Advertisement

The Protein Journal

, Volume 37, Issue 5, pp 407–427 | Cite as

Nonfunctional Missense Mutants in Two Well Characterized Cytosolic Enzymes Reveal Important Information About Protein Structure and Function

  • Ashley E. Cole
  • Fatmah M. Hani
  • Brian W. Allen
  • Paul C. Kline
  • Elliot Altman
Article
  • 55 Downloads

Abstract

The isolation and characterization of 42 unique nonfunctional missense mutants in the bacterial cytosolic β-galactosidase and catechol 2,3-dioxygenase enzymes allowed us to examine some of the basic general trends regarding protein structure and function. A total of 6 out of the 42, or 14.29% of the missense mutants were in α-helices, 17 out of the 42, or 40.48%, of the missense mutants were in β-sheets and 19 out of the 42, or 45.24% of the missense mutants were in unstructured coil, turn or loop regions. While α-helices and β-sheets are undeniably important in protein structure, our results clearly indicate that the unstructured regions are just as important. A total of 21 out of the 42, or 50.00% of the missense mutants caused either amino acids located on the surface of the protein to shift from hydrophilic to hydrophobic or buried amino acids to shift from hydrophobic to hydrophilic and resulted in drastic changes in hydropathy that would not be preferable. There was generally good consensus amongst the widely used algorithms, Chou–Fasman, GOR, Qian–Sejnowski, JPred, PSIPRED, Porter and SPIDER, in their ability to predict the presence of the secondary structures that were affected by the missense mutants and most of the algorithms predicted that the majority of the 42 inactive missense mutants would impact the α-helical and β-sheet secondary structures or the unstructured coil, turn or loop regions that they altered.

Keywords

Protein secondary structure α-Helices β-Sheets Unstructured regions Coils Hydropathy 

