A Knowledge-Based Method for Protein Structure Refinement and Prediction

Proceedings of the 4th International Conference on Intelligent Systems for Molecular Biology, ISMB 1996

Volumn , Issue , 1996, Pages 218-229

  Subramaniam, Shankar ^a,b   Tcheng, David K ^a   Fenton, James M ^a  

^a UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

^b UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AMINO ACIDS; KNOWLEDGE BASED SYSTEMS; PROBABILITY DENSITY FUNCTION; STRUCTURAL OPTIMIZATION;

ALPHA-BETA; AMINO ACID SEQUENCE; HIGH RESOLUTION; INTERATOMIC INTERACTIONS; KNOWLEDGE-BASED APPROACH; KNOWLEDGE-BASED METHODS; NATIVE CONFORMATION; PROTEINS STRUCTURES; STRUCTURE PREDICTION; STRUCTURE REFINEMENTS;

PROTEINS;

ARTICLE; CHEMICAL STRUCTURE; FACTUAL DATABASE; NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY; PROTEIN CONFORMATION; X RAY CRYSTALLOGRAPHY;

CRYSTALLOGRAPHY, X-RAY; DATABASES, FACTUAL; MAGNETIC RESONANCE SPECTROSCOPY; MODELS, MOLECULAR; PROTEIN CONFORMATION;

EID: 0030341420     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (29)

1. Anfinsen, C.B. Principles that govern the folding of protein chains. Science 181, 223¬ 230 (1973).
2. Dill, K.A. Dominant forces in protein folding. Biochemistry 29, 7133-7155 (1990).
3. Lattman, E . E . and Rose, G.D. Protein folding - What is the question? Proc. Natl. Acad. Sci. U.S.A. 90, 439-441 (1993).
4. Levitt,M. and Warshel. A. Computer simulation of protein folding. Nature 253, 694-698 (1975).
5. Godzik, A., Koliński, A. and Skolnick, J. Are proteins ideal mixtures of amino acids - Analysis of energy parameter sets. Protein Science 10, 2107-2117 (1995).
6. Elofsson, A., Le Grand, S.M., Eisenberg, D. Local moves - An efficient algorithm for simulation of protein folding. Proteins: Struct. Funct. Genet. 23, 73-82 (1995).
7. McCammon, J.A. and Harvey, S.C., Dynamics of Proteins and Nucleic Acids. (Cambridge: Cambridge University Press, 1987).
8. Ponder, J.W. and Richards, P.M. Tertiary templates for proteins - Use of packing criteria in the enumeration of allowed sequences for different structural classes. J. Mol. Biol. 193, 775-791 (1987).
9. Srinivasan, R. and Rose, G.D. LINUS: A hierarchic procedure to predict the fold of a protein. Proteins: Struct. Funct. Genet. 22, 81¬ 99 (1995).
10. Brunger, A.T., Kuriyan, J. and Karplus, M. Crystallographic R-factor refinement by molecular dynamics. Science 235, 458-460 (1987).
11. Branden, CL and Jones, T.A. Between objectivity and subjectivity. Nature 343, 687¬ 689 (1990).
12. Grippen, G.M. Prediction of protein folding from amino acid sequence over discrete conformation spaces. Biochemistry 30, 4232¬ 4237 (1991).
13. Luthy, R., Bowie, J . U . and Eisenberg, D. Assessment of protein models with three-dimensional profiles. Nature 356, 83-85 (1992).
14. Sali, A., Shakhnovich, E. and Karplus, M. How does protein fold? Nature 369, 248-251 (1994).
15. Dill, K A ., Bromberg, S., Yue, K., Fiebig, K.M., Yee, D.P., Thomas, P.D. and Chan, H.S. Principles of protein folding - A perspective from simple exact models. Protein Sci. 4, 561¬ 602 (1995).
16. Karplus, M. and Sali, A. Theoretical studies of protein folding and unfolding. Curr. Opin. Struct. Biol. 5, 58-73 (1995).
17. Sippl, M.J. and Weitckus, S. Detection of native-like models for amino acid sequences of unknown three-dimensional structure in a data base of known protein conformations. Proteins: Struct. Funct. Genet. 13, 258-271 (1992).
18. Jones, D.T., Taylor, W.R. and Thornton, J . M . A new approach to protein fold recognition. Nature 358, 86-89 (1992).
19. Holm, L. and Sander, C. Protein structure comparison by alignment of distance matrices. J. Mol. Biol. 233, 123-138 (1993).
20. Bryant, S.H. and Lawrence, C E . An empirical energy function for threading protein sequence through the folding motif. Proteins: Struct. Funct. Genet. 16, 92-112 (1993).
21. L Hearst, D.P. and Cohen, F . E . GRAFTER - A computational aid for the design of novel proteins. Prot. Eng. 7, 1411-1421 (1994).
22. Sali, A. and Blundell, T.L. Comparative protein modeling by satisfaction of spatial restraints. J. Mol. Biol. 234, 779-815 (1993).
23. Sippl, M.J. Calculation of conformational ensembles from potentials of mean force. An approach to the knowledge-based prediction of local structures in globular proteins. J. Mol. Biol. 213, 859-883 (1990).
24. Sun, S. Reduced representation model of protein structure prediction: Statistical potential and genetic algorithms. Protein Sci. 2, 762-785 (1993).
25. Altschul, S.F., Gish, W., Miller, W., Myers, E.W. and Lipman, D.J. J. Mol. Biol. 215, 403-410 (1990).
26. Hogg, R.V. and Tanis, E.A. Probability and Statistical Inference (New York: MacMillan Pubi. Company, 1988).
27. Brunger, A. T. and Nilges, M. Computational challenges for macromolecular structure determination by x-ray crystallography and solution NMR-spectroscopy. Q. Rev. Biophys. 26, 49-125 (1993).
28. Walsh, L . L . An annotated guide to the Brookhaven Protein Databank, classification and comparisons of protein structures. (Doctoral Dissertation Submitted to the University of Illinois, 1994).
29. The color-coded versions of figures in the manuscript C£m be found on the WWW, whose URL is http://bioweb.ncsa.uiuc.edu/doc/ISMB96.html.