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• W2082569982 abstract "The difficulties in predicting disulfide connectivity from protein sequences lie in the nonlocal properties of the disulfide bridges that involve cysteine pairs at large sequence separation. Though some progress has been recently made in the prediction of disulfide connectivity, the current methods predict less than half of the disulfide patterns for the data set sharing less than 30% sequence identity. In this report, we use the support vector machines based on sequence features such as the coupling between the local sequence environments of cysteine pair, the cysteines sequence separations, and the global sequence descriptor, such as amino acid content. Our approach is able to predict 55% of the disulfide patterns of proteins with two to five disulfide bridges, which is 11–26% higher than other methods in the literature. Proteins 2005. © 2005 Wiley-Liss, Inc." @default.
• W2082569982 created "2016-06-24" @default.
• W2082569982 creator A5041730336 @default.
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• W2082569982 date "2005-09-16" @default.
• W2082569982 modified "2023-09-27" @default.
• W2082569982 title "Prediction of disulfide connectivity from protein sequences" @default.
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• W2082569982 doi "https://doi.org/10.1002/prot.20627" @default.
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