Predicting solvent accessibility: Higher accuracy using Bayesian statistics and optimized residue substitution classes

Thompson, Michael J.; Goldstein, Richard A.

doi:10.1002/(sici)1097-0134(199605)25:1<38::aid-prot4>3.0.co;2-g

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Cited by 50 publications

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Better 1D predictions by experts with machines

Rost¹

1997

Proteins

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Accuracy of predicting protein secondary structure and solvent accessibility has been improved significantly by using evolutionary information contained in multiple sequence alignments. For the second Asilomar meeting, predictions were made automatically for all targets using the publicly available prediction service PredictProtein. Additionally, a semiautomatic procedure for generating more informative alignments was used in combination with the PHD prediction methods. Results confirmed the estimates for prediction accuracy. Furthermore, the more informative alignments yielded better predictions. The fairly accurate predictions of 1D structure were successfully used by various groups for the Asilomar meeting as first step toward predicting higher dimensions of protein structure.

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