Author(s): Dubchak I, Muchnik I, Holbrook SR, Kim SH
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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.
This article was published in Proc Natl Acad Sci U S A
and referenced in International Journal of Biomedical Data Mining