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Disulfide Connectivity Prediction Based on Modelled Protein 3D Structural Information and Random Forest Regression by Dong-Jun Yu; Yang Li; Jun Hu; Xi-Bei Yang; Jing-Yu Yang; Hong-Bin Shen is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.

What is Disulfide Connectivity Prediction Based on Modelled Protein 3D Structural Information and Random Forest Regression about?

Disulfide connectivity is an important protein structural characteristic. Accurately predicting disulfide connectivity solely from protein sequence helps to improve the intrinsic understanding of protein structure and function, especially in the post-genome era where large volume of sequenced proteins without being functional annotated is quickly accumulated. In this study, a new feature extracted from the predicted protein 3D structural information is proposed and integrated with traditional features to form discriminative features. Based on the extracted features, a random forest regression model is performed to predict protein disulfide connectivity. We compare the proposed method with popular existing predictors by performing both cross-validation and independent validation tests on benchmark datasets. The experimental results demonstrate the superiority of the proposed method over existing predictors. We believe the superiority of the proposed method benefits from both the good discriminative capability of the newly developed features and the powerful modelling capability of the random forest. The web server implementation, called TargetDisulfide, and the benchmark datasets ar

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Author

Dong-Jun Yu; Yang Li; Jun Hu; Xi-Bei Yang; Jing-Yu Yang; Hong-Bin Shen

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Published

2015

Language

EN

Field

Biochemistry, Genetics and Molecular Biology (Life Sciences)