GOR method

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The GOR method (Garnier-Osguthorpe-Robson) is an information theory-based method for the prediction of secondary structures in proteins.[1] It was developed in the late 1970's shortly after the simpler Chou-Fasman method. Like Chou-Fasman, the GOR method is based on probability parameters derived from empirical studies of known protein tertiary structures solved by X-ray crystallography. However, unlike Chou-Fasman, the GOR method takes into account not only the propensities of individual amino acids to form particular secondary structures, but also the conditional probability of the amino acid to form a secondary structure given that its immediate neighbors have already formed that structure. The method is therefore essentially Bayesian in its analysis.[2]

The GOR method analyzes sequences to predict alpha helix, beta sheet, turn, or random coil secondary structure at each position based on 17-amino acid sequence windows. The original description of the method included four scoring matrices of size 17x20, where the columns correspond to the log-odds score, which reflects the probability of finding a given amino acid at each position in the 17-residue sequence. The four matrices reflect the probabilities of the central, eighth amino acid being in a helical, sheet, turn, or coil conformation. In subsequent revisions to the method, the turn matrix was eliminated due to the high variability of sequences in turn regions (particularly over such a large window). The method requires at least four contiguous residues to score as alpha helices to classify the region as helical, and at least two contiguous residues for a beta sheet.[3]

[edit] References

  1. ^ Garnier J, Gibrat JF, Robson B. (1996). GOR method for predicting protein secondary structure from amino acid sequence. Methods Enzymol 266:540-53.
  2. ^ Garnier J, Osguthorpe DJ, Robson B. (1978). Analysis of the accuracy and implications of simple methods for predicting the seconday structure of globular proteins. J Mol Biol 120:97-120.
  3. ^ Mount DM (2004). Bioinformatics: Sequence and Genome Analysis, 2, Cold Spring Harbor Laboratory Press. ISBN 0879697121.

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