alexa Accurate prediction of protein secondary structural content.
Biomedical Sciences

Biomedical Sciences

International Journal of Biomedical Data Mining

Author(s): Lin Z, Pan XM

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Abstract An improved multiple linear regression (MLR) method is proposed to predict a protein's secondary structural content based on its primary sequence. The amino acid composition, the autocorrelation function, and the interaction function of side-chain mass derived from the primary sequence are taken into account. The average absolute errors of prediction over 704 unrelated proteins with the jackknife test are 0.088, 0.081, and 0.059 with standard deviations 0.073, 0.066, and 0.055 for alpha-helix, beta-sheet, and coil, respectively. That the sum of predicted secondary structure content should be close to 1.0 was introduced as a criterion to evaluate whether the prediction is acceptable. While only the predictions with the sum of predicted secondary structure content between 0.99 and 1.01 are accepted (about 11\% of all proteins), the absolute errors are 0.058 for alpha-helix, 0.054 for beta-sheet, and 0.045 for coil.
This article was published in J Protein Chem and referenced in International Journal of Biomedical Data Mining

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