Real value prediction of solvent accessibility in proteins using multiple sequence alignment and secondary structure

Proteins: Structure, Function and Genetics

Volumn 61, Issue 2, 2005, Pages 318-324

  Garg, Aarti ^a   Kaur, Harpreet ^a   Raghava, G P S ^a  

^a INSTITUTE OF MICROBIAL TECHNOLOGY   (India)

Author keywords

Multiple alignment; Neural network; Prediction; Real value; Secondary structure; Solvent accessibility

Indexed keywords

PROTEIN; SOLVENT; THIOREDOXIN;

ARTICLE; ARTIFICIAL NEURAL NETWORK; MATHEMATICAL ANALYSIS; PREDICTION; PRIORITY JOURNAL; PROTEIN SECONDARY STRUCTURE; PROTEIN STRUCTURE; STRUCTURE ANALYSIS;

AMINO ACIDS; NEURAL NETWORKS (COMPUTER); PROTEIN STRUCTURE, SECONDARY; PROTEINS; REPRODUCIBILITY OF RESULTS; SEQUENCE ALIGNMENT; SEQUENCE ANALYSIS, PROTEIN; SOLVENTS; THIOREDOXIN;

EID: 26444473604     PISSN: 08873585     EISSN: None     Source Type: Journal    
DOI: 10.1002/prot.20630     Document Type: Article

References (29)

1. Chan HS, Dill KA. Origins of structures in globular proteins. Proc Natl Acad Sci USA 1990;87:6388-6392.
2. Ooi T, Oobatake M, Nemethy G, Scheraga HA. Accessible surface areas as a measure of the thermodynamics parameters of hydration of peptides. Proc Natl Acad Sci USA 1987;84:3086-3090.
3. Andrade MA, O'Donoghue SI, Host B. Adaptation of protein surfaces to subcellular location. J Mol Biol 1998;276:517-525.
4. Ehrlich L, Reczko M, Bohr H, Wade RC. Prediction of protein hydration sites from sequence by modular neural networks. Protein Eng 1998;11:11-19.
5. Rost B, Sander C. Conservation and prediction of solvent accessibility in protein families. Proteins 1994;20:216-226.
6. Cuff JA, Barton GJ. Application of multiple sequence alignment profiles to improve protein secondary structure prediction. Proteins 2000;40:502-511.
7. Ahmad S, Gromiha MM. NETASA: neural network based prediction of solvent accessibility. Bioinformatics 2002;18:819-824.
8. Thompson MJ, Goldstein RA. Predicting solvent accessibility: higher accuracy using Bayesian statistics and optimized residue substitution classes. Proteins 1996;25:38-47.
9. Li X, Pan X-M. New methods for accurate prediction of solvent accessibility from protein sequence. Proteins 2001;42:1-5.
10. Mucchielli-Giorgi MH, Hazout S, Tuffery P. PredAcc: prediction of solvent accessibility. Bioinformatics 1999;15:176-172.
11. Manesh HN, Sadeghi M, Arab S, Movahedi AM. Prediction of protein surface accessibility with information theory. Proteins 2001;42:452-459.
12. Yuan Z, Burrage K, Mattick JS. Prediction of protein solvent accessibility using support vector machines. Proteins 2002;48:566-570.
13. Ahmad S, Gromiha MM, Sarai A. Real value prediction of solvent accessibility from amino acid sequence. Proteins 2003;50:629-635.
14. Ahmad S, Gromiha MM and Sarai A. RVP-net: online prediction of real valued accessible surface area of proteins from single sequences. Bioinformatics 2003;19:1849-1851.
15. Adamczak R, Porollo A, Meller J. Accurate prediction of solvent accessibility using neural networks-based regression. Proteins 2004;56:753-767.
16. Yuan Z, Huang B. Prediction of protein accessible surface areas by support vector regression. Proteins 2004;57:558-564.
17. Jones DT. Protein secondary structure prediction based on position-specific scoring matrices. J Mol Biol 1999;292:195-202.
18. Kaur H, Raghava GPS. A Neural network based method for prediction of gamma-turns in proteins from multiple sequence alignment and secondary structure information. Protein Sci 2003; 12:923-929.
19. Kaur H, Raghava GPS. Prediction of β-turns in proteins from multiple alignment using neural network. Protein Sci 2003;12:627-634.
20. Kaur H, Raghava GPS. Prediction of alpha-turns in proteins using PSI-BLAST profiles and secondary structure information. Proteins 2004;55:83-90.
21. Pollastri G, Baldi P, Fariselli P, Casadio R. Prediction of coordination number and relative solvent accessibility in proteins. Proteins 2002;47:142-153.
22. Cuff JA, Barton GJ. Application of enhanced multiple sequence alignment profiles to improve protein secondary structure prediction. Proteins 1994;40:502-511.
23. Rost B, Sander C. Conservation and prediction of solvent accessibility in protein families. Proteins 1994;20:216-226.
24. Kabsch W, Sander C. Dictionary of protein secondary structure: pattern recognition of hydrogen bond and geometrical features. Biopolymers 1983;22:2577-2637.
25. Chou KC, Zhang CT. Prediction of protein structural classes. Crit Rev Biochem Mol Biol 1995;30:275-349.
26. Zell A, Mamier G. Stuttgart neural network simulator, version 4.2. Stuttgart, Germany: University of Stuttgart.
27. Romelhart DE, Hinton GE, Williams RJ. Learning representations by back-propagation errors. Nature 1986;323:533-536.
28. Altschul SF, Madden TL, Alejandro AS, Zhang J, Zhang Z, Miller W, Lipman DJ. Gapped blast and psi-blast: a new generation of protein databases and search programs. Nucleic Acids Res 1997;25: 3389-3402.
29. Maconald JR Jr, Johnson WC. Environmental features are important in determining protein secondary structure. Protein Sci 2001;10:1172-1177.