Nothing Special   »   [go: up one dir, main page]
Skip to main content

Advertisement

Springer Nature Link
Log in

Real value prediction of protein folding rate change upon point mutation

  • Published:
Journal of Computer-Aided Molecular Design Aims and scope Submit manuscript

Abstract

Prediction of protein folding rate change upon amino acid substitution is an important and challenging problem in protein folding kinetics and design. In this work, we have analyzed the relationship between amino acid properties and folding rate change upon mutation. Our analysis showed that the correlation is not significant with any of the studied properties in a dataset of 476 mutants. Further, we have classified the mutants based on their locations in different secondary structures and solvent accessibility. For each category, we have selected a specific combination of amino acid properties using genetic algorithm and developed a prediction scheme based on quadratic regression models for predicting the folding rate change upon mutation. Our results showed a 10-fold cross validation correlation of 0.72 between experimental and predicted change in protein folding rates. The correlation is 0.73, 0.65 and 0.79, respectively in strand, helix and coil segments. The method has been further tested with an extended dataset of 621 mutants and a blind dataset of 62 mutants, and we observed a good agreement with experiments. We have developed a web server for predicting the folding rate change upon mutation and it is available at http://bioinformatics.myweb.hinet.net/fora.htm.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Apetri AC, Surewicz K, Surewicz WK (2004) J Biol Chem 279(17):18008

    Article  CAS  Google Scholar 

  2. Capriotti E, Casadio R (2007) Bioinformatics 23(3):385

    Article  CAS  Google Scholar 

  3. Jenkins DC, Pearson DS, Harvey A, Sylvester ID, Geeves MA, Pinheiro TJ (2009) Eur Biophys J 38(5):625

    Article  CAS  Google Scholar 

  4. Hart T, Hosszu LL, Trevitt CR, Jackson GS, Waltho JP, Collinge J, Clarke AR (2009) Proc Natl Acad Sci USA 106(14):5651

    Article  CAS  Google Scholar 

  5. Maxwell KL, Wildes D, Zarrine-Afsar A, De Los Rios MA, Brown AG, Friel CT, Hedberg L, Horng JC, Bona D, Miller EJ, Vallee-Belisle A, Main ER, Bemporad F, Qiu L, Teilum K, Vu ND, Edwards AM, Ruczinski I, Poulsen FM, Kragelund BB, Michnick SW, Chiti F, Bai Y, Hagen SJ, Serrano L, Oliveberg M, Raleigh DP, Wittung-Stafshede P, Radford SE, Jackson SE, Sosnick TR, Marqusee S, Davidson AR, Plaxco KW (2005) Protein Sci 14(3):602

    Google Scholar 

  6. Jackson S (1998) Fold Des 3(4):R81

    Article  CAS  Google Scholar 

  7. Gromiha MM, Huang LT (2011) Curr Protein Pept Sci 12(6):490

    Article  CAS  Google Scholar 

  8. Gromiha MM (2010) Protein Bioinformatics: From Sequence to Function. Academic Press, Singapore

    Google Scholar 

  9. Fulton KF, Devlin GL, Jodun RA, Silvestri L, Bottomley SP, Fersht AR, Buckle AM (2005) Nucleic Acids Res 33(Database issue):D279

