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首页> 外文期刊>IEEE/ACM transactions on computational biology and bioinformatics >Prediction the Substrate Specificities of Membrane Transport Proteins Based on Support Vector Machine and Hybrid Features
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Prediction the Substrate Specificities of Membrane Transport Proteins Based on Support Vector Machine and Hybrid Features

机译：基于支持向量机和混合特征的膜转运蛋白底物特异性预测

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摘要

Membrane transport proteins and their substrate specificities play crucial roles in a variety of cellular functions. Identifying the substrate specificities of membrane transport proteins is closely related to the protein-target interaction prediction, drug design, membrane recruitment, and dysregulation analysis. However, experimental methods to this aim are time consuming, labor intensive, and costly. Therefore, we proposed a novel method basing on support vector machine (SVM) to predict substrate specificities of membrane transport proteins by integrating features from position-specific score matrix (PSSM), PROFEAT, and Gene Ontology (GO). Finally, jackknife cross-validation tests were adopted on a benchmark and independent datasets to measure the performance of the proposed method. The overall accuracy of 96.16 and 80.45 percent were obtained for two datasets, which are higher (from 2.12 to 20.44 percent) than that by the state-of-the-art tool. Comparison results indicate that the proposed model is more reliable and efficient for accurate prediction the substrate specificities of membrane transport proteins.
机译：膜转运蛋白及其底物特异性在多种细胞功能中起着至关重要的作用。鉴定膜转运蛋白的底物特异性与蛋白-靶标相互作用预测，药物设计，膜募集和失调分析密切相关。然而，为此目的的实验方法是费时，费力的并且昂贵的。因此，我们提出了一种基于支持向量机（SVM）的新方法，通过整合位置特异性得分矩阵（PSSM），PROFEAT和Gene Ontology（GO）的特征来预测膜转运蛋白的底物特异性。最后，在基准和独立数据集上采用折刀交叉验证测试来测量所提出方法的性能。两个数据集的总体准确度为96.16％和80.45％，比最新技术的数据集更高（从2.12％到20.44％）。比较结果表明，所提出的模型对于准确预测膜转运蛋白的底物特异性更为可靠和有效。

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