Artificial Intelligence in Medicine
Volume 35, Issue 1 , Pages 19-35 , September 2005

Highly accurate and consistent method for prediction of helix and strand content from primary protein sequences

  • Jishou Ruan

      Affiliations

    • College of Mathematics and LPMC, Nankai University, Tianjin 300071, PR China
  • ,
  • Kui Wang

      Affiliations

    • College of Mathematics and LPMC, Nankai University, Tianjin 300071, PR China
  • ,
  • Jie Yang

      Affiliations

    • College of Mathematics and LPMC, Nankai University, Tianjin 300071, PR China
  • ,
  • Lukasz A. Kurgan

      Affiliations

    • Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada
    • Corresponding Author InformationCorresponding author. Tel.: +1 780 492 5488; fax: +1 780 492 1811.
  • ,
  • Krzysztof Cios

      Affiliations

    • University of Colorado at Denver and Health Sciences Center, Denver, CO, USA
    • University of Colorado at Boulder, Boulder, CO, USA
    • 4cData, LLC, Golden, CO, USA

Received 14 November 2004 ,Revised 22 January 2005 ,Accepted 22 February 2005.

References 

  1. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, et al. The Protein Data Bank. Nucl Acids Res. 2000;28:235–242
  2. Ganapathiraju MK, Klein-Seetharaman J, Balakrishnan N, Reddy R. Characterization of protein secondary structure. IEEE Signal Process Mag. 2004;21(3):78–87
  3. Dwyer DS. Electronic properties of the amino acids side chains contribute to the structural preferences in protein folding. J Biomol Struct Dyn. 2001;18(6):881–892
  4. Rost B. Review: protein secondary structure prediction continues to rise. J Struct Biol. 2001;134(2–3):204–218
  5. Chou PY, Fasman GD. Prediction of the secondary structure of proteins from their amino acid sequence. Adv Enzymol. 1978;47:45–148
  6. Garnier J, Osguthorpe DJ, Robson B. Analysis of the accuracy and implications of simple methods for predicting the secondary structure of globular proteins. J Mol Biol. 1978;120(1):97–120
  7. Rost B, Sander C. Combining evolutionary information and neural networks to predict protein secondary structure. Proteins. 1994;19(1):55–72
  8. Gibrat JF, Garnier J, Robson B. Further developments of protein secondary structure prediction using information theory. New parameters and consideration of residue pairs. J Mol Biol. 1987;198(3):425–443
  9. Schmidler SC, Liu JS, Brutlag DL. Bayesian segmentation of protein secondary structure. J Comput Biol. 2000;7(1–2):233–248
  10. Andrew CD, Penel S, Jones GR, Doig AJ. Stabilizing nonpolar/polar side-chain interactions in the alpha-helix. Proteins. 2001;45(4):449–455
  11. Levitt M, Chothia C. Structural patterns in globular proteins. Nature. 1976;261:552–557
  12. Zhang N. Markov models for classification of protein helices. Biochemistry. 2001;218(4):501–502
  13. Thomas A, Meurisse R, Charloteaux B, Brasseur R. Aromatic side-chain interactions in proteins. I. Main structural features. Proteins. 2002;48(4):628–634
  14. Thomas A, Meurisse R, Brasseur R. Aromatic side-chain interactions in proteins. II. Near- and far-sequence Phe-X pairs. Proteins. 2002;48(4):635–644
  15. Eisenhaber F, Person B, Argos P. Protein structure prediction: recognition of primary, secondary, and tetriary structural features from amino acid sequence. Crit Rev Biochem Mol Biol. 1995;30:1–94
  16. Rost B, Sander C. Third generation prediction of secondary structure. In:  Webstar D editors. Protein Structure Prediction: Methods and Protocols. Clifton, NJ: Humana Press; 2000;p. 71–95
  17. Zhang ZD, Sun ZR, Zhang CT. A new approach to predict the helix/strand content of globular proteins. J Theor Biol. 2001;208:65–78
  18. Sreerama N, Woody RW. Protein secondary structure from circular dichroism spectroscopy. J Mol Biol. 1994;242:497–507
