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• W2047722011 abstract "We introduce a novel Bayesian probabilistic method for predicting the solvent accessibilities of amino acid residues in globular proteins. Using single sequence data, this method achieves prediction accuracies higher than previously published methods. Substantially improved predictions—comparable to the highest accuracies reported in the literature to date—are obtained by representing alignments of the example proteins and their homologs as strings of residue substitution classes, depending on the side chain types observed at each alignment position. These results demonstrate the applicability of this relatively simple Bayesian approach to structure prediction and illustrate the utility of the classification methodology previously developed to extract information from aligned sets of structurally related proteins. © 1996 Wiley-Liss, Inc." @default.
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• W2047722011 date "1996-05-01" @default.
• W2047722011 modified "2023-10-17" @default.
• W2047722011 title "Predicting solvent accessibility: Higher accuracy using Bayesian statistics and optimized residue substitution classes" @default.
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• W2047722011 doi "https://doi.org/10.1002/(sici)1097-0134(199605)25:1<38::aid-prot4>3.0.co;2-g" @default.
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