engleski

New binary algorithm for the prediction of protein folding types

New binary algorithm for the prediction of alpha and beta protein folding types from RNA, DNA and amino acid sequences is described. The algorithm was tested with machine learning SMO classifier for the support vector machines and classification trees, on a dataset of 140 dissimilar protein folds. Depending on the method of testing, the overall classification accuracy was > 90 and the tenfold cross validation result of the procedure was > 80%. The method enables quick, simple and accurate prediction of alpha and beta protein folds on a personal computer by means of few binary patterns of coded amino acid and nucleotide physicochemical properties. Genetic code randomisation analysis based on 100, 000 different codes tested for the protein fold prediction quality indicated that dipeptides represent basic protein units with respect to the genetic code defining of the secondary protein structure.

amino acid; binary algorithm; classification trees; DNA; genetic code; machine learning; nucleotide; protein folding; RNA

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