  10. Bogatyreva NS, Osypov AA, Ivankov DN (2009) Nucleic Acids Res 37(Database issue):D342

  11. Huang L-T, Gromiha MM (2010) Bioinformatics 26(17):2121

    Article  CAS  Google Scholar 

  12. Plaxco KW, Simons KT, Baker D (1998) J Mol Biol 277(4):985

    Article  CAS  Google Scholar 

  13. Gromiha MM, Selvaraj S (2001) J Mol Biol 310(1):27

    Article  CAS  Google Scholar 

  14. Zhou H, Zhou Y (2002) Biophys J 82(1 Pt 1):458

    Article  CAS  Google Scholar 

  15. Micheletti C (2003) Proteins 51(1):74

    Article  CAS  Google Scholar 

  16. Gromiha MM (2009) J Chem Inf Model 49(4):1130

    Article  CAS  Google Scholar 

  17. Gromiha MM (2003) J Chem Inf Comput Sci 43(5):1481

    Article  CAS  Google Scholar 

  18. Gromiha MM (2005) J Chem Inf Model 45(2):494

    Article  CAS  Google Scholar 

  19. Huang JT, Tian J (2006) Proteins 63(3):551

    Article  Google Scholar 

  20. Gromiha MM, Thangakani AM, Selvaraj S (2006) Nucleic Acids Res 34(Web Server issue):W70

  21. Ivankov DN, Finkelstein AV (2004) Proc Natl Acad Sci USA 101(24):8942

    Article  CAS  Google Scholar 

  22. Punta M, Rost B (2005) J Mol Biol 348(3):507

    Article  CAS  Google Scholar 

  23. Ma BG, Guo JX, Zhang HY (2006) Proteins 65(2):362

    Article  CAS  Google Scholar 

  24. Huang LT, Gromiha MM (2008) J Comput Chem 29(10):1675

    Article  CAS  Google Scholar 

  25. Huang JT, Cheng JP, Chen H (2007) Proteins 67(1):12

    Article  CAS  Google Scholar 

  26. Jiang Y, Iglinski P, Kurgan L (2009) J Comput Chem 30(5):772

    Article  CAS  Google Scholar 

  27. Gao J, Zhang T, Zhang H, Shen S, Ruan J, Kurgan L (2010) Proteins 78(9):2114

    CAS  Google Scholar 

  28. Gromiha Selvaraj (2008) Curr Bioinforma 3(1):1

    Article  CAS  Google Scholar 

  29. Prabakaran P, An J, Gromiha MM, Selvaraj S, Uedaira H, Kono H, Sarai A (2001) Bioinformatics 17(11):1027

    Article  CAS  Google Scholar 

  30. Porter CT, Bartlett GJ, Thornton JM (2004) Nucleic Acids Res 32(Database issue):D129

  31. Lopez G, Valencia A, Tress M (2007) Nucleic Acids Res 35(Database issue):D219

  32. Kumar MD, Gromiha MM (2006) Nucleic Acids Res 34(Database issue):D195

  33. Gromiha MM, Yabuki Y, Suresh MX, Thangakani AM, Suwa M, Fukui K (2009) Nucleic Acids Res 37(Database issue):D201

  34. Gromiha MM, An J, Kono H, Oobatake M, Uedaira H, Sarai A (1999) Nucleic Acids Res 27(1):286

    Article  CAS  Google Scholar 

  35. Guerois R, Nielsen JE, Serrano L (2002) J Mol Biol 320(2):369

    Article  CAS  Google Scholar 

  36. Bordner AJ, Abagyan RA (2004) Proteins 57(2):400

    Article  CAS  Google Scholar 

  37. Capriotti E, Fariselli P, Calabrese R, Casadio R (2005) Bioinformatics 21(Suppl 2):ii54

    Article  CAS  Google Scholar 

  38. Cheng J, Randall A, Baldi P (2006) Proteins 62(4):1125

    Article  CAS  Google Scholar 

  39. Yin S, Ding F, Dokholyan NV (2007) Nat Methods 4(6):466

    Article  CAS  Google Scholar 

  40. Bromberg Y, Yachdav G, Rost B (2008) Bioinformatics 24(20):2397

    Article  CAS  Google Scholar 

  41. Huang LT, Gromiha MM (2009) Bioinformatics 25(17):2181

    Article  CAS  Google Scholar 

  42. Dehouck Y, Grosfils A, Folch B, Gilis D, Bogaerts P, Rooman M (2009) Bioinformatics 25(19):2537

    Article  CAS  Google Scholar 

  43. Carlsson J, Soussi T, Persson B (2009) FEBS J 276(15):4142

    Article  CAS  Google Scholar 

  44. Gao S, Zhang N, Duan GY, Yang Z, Ruan JS, Zhang T (2009) Hum Mutat 30(8):1161

    Article  CAS  Google Scholar 

  45. Munoz V, Eaton WA (1999) Proc Natl Acad Sci USA 96(20):11311

    Article  CAS  Google Scholar 

  46. Weikl TR (2005) Proteins 60(4):701

    Article  CAS  Google Scholar 

  47. Tomii K, Kanehisa M (1996) Protein Eng 9(1):27

    Article  CAS  Google Scholar 

  48. Gromiha MM, Oobatake M, Sarai A (1999) Biophys Chem 82(1):51

    Article  CAS  Google Scholar 

  49. Gromiha MM, Oobatake M, Kono H, Uedaira H, Sarai A (2000) J Biomol Struct Dyn 18(2):281

    CAS  Google Scholar 

  50. Kawashima S, Pokarowski P, Pokarowska M, Kolinski A, Katayama T, Kanehisa M (2008) Nucleic Acids Res 36(Database issue):D202

