The prediction of the degree of exposure to solvent of amino acid residues via genetic programming   [GP]

by

Handley, S., G. and Handley, S.

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Info: Second International Conference on Intelligent Systems for Molecular Biology (Conference proceedings), 1994
Keywords:genetic algorithms, genetic programming
Abstract:
In this paper I evolve programs that predict the degree of exposure to solvent (the buriedness) of amino acid residues given only the primary structure. I use genetic programming to evolve programs [GP] that take as input the primary structure and that output the buriedness of each residue. I trained these programs on a set of 82 proteins from the Brookhaven Protein Data Bank (PDB) and cross-validated them on a separate testing set of 40 proteins, also from the PDB. The best program evolved had a correlation of 0.434 between the predicted and observed buriednesses on the testing set.
URL(s):(G)zipped postscript

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BibTex:
@InProceedings{handley:1994:solvent,
  author =       "Simon G. Handley",
  title =        "The prediction of the degree of exposure to solvent of
                 amino acid residues via genetic programming",
  booktitle =    "Second International Conference on Intelligent Systems
                 for Molecular Biology",
  year =         "1994",
  address =      "Stanford University, Stanford, CA, USA",
  publisher =    "AAAI Press",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www-leland.stanford.edu/~shandley/postscript/pburied.ps.gz",
  abstract =     "In this paper I evolve programs that predict the
                 degree of exposure to solvent (the buriedness) of amino
                 acid residues given only the primary structure. I use
                 genetic programming to evolve programs that take as
                 input the primary structure and that output the
                 buriedness of each residue. I trained these programs on
                 a set of 82 proteins from the Brookhaven Protein Data
                 Bank (PDB) and cross-validated them on a separate
                 testing set of 40 proteins, also from the PDB. The best
                 program evolved had a correlation of 0.434 between the
                 predicted and observed buriednesses on the testing
                 set.",
}