Linear regression models for solvent accessibility prediction in proteins

Journal of Computational Biology

Volumn 12, Issue 3, 2005, Pages 355-369

  Wagner, Michael ^a   Adamczak, Rafał ^a   Porollo, Aleksey ^a   Meller, Jarosław ^a,b  

^a CINCINNATI CHILDREN'S HOSPITAL MEDICAL CENTER   (United States)

^b NICOLAUS COPERNICUS UNIVERSITY   (Poland)

Author keywords

Classification; Least squares regression; Neural networks; Protein structure prediction; Relative solvent accessibility; Support vector regression

Indexed keywords

PROTEIN; SOLVENT;

ACCURACY; ARTICLE; ERROR; LINEAR REGRESSION ANALYSIS; MATHEMATICAL COMPUTING; PREDICTION; PRIORITY JOURNAL; PROTEIN STRUCTURE;

AMINO ACIDS; COMPUTER SIMULATION; DATA INTERPRETATION, STATISTICAL; LEAST-SQUARES ANALYSIS; LINEAR MODELS; PROTEINS; SOLUBILITY; SOLVENTS;

EID: 17844381893     PISSN: 10665277     EISSN: None     Source Type: Journal    
DOI: 10.1089/cmb.2005.12.355     Document Type: Article

References (33)

1. Adamczak, R., Porollo, A., and Meller, J. 2004. Accurate prediction of solvent accessibility using neural networks based regression. Proteins 56, 753-767.
2. Ahmad, S., and Gromiha, M. 2002. NET: Neural network based prediction of solvent accessibility. Bioinformatics 18, 819-824.
3. Ahmad, S., Gromiha, M., and Sarai, A. 2003. Real value prediction of solvent accessibility from amino acid sequence. Proteins 50, 629-635.
4. Altschul, S., Madden, T., and Schaffer, A. 1997. Gapped BLAST and Psi-BLAST: A new generation of protein database search programs. Nucl. Acids Res. 25, 3389-3402.
5. Bateman, A., Birney, E., Cerruti, L., Durbin, R., Etwiller, L., Eddy, S., Griffiths-Jones, S., Howe, K., Marshall, M., and Sonnhammer, E. 2002. The Pfam protein families database. Nucl. Acids Res. 30(1), 276-280.
6. Benson, D., Karsch-Mizrachi, I., Lipman, D., Ostell, J., and Wheeler, D. 2003. GenBank. Nucl. Acids Res. 31(1), 23-27.
7. Berman, H., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T., Weissig, H., Shindyalov, I., and Bourne, P. 2000. The Protein Data Bank. Nucl. Acids Res. 28, 235-242.
8. Chothia, C. 1976. The nature of accessible and buried surfaces in proteins. J. Mol. Biol. 105, 1-14.
9. Cuff, J., and Barton, G. 1999. Application of enhanced multiple sequence alignment profiles to improve protein secondary structure prediction. Proteins 40, 502-511.
10. Czyzyk, J., Mehrotra, S., Wagner, M., and Wright, S. 1999. PCx: An interior-point code for linear programming. Optim. Method. Software 12, 397-430.
11. Eyrich, V., Marti-Renom, M., Madhusudhan, M., Fiser, A., Pazos, F., Valencia, A., Sali, A., and Rost, B. 2001. EVA: Continuous automatic evaluation of protein structure prediction servers. Bioinformatics 17, 1242-1243.
12. Fischer, D., Elofsson, A., Rychlewski, L., Pazos, F., Valencia, A., Rost, B., Ortiz, A., and Dunbrack, R. 2001. CAFASP2: The second critical assessment of fully automated structure prediction methods. Proteins Suppl. 5, 171-183.
13. Hastie, T., Tibshirani, R., and Friedman, J. 2001. Elements of Statistical Learning: Data Mining, Inference and Prediction, Springer Verlag, New York.
14. Jones, D. 1999. Protein secondary structure prediction based on position-specific scoring matrices. J. Mol. Biol. 292, 195-202.
15. Kabsch, W., and Sander, C. 1983. Dictionary of protein secondary structure: Pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 22, 2577-2637.
16. Kim, H., and Park, H. 2004. Prediction of protein relative solvent accessibility with support vector machines and long-range interaction 3D local descriptor. Proteins 54, 557-562.
17. The Mathworks. Matlab, Version 7.0, The Mathworks, Natick, MA.
18. Matthews, B. 1975. Comparison of predicted and observed secondary structure of T4 ohage lysozyme. Biochem. Biophys. Acta. 405, 442-451.
19. Meller, J., and Elber, R. 2000. LOOPP: Learning, observing and outputting protein patterns. www.cbsu.tc.cornell.edu/software/loopp.
20. Meller, J., and Elber, R. 2001. Linear optimization and a double statistical filter for protein threading protocols. Proteins 45, 241-261.
21. Naderi-Manesh, H., Sadeghi, M., Arab, S., and Moosavi-Movahedi, A. 2001. Prediction of protein surface accessibility with information theory. Proteins 42, 452-459.
22. Pollastri, G., Baldi, P., Fariselli, P., and Casadio, R. 2002. Prediction of coordination number and relative solvent accessibility in proteins. Proteins 47, 142-153.
23. Przybylski, D., and Rost, B. 2002. Alignments grow, secondary structure prediction improves. Proteins 46, 197-205.
24. Richardson, C., and Barlow, D. 1999. The bottom line for prediction of residue solvent accessibility. Protein Eng. 12, 1051-1054.
25. Rost, B. 2001. Protein secondary structure prediction continues to rise. J. Struct. Biol. 134, 204-218.
26. Rost, B., and Sander, C. 1994a. Conservation and prediction of solvent accessibility in protein families. Proteins 20, 216-226.
27. Rost, B., and Sander, C. 1994b. Combining evolutionary information and neural networks to predict protein secondary structure. Proteins 19, 55-72.
28. Schonbrun, J., Wedemeyer, W., and Baker, D. 2002. Protein structure prediction in 2002. Curr. Opin. Struct. Biol. 12, 348-354.
29. Simons, K., Kooperberg, C., Huang, E., and Baker, D. 1997. Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and Bayesian scoring functions. J. Mol. Biol. 268, 209-225.
30. Thompson, M., and Goldstein, R. 1996. Predicting solvent accessibility: Higher accuracy using Bayesian statistics and optimized residue substitution classes. Proteins 25, 38-47.
31. Venclovas, C., Zemla, A., Fidelis, K., and Moult, J. 2001. Comparison of performance in successive CASP experiments. Proteins Suppl. 5, 163-170.
32. Wagner, M., Meller, J., and R. Elber, R. 2004. Large-scale linear programming techniques for the design of protein folding potentials. Math. Program. 101, 301-318.
33. Wagner, M., Naik, D., Pothen, A., Kasukurti, S., Devineni, R., Adam, B., Semmes, O., and Wright Jr., G. 2004. Computational protein biomarker prediction: A case study for prostate cancer. BMC Bioinformatics 5, 26.