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. 1995 Sep 12;92(19):8700–8704. doi: 10.1073/pnas.92.19.8700

Prediction of protein folding class using global description of amino acid sequence.

I Dubchak 1, I Muchnik 1, S R Holbrook 1, S H Kim 1
PMCID: PMC41034  PMID: 7568000

Abstract

We present a method for predicting protein folding class based on global protein chain description and a voting process. Selection of the best descriptors was achieved by a computer-simulated neural network trained on a data base consisting of 83 folding classes. Protein-chain descriptors include overall composition, transition, and distribution of amino acid attributes, such as relative hydrophobicity, predicted secondary structure, and predicted solvent exposure. Cross-validation testing was performed on 15 of the largest classes. The test shows that proteins were assigned to the correct class (correct positive prediction) with an average accuracy of 71.7%, whereas the inverse prediction of proteins as not belonging to a particular class (correct negative prediction) was 90-95% accurate. When tested on 254 structures used in this study, the top two predictions contained the correct class in 91% of the cases.

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Selected References

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