  51. Kawashima S, Ogata H, Kanehisa M (1999) Nucleic Acids Res 27(1):368

    Article  CAS  Google Scholar 

  52. Gromiha MM, Oobatake M, Kono H, Uedaira H, Sarai A (1999) Protein Eng 12(7):549

    Article  CAS  Google Scholar 

  53. Kabsch W, Sander C (1983) Biopolymers 22(12):2577

    Article  CAS  Google Scholar 

  54. Gromiha MM, Selvaraj S (1997) J of Biol Phys 23(3):151

    Article  CAS  Google Scholar 

  55. Gromiha MM, Selvaraj S (2004) Prog Biophys Mol Biol 86(2):235

    Article  CAS  Google Scholar 

  56. Friel CT, Capaldi AP, Radford SE (2003) J Mol Biol 326(1):293

    Article  CAS  Google Scholar 

  57. Gromiha MM (2007) Biochem Soc Trans 35(Pt 6):1569

    Article  CAS  Google Scholar 

  58. Gromiha MM, Huang LT (2011) Curr Protein Pept Sci 12(6):490

    Google Scholar 

  59. Ahmad S, Gromiha MM, Sarai A (2004) Bioinformatics 20(4):477

    Article  CAS  Google Scholar 

  60. Chang C–C, Lin C-J (2001):http://www.csie.ntu.edu.tw/~cjlin/libsvm

  61. Wu T-F, Lin C-J, Weng R (2004) J Mach Learn Res 5:975

    Google Scholar 

  62. Moody J, Darken C (1989) Neural Comput 1(2):281

    Article  Google Scholar 

  63. Rumelhart DE, Hinton GE, Williams RJ (1986) In Parallel distributed processing: explorations in the microstructure of cognition, vol. 1. Cambridge, MA, USA, MIT Press, pp 318

  64. Cleary JG, Trigg LE (1995) In Proceedings of the 12th international conference on machine learning. San Francisco: Morgan Kaufmann, pp 108

  65. Hastie T, Tibshirani R, Friedman JH (2009) The elements of statistical learning : data mining, inference, and prediction, 2nd edn. Springer, New York, NY

    Google Scholar 

  66. Naganathan AN, Munoz V (2010) Proc Natl Acad Sci USA 107(19):8611

    Article  CAS  Google Scholar 

  67. Koshi JM, Goldstein RA (1995) Protein Eng 8(7):641

    CAS  Google Scholar 

  68. Luthy R, McLachlan AD, Eisenberg D (1991) Proteins 10(3):229

    Article  CAS  Google Scholar 

  69. Tobi D, Shafran G, Linial N, Elber R (2000) Proteins 40(1):71

    Article  CAS  Google Scholar 

  70. Miyazawa S, Jernigan R (1985) Macromolecules 18(3):534

    Article  CAS  Google Scholar 

Download references

Acknowledgments

We thank the anonymous reviewers for their constructive comments to improve the manuscript.

Author information

Authors and Affiliations

  1. Department of Biotechnology, Mingdao University, Changhua, 523, Taiwan

    Liang-Tsung Huang

  2. Department of Biotechnology, Indian Institute of Technology (IIT) Madras, Chennai, 600 036, Tamilnadu, India

    M. Michael Gromiha

Authors
  1. Liang-Tsung Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. Michael Gromiha
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to M. Michael Gromiha.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOC 1503 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Huang, LT., Gromiha, M.M. Real value prediction of protein folding rate change upon point mutation. J Comput Aided Mol Des 26, 339–347 (2012). https://doi.org/10.1007/s10822-012-9560-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10822-012-9560-3

Keywords

Search

Navigation