首页> 外文期刊>Journal of Bioinformatics and Computational Biology >PhD7FASTER: PREDICTING CLONES PROPAGATING FASTER FROM THE Ph.D.-7 PHAGE DISPLAY PEPTIDE LIBRARY
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PhD7FASTER: PREDICTING CLONES PROPAGATING FASTER FROM THE Ph.D.-7 PHAGE DISPLAY PEPTIDE LIBRARY

机译:PhD7FASTER:从Ph.D.7噬菌体展示肽库预测克隆的传播更快

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Phage display can rapidly discover peptides binding to any given target; thus, it has been widely used in basic and applied research. Each round of panning consists of two basic processes: Selection and amplification. However, recent studies have showed that the amplification step would decrease the diversity of phage display libraries due to different propagation capacity of phage clones. This may induce phages with growth advantage rather than specific affinity to appear in the final experimental results. The peptides displayed by such phages are termed as propagation-related target-unrelated peptides (PrTUPs). They would mislead further analysis and research if not removed. In this paper, we describe PhD7Faster, an ensemble predictor based on support vector machine (SVM) for predicting clones with growth advantage from the Ph.D.-7 phage display peptide library. By using reduced dipeptide composition (ReDPC) as features, an accuracy (Acc) of 79.67% and a Matthews correlation coefficient (MCC) of 0.595 were achieved in 5-fold cross-validation. In addition, the SVM-based model was demonstrated to perform better than several representative machine learning algorithms. We anticipate that PhD7Faster can assist biologists to exclude potential PrTUPs and accelerate the finding of specific binders from the popular Ph.D.-7 library. The web server of PhD7Faster can be freely accessed at http://immunet.cn/sarotup/cgi-bin/PhD7Faster.pl.
机译:噬菌体展示可以迅速发现与任何给定靶标结合的肽;因此,它已被广泛应用于基础研究和应用研究。每轮平移包含两个基本过程:选择和放大。然而,最近的研究表明,由于噬菌体克隆的不同繁殖能力,扩增步骤将降低噬菌体展示文库的多样性。这可能诱导具有生长优势而不是特定亲和力的噬菌体出现在最终实验结果中。这种噬菌体展示的肽被称为与繁殖相关的靶标无关肽(PrTUPs)。如果不删除,它们将误导进一步的分析和研究。在本文中,我们描述了PhD7Faster,这是一种基于支持向量机(SVM)的整体预测器,用于从Ph.D.-7噬菌体展示肽库预测具有生长优势的克隆。通过使用降低的二肽组成(ReDPC)作为特征,在5倍交叉验证中,准确度(Acc)为79.67%,马修斯相关系数(MCC)为0.595。此外,还证明了基于SVM的模型比几种代表性的机器学习算法具有更好的性能。我们预计PhD7Faster可以帮助生物学家排除潜在的PrTUP,并加快从流行的Ph.D.-7库中找到特定结合物的速度。可以从http://immunet.cn/sarotup/cgi-bin/PhD7Faster.pl免费访问PhD7Faster的Web服务器。

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