Notes

Compliance with Ethical Standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. 1.
    Astbury WT, Street A (1931) X-ray studies of the structures of hair, wool and related fibres. I. General. Phil Trans Roy Soc Lond A 230:75–101CrossRefGoogle Scholar
  2. 2.
    Astbury WT, Woods HJ (1933) X-ray studies of the structures of hair, wool and related fibres. II. The molecular structure and elastic properties of hair keratin. Phil Trans Roy Soc Lond A 232:333–394CrossRefGoogle Scholar
  3. 3.
    Astbury WT, Sisson WA (1935) X-ray studies of the structures of hair, wool and related fibres. III. The configuration of the keratin molecule and its orientation in the biological cell. Proc Roy Soc Lond A 150:533–551CrossRefGoogle Scholar
  4. 4.
    Pauling L, Corey RB (1951) Configurations of polypeptide chains with favored orientations around single bonds: two new pleated sheets. Proc Natl Acad Sci USA 37:729–740CrossRefPubMedGoogle Scholar
  5. 5.
    Pauling L, Corey RB, Branson HR (1951) The structure of proteins; two hydrogen-bonded helical configurations of the polypeptide chain. Proc Natl Acad Sci USA 37:205–211CrossRefPubMedGoogle Scholar
  6. 6.
    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 181:662–666CrossRefPubMedGoogle Scholar
  7. 7.
    Perutz MF, Rossmann MG, Cullis AF, Muirhead H, Will G, North AC (1960) Structure of haemoglobin: a three-dimensional Fourier synthesis at 5.5-A resolution, obtained by X-ray analysis. Nature 185:416–422CrossRefPubMedGoogle Scholar
  8. 8.
    Blake CC, Koenig DF, Mair GA, North AC, Phillips DC, Sarma VR (1965) Structure of hen egg white lysozyme. A three-dimensional Fourier synthesis at 2 Angstrom resolution. Nature 206:757–761CrossRefPubMedGoogle Scholar
  9. 9.
    Anfinsen CB (1973) Principles that govern the folding of protein chains. Science 181:223–230CrossRefPubMedGoogle Scholar
  10. 10.
    Tanford C (1962) Contribution of hydrophobic interactions to the stability of the globular conformation of proteins. J Am Chem Soc 84:4240–4247CrossRefGoogle Scholar
  11. 11.
    Zimmerman JM, Eliezer N, Simha R (1968) The characterization of amino acid sequences in proteins by statistical methods. J Theor Biol 21:170–201CrossRefPubMedGoogle Scholar
  12. 12.
    Kyte J, Doolittle RF (1982) A simple method for displaying the hydrophathic character of a protein. J Mol Biol 157:105–132CrossRefPubMedGoogle Scholar
  13. 13.
    Hopp TP, Woods KR (1983) A computer program for predicting protein antigenic determinants. Mol Immunol 20:483–489CrossRefPubMedGoogle Scholar
  14. 14.
    Eisenberg D, Schwarz E, Komaromy M, Wall R (1984) Analysis of membrane and surface protein sequences with the hydrophobic moment plot. J Mol Biol 179:125–142CrossRefPubMedGoogle Scholar
  15. 15.
    Simm S, Einloft J, Mirus O, Schleif E (2016) 50 years of amino acid hydrophobicity scales: revisiting the capacity for peptide classification. Biol Res 49:31CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Chou PY, Fasman GD (1974) Prediction of protein conformation. Biochem 13:222–245CrossRefGoogle Scholar
  17. 17.
    Prevelige P Jr, Fasman GD (1989) Chou-Fasman prediction of the secondary structure of proteins: The Chou-Fasman-Prevelige algorithm. In: Fasman GD (ed) Prediction of protein structure and the principles of protein conformation. Plenum, New York, pp 391–416CrossRefGoogle Scholar
  18. 18.
    Garnier J, Osguthorpe D, Robson B (1978) Analysis of the accuracy and implications of simple methods for predicting the secondary structure of globular proteins. J Mol Biol 120:97–120CrossRefPubMedGoogle Scholar
  19. 19.
    Garnier J, Gibrat JF, Robson B (1996) GOR method for predicting protein secondary structure from amino acid sequence. Methods Enzymol 266:540–553CrossRefPubMedGoogle Scholar
  20. 20.
    Qian N, Sejnowski TJ (1988) Predicting the secondary structure of globular proteins using network models. J Mol Biol 202:865–884CrossRefPubMedGoogle Scholar