  19. Bussian BM, Sender C. How to determine protein secondary structure in solution by raman spectroscopy: practical guide and test case DNsae I. Biochemistry. 1989;28:4271–4277
  20. Mitchie AD, Orengo CA, Thornton JM. Analysis of domain structural class using an automated class assignment protocol. J Mol Biol. 1996;262:168–185
  21. Krigbaum WR, Knutton SP. Prediction of the amount of secondary structure in a globular protein from its amino acid composition. Proc Natl Acad Sci. 1973;70:2809–2813
  22. Muskal SM, Kim S-H. Predicting protein secondary structure content: a tandem neural network approach. J Mol Biol. 1992;225:713–727
  23. Eisenhaber F, Imperiale F, Argos P, Frommel C. Prediction of secondary structural contents of proteins from their amino acid composition alone. I. New analytic vector decomposition methods. Proteins. 1996;25(2):157–168
  24. Zhang CT, Zhang Z, He Z. Prediction of the secondary structure contents of globular proteins based on three structural classes. J Protein Chem. 1998;17:261–272
  25. Zhang CT, Zhang Z, He Z. Prediction of the secondary structure of globular proteins based on structural classes. J Protein Chem. 1996;15:775–786
  26. Zhang CT, Lin ZS, Zhang Z, Yan M. Prediction of helix/strand content of globular proteins based on their primary sequences. Protein Eng. 1998;11(11):971–979
  27. Cedano J, Aloy P, Peres-Pons JA, Querol E. Relation between amino acid composition and cellular location of proteins. J Mol Biol. 1997;266:594–600
  28. Chou KC. A novel approach to predicting protein structural classes in a (20-1)-D amino acid composition space. Proteins. 1995;21:319–344
  29. Chou KC, Zhang CT. Prediction of protein structural classes. Crit Rev Biochem Mol Biol. 1995;30:275–349
  30. Chou PY. Prediction of protein structural classes from amino acid composition. In:  Fasman GD editors. Prediction of Protein Structures and the Principles of Protein Conformation. New York: Plenum Press; 1989;p. 549–586
  31. Eisenhaber F, Imperiale F, Argos P, Frommel C. Prediction of secondary structural contents of proteins from their amino acid composition alone. II. The paradox with secondary structural class. Proteins. 1996;25(2):169–179
  32. Klein P, Delist C. Prediction of protein structural classes from amino acids sequence. Biopolymers. 1986;25:1659–1672
  33. Kneller DG, Cohen FE, Langridge R. Improvement in protein secondary structure prediction by an enhanced neural network. J Mol Biol. 1990;214:171–182
  34. Sheridan RP, Dixon JS, Venkataraghavan R. Generating plausible protein folds by secondary structure similarity. Int J Pept Protein Res. 1985;25:132–143
  35. Hobohm U, Scharf M, Schneider R, Sander C. Selection of a representative set of structures from the Brookhaven Protein Data Bank. Protein Sci. 1992;1:409–417
  36. Hobohm U, Sander C. Enlarged representative set of protein structures. Protein Sci. 1994;3:522
  37. Kabsch W, Sander C. Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers. 1983;22(12):2577–2637
  38. Hornik K, Stinchcombe M, White H. MLP's are universal approximators. Neural Netw. 1989;2:359–366
  39. Murzin AG, Brenner SE, Hubbard T, Chothia C. SCOP: a natural classification of proteins database for the investigation of sequences and structures. J Mol Biol. 1995;247:536–540
  40. Chou PY, Maggiora GM. Domain structural class prediction. Protein Eng. 1998;11:523–538
  41. Wang Z, Yuan Z. How good is prediction of protein structural class by the component coupled methods. Proteins. 2000;38:165–175
  42. Zhou GP. An intriguing controversy over protein structural class prediction. J Protein Chem. 1998;17:729–738
  43. Kihara D, Skolnick J. The PDB is a covering set of small protein structures. J Mol Biol. 2003;223:793–802

PII: S0933-3657(05)00061-8

doi: 10.1016/j.artmed.2005.02.006

Artificial Intelligence in Medicine
Volume 35, Issue 1 , Pages 19-35 , September 2005