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首页> 外文期刊>Computational Biology and Bioinformatics, IEEE/ACM Transactions on >Disulfide Connectivity Prediction Based on Modelled Protein 3D Structural Information and Random Forest Regression
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Disulfide Connectivity Prediction Based on Modelled Protein 3D Structural Information and Random Forest Regression

机译:基于建模蛋白质3D结构信息和随机森林回归的二硫键连通性预测

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摘要

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 are freely available at: http://csbio.njust.edu.cn/bioinf/TargetDisulfide for academic use.
机译:二硫键连接性是重要的蛋白质结构特征。仅从蛋白质序列准确预测二硫键连接性有助于改善对蛋白质结构和功能的内在理解,尤其是在后基因组时代,在该时代后代中大量积累了没有功能注释的测序蛋白质。在这项研究中,提出了从预测的蛋白质3D结构信息中提取的新特征,并将其与传统特征集成以形成判别特征。基于提取的特征,执行随机森林回归模型以预测蛋白质二硫键的连通性。通过对基准数据集执行交叉验证和独立验证测试,我们将提出的方法与现有的流行预测变量进行了比较。实验结果证明了该方法优于现有预测器的优越性。我们认为,所提出方法的优越性得益于新开发功能的良好判别能力和随机森林的强大建模能力。 Web服务器实现(称为TargetDisulfide)和基准数据集可从以下网址免费获得:http://csbio.njust.edu.cn/bioinf/TargetDisulfide,以供学术使用。

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