  21. 21.
    Cuff JA, Clamp ME, Siddiqui AS, Finlay M, Barton GJ (1998) JPred: a consensus secondary structure prediction server. Bioinformatics 14:892–893CrossRefPubMedGoogle Scholar
  22. 22.
    Cuff JA, Barton GJ (2000) Application of multiple sequence alignment profiles to improve protein secondary structure prediction. Proteins 40:502–511CrossRefPubMedGoogle Scholar
  23. 23.
    Drozdetskiy A, Cole C, Procter J, Barton GJ (2015) JPred4: a protein secondary structure prediction server. Nucl Acids Res 43:W389–W394CrossRefPubMedGoogle Scholar
  24. 24.
    Jones DT (1999) Protein secondary structure prediction based on position-specific scoring matrices. J Mol Biol 292:195–202CrossRefPubMedGoogle Scholar
  25. 25.
    Pollastri G, McLysaght A (2005) Porter: a new, accurate server for protein secondary structure prediction. Bioinformatics 21:1719–1720CrossRefPubMedGoogle Scholar
  26. 26.
    Mirabello C, Pollastri G (2013) Porter, PaleAle 4.0: high-accuracy prediction of protein secondary structure and relative solvent accessibility. Bioinformatics 29:2056–2058CrossRefPubMedGoogle Scholar
  27. 27.
    Lyons J, Dehzangi A, Heffernan R, Sharma A, Paliwal K, Sattar A, Zhou Y, Yang Y (2014) Predicting backbone Cα angles and dihedrals from protein sequences by stacked sparse auto-encoder deep neural network. J Comput Chem 35:2040–2046CrossRefPubMedGoogle Scholar
  28. 28.
    Heffernen 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 5:11476CrossRefGoogle Scholar
  29. 29.
    Pirovano W, Heringa J (2010) Protein secondary structure prediction. In: Carugo O, Eisenhaber F (eds) Data mining techniques for the life sciences, Methods in molecular biology. Humana Press, TotowaGoogle Scholar
  30. 30.
    Pavlopoulou A, Michalopoulos I (2011) State-of-the-art bioinformatics protein structure prediction tools (Review). Int J Mol Med 28:295–310PubMedGoogle Scholar
  31. 31.
    Fredericks ZL, Pielak GJ (1993) Exploring the interface between the N- and C terminal helices of cytochrome c by random mutagenesis within the C-terminal helix. Biochemistry 32:929–936CrossRefPubMedGoogle Scholar
  32. 32.
    He MM, Wood ZA, Baase WA, Xiao H, Matthews BW (2004) Alanine-scanning mutagenesis of the β-sheet region of phage T4 lysozyme suggests that tertiary context has a dominant effect on β-sheet formation. Protein Sci 13:2716–2724CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Conidi A, van den Berghe V, Leslie K, Stryjewska A, Xue H, Chen YG, Seuntjens E, Huylebroeck D (2013) Four amino acids within a tandem QxVx repeat in a predicted extended α helix of the Smad-binding domain of Sip1 are necessary for binding to activated Smad proteins. PLoS ONE 8:10CrossRefGoogle Scholar
  34. 34.
    Kita A, Kita S, Fujisawa I, Inaka K, Ishida T, Horiike K, Nozaki M, Miki K (1999) An archetypical extradiol-cleaving catecholic dioxygenase: the crystal structure of catechol 2,3-dioxygenase (metapyrocatechase) from Pseudomonas putida mt-2. Struct Fold Des 7:25–34CrossRefGoogle Scholar
  35. 35.
    Juers DH, Jacobson RH, Wigley D, Zhang XJ, Huber RH, Tronrud DE, Matthews BW (2000) High resolution refinement of beta-galactosidase in a new crystal form reveals multiple metal binding sites and provides a structural basis for alpha-complementation. Protein Sci 9:1685–1699CrossRefPubMedPubMedCentralGoogle Scholar
  36. 36.
    Miller JH (1972) Experiments in molecular genetics. Cold Spring Harbor Laboratory Press, Cold Spring HarborGoogle Scholar
  37. 37.
    Cole AE, Hani FM, Altman R, Meservey M, Roth JR, Altman E (2017) The promiscuous sumA missense suppressor from Salmonella enterica has an intriguing mechanism of action. Genetics 205:577–588CrossRefPubMedGoogle Scholar
  38. 38.
    Bertani G (1951) Studies on lysogenesis. I. The mode of phage liberation by lysogenic Escherichia coli. J Bacteriol 62:293–300PubMedPubMedCentralGoogle Scholar
