Skip to main content
SpringerLink
Log in

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

  • Published:
The Protein Journal Aims and scope Submit manuscript

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.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

References

  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 230:75–101

    Article  Google Scholar 

  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 232:333–394

    Article  Google Scholar 

  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 150:533–551

    Article  CAS  Google Scholar 

  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 37:729–740

    Article  CAS  PubMed  Google Scholar 

  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 37:205–211

    Article  CAS  PubMed  Google Scholar 

  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 181:662–666

    Article  CAS  PubMed  Google Scholar 

  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 185:416–422

    Article  CAS  PubMed  Google Scholar 

  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 206:757–761

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  10. Tanford C (1962) Contribution of hydrophobic interactions to the stability of the globular conformation of proteins. J Am Chem Soc 84:4240–4247

    Article  CAS  Google Scholar 

  11. Zimmerman JM, Eliezer N, Simha R (1968) The characterization of amino acid sequences in proteins by statistical methods. J Theor Biol 21:170–201

    Article  CAS  PubMed  Google Scholar 

  12. Kyte J, Doolittle RF (1982) A simple method for displaying the hydrophathic character of a protein. J Mol Biol 157:105–132

    Article  CAS  PubMed  Google Scholar 

  13. Hopp TP, Woods KR (1983) A computer program for predicting protein antigenic determinants. Mol Immunol 20:483–489

    Article  CAS  PubMed  Google Scholar 

  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 179:125–142

    Article  CAS  PubMed  Google Scholar 

  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 49:31

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Chou PY, Fasman GD (1974) Prediction of protein conformation. Biochem 13:222–245

    Article  CAS  Google Scholar 

  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 391–416

    Chapter  Google Scholar 

  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 120:97–120

    Article  CAS  PubMed  Google Scholar 

  19. Garnier J, Gibrat JF, Robson B (1996) GOR method for predicting protein secondary structure from amino acid sequence. Methods Enzymol 266:540–553

    Article  CAS  PubMed  Google Scholar 

  20. Qian N, Sejnowski TJ (1988) Predicting the secondary structure of globular proteins using network models. J Mol Biol 202:865–884

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  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 35:2040–2046

    Article  CAS  PubMed  Google Scholar 

  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 5:11476

    Article  Google Scholar 

  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, Totowa

    Google Scholar 

  30. Pavlopoulou A, Michalopoulos I (2011) State-of-the-art bioinformatics protein structure prediction tools (Review). Int J Mol Med 28:295–310

    CAS  PubMed  Google Scholar 

  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 32:929–936

    Article  CAS  PubMed  Google Scholar 

  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 13:2716–2724

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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 8:10

    Article  CAS  Google Scholar 

  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 7:25–34

    Article  CAS  Google Scholar 

  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 9:1685–1699

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Miller JH (1972) Experiments in molecular genetics. Cold Spring Harbor Laboratory Press, Cold Spring Harbor

    Google Scholar 

  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 205:577–588

    Article  CAS  PubMed  Google Scholar 

  38. Bertani G (1951) Studies on lysogenesis. I. The mode of phage liberation by lysogenic Escherichia coli. J Bacteriol 62:293–300

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Casadaban MJ, Cohen SN (1980) Analysis of gene control signals by DNA fusion and cloning in Escherichia coli. J Mol Biol 138:179–207

    Article  CAS  PubMed  Google Scholar 

  40. Studier FW, Moffatt BA (1986) Use of bacteriophage T7 RNA polymerase to direct selective high level expression of cloned genes. J Mol Biol 189:113–130

    Article  CAS  PubMed  Google Scholar 

  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 173:7540–7548

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Studier FW, Rosenberg AH, Dunn JJ, Dubendorff JW (1990) Use of T7 RNA polymerase to direct expression of cloned genes. Methods Enzymol 185:60–89

    Article  CAS  PubMed  Google Scholar 

  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 342:441–551

    Article  CAS  PubMed  Google Scholar 

  44. O’Neil KT, DeGrado WF (1990) A thermodynamic scale for the helix-forming tendencies of the commonly occurring amino acids. Science 250:646–651

    Article  PubMed  Google Scholar 

  45. Pace CN, Scholtz JM (1998) A helix propensity scale based on experimental studies of peptides and proteins. Biophys J 75:422–427

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Minor DL Jr, Kim PS (1994) Measurement of the β-sheet-forming propensities of amino acids. Nature 367:660–663

    Article  CAS  PubMed  Google Scholar 

  47. Smith CK, Withka JM, Regan L (1994) A thermodynamic scale for the β-sheet forming tendencies of the amino acids. Biochemistry 33:5510–5517

    Article  CAS  PubMed  Google Scholar 

  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 50:690–700

    Article  CAS  PubMed  Google Scholar 

  49. Jacobson RH, Zhang X-J, DuBose RF, Matthews BW (1994) Three-dimensional structure of β-galactosidase from E. coli. Nature 369:761–766

    Article  CAS  PubMed  Google Scholar 

  50. Whitfield HJ Jr, Martin RG, Ames BN (1966) Classification of aminotransferase (C gene) mutants in the histidine operon. J Mol Biol 21:335–355

    Article  CAS  PubMed  Google Scholar 

  51. Greeb J, Atkins JF, Loper JC (1971) Histidinol dehydrogenase (hisD) mutants of Salmonella typhimurium. J Bacteriol 106:421–431

    CAS  PubMed  PubMed Central  Google Scholar 

  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 126:1063–1074

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Bergquist PL, Truman P (1978) Degradation of missense mutant β-galactosidase proteins in Escherichia coli K-12. Mol Gen Genet 164:105–108

    Article  CAS  PubMed  Google Scholar 

  54. Babu MM, Kriwacki RW, Pappu RV (2012) Versatility from protein disorder. Science 337:1460–1461

    Article  CAS  PubMed  Google Scholar 

  55. Oldfield CJ, Dunker AK (2014) Intrinsically disordered proteins and intrinsically disordered protein regions. Annu Rev Biochem 83:553–584

    Article  CAS  PubMed  Google Scholar 

  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 114:6589–6631

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Biology, Middle Tennessee State University, 440 Friendship St., Murfreesboro, TN, 37132, USA

    Ashley E. Cole, Fatmah M. Hani & Elliot Altman

  2. Department of Chemistry, Middle Tennessee State University, Murfreesboro, TN, 37132, USA

    Paul C. Kline

  3. Department of Biomedical Engineering, Duke University, Durham, NC, 27708, USA

    Brian W. Allen

Authors
  1. Ashley E. Cole
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fatmah M. Hani
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Brian W. Allen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Paul C. Kline
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Elliot Altman
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Elliot Altman.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cole, A.E., Hani, F.M., Allen, B.W. et al. Nonfunctional Missense Mutants in Two Well Characterized Cytosolic Enzymes Reveal Important Information About Protein Structure and Function. Protein J 37, 407–427 (2018). https://doi.org/10.1007/s10930-018-9786-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10930-018-9786-6

Keywords

Search

Navigation