence of α-helical and β-sheet amino acid propensities on the overall protein fold type. BMC Struct Biol 12:18CrossRefGoogle Scholar
  29. 29.
    Nilsson I, Johnson AE, von Heijne G (2003) How hydrophobic is alanine? J Biol Chem 278(32):29389–29393CrossRefGoogle Scholar
  30. 30.
    Nagano N, Ota M, Nishikawa K (1999) Strong hydrophobic nature of cysteine residues in proteins. FEBS Lett 458(1):69–71CrossRefGoogle Scholar
  31. 31.
    Kister AE, Finkelstein AV, Gelfand IM (2002) Common features in structures and sequences of sandwich-like proteins. Proc Natl Acad Sci U S A 99:14137–14141CrossRefGoogle Scholar
  32. 32.
    Kister AE (2015) Amino acid distribution rules predict protein fold: protein grammar for beta-strand sandwich-like structures. Biomolecules 5:41–59Google Scholar

Copyright information

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

Authors and Affiliations

  1. 1.Department of MathematicsRutgers UniversityPiscatawayUSA

Personalised recommendations