  39. 39.
    Casadaban MJ, Cohen SN (1980) Analysis of gene control signals by DNA fusion and cloning in Escherichia coli. J Mol Biol 138:179–207CrossRefPubMedGoogle Scholar
  40. 40.
    Studier FW, Moffatt BA (1986) Use of bacteriophage T7 RNA polymerase to direct selective high level expression of cloned genes. J Mol Biol 189:113–130CrossRefPubMedGoogle Scholar
  41. 41.
    Harayama S, Rekik M, Bairoch A, Neidle EL, Ornston LN (1991) Potential DNA slippage structures acquired during evolutionary divergence of Acinetobacter calcoaceticus chromosomal benABC and Pseudomonas putida TOL pWWO plasmid xylXYZ, genes encoding benzoate dioxygenases. J Bacteriol 173:7540–7548CrossRefPubMedPubMedCentralGoogle Scholar
  42. 42.
    Studier FW, Rosenberg AH, Dunn JJ, Dubendorff JW (1990) Use of T7 RNA polymerase to direct expression of cloned genes. Methods Enzymol 185:60–89CrossRefPubMedGoogle Scholar
  43. 43.
    Costantini S, Colonna G, Facchiano AM (2006) Amino acid propensities for secondary structures are influenced by the protein structural class. Biochem Biophys Res Commun 342:441–551CrossRefPubMedGoogle Scholar
  44. 44.
    O’Neil KT, DeGrado WF (1990) A thermodynamic scale for the helix-forming tendencies of the commonly occurring amino acids. Science 250:646–651CrossRefPubMedGoogle Scholar
  45. 45.
    Pace CN, Scholtz JM (1998) A helix propensity scale based on experimental studies of peptides and proteins. Biophys J 75:422–427CrossRefPubMedPubMedCentralGoogle Scholar
  46. 46.
    Minor DL Jr, Kim PS (1994) Measurement of the β-sheet-forming propensities of amino acids. Nature 367:660–663CrossRefPubMedGoogle Scholar
  47. 47.
    Smith CK, Withka JM, Regan L (1994) A thermodynamic scale for the β-sheet forming tendencies of the amino acids. Biochemistry 33:5510–5517CrossRefPubMedGoogle Scholar
  48. 48.
    Otaki JM, Tsutsumi M, Gotoh T, Yamamoto H (2010) Secondary structure characterization based on amino acid composition and availability in proteins. J Chem Inf Model 50:690–700CrossRefPubMedGoogle Scholar
  49. 49.
    Jacobson RH, Zhang X-J, DuBose RF, Matthews BW (1994) Three-dimensional structure of β-galactosidase from E. coli. Nature 369:761–766CrossRefPubMedGoogle Scholar
  50. 50.
    Whitfield HJ Jr, Martin RG, Ames BN (1966) Classification of aminotransferase (C gene) mutants in the histidine operon. J Mol Biol 21:335–355CrossRefPubMedGoogle Scholar
  51. 51.
    Greeb J, Atkins JF, Loper JC (1971) Histidinol dehydrogenase (hisD) mutants of Salmonella typhimurium. J Bacteriol 106:421–431PubMedPubMedCentralGoogle Scholar
  52. 52.
    Truman P, Bergquist PL (1976) Genetic and biochemical characterization of some missense mutations in the lacZ gene of Escherichia coli K-12. J Bacteriol 126:1063–1074PubMedPubMedCentralGoogle Scholar
  53. 53.
    Bergquist PL, Truman P (1978) Degradation of missense mutant β-galactosidase proteins in Escherichia coli K-12. Mol Gen Genet 164:105–108CrossRefPubMedGoogle Scholar
  54. 54.
    Babu MM, Kriwacki RW, Pappu RV (2012) Versatility from protein disorder. Science 337:1460–1461CrossRefPubMedGoogle Scholar
  55. 55.
    Oldfield CJ, Dunker AK (2014) Intrinsically disordered proteins and intrinsically disordered protein regions. Annu Rev Biochem 83:553–584CrossRefPubMedGoogle Scholar
  56. 56.
    van der Lee R, Buljan M, Lang B, Weatheritt RJ, Daughdrill GW, Dunker AK, Fuxreiter M, Gough J, Gsponer J, Jones DT, Kim PM, Kriwacki RW, Oldfield CJ, Pappu RV, Tompa P, Uversky VN, Wright PE, Babu MM (2014) Classification of intrinsically disordered regions and proteins. Chem Rev 114:6589–6631CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of BiologyMiddle Tennessee State UniversityMurfreesboroUSA
  2. 2.Department of ChemistryMiddle Tennessee State UniversityMurfreesboroUSA
  3. 3.Department of Biomedical EngineeringDuke UniversityDurhamUSA

Personalised recommendations