VirulentPred: a SVM based prediction method for virulent proteins in bacterial pathogens.
VirulentPred: a SVM based prediction method for virulent proteins in bacterial pathogens.
复制标题
有毒剂：一种基于SVM的细菌病原体中毒素的预测方法。
DOI：
10.1186/1471-2105-9-62
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发表时间：
2008-01-28
影响因子：
3
通讯作者：
Gupta, Dinesh
中科院分区：
文献类型：
--
作者：
Garg, Aarti;Gupta, Dinesh
预测细菌毒力蛋白序列对于鉴定和表征新的毒力相关因子、发现针对致病性不可或缺的蛋白质的新的药物/疫苗靶点以及理解病原体中复杂的毒力机制具有重要意义。本研究提出了一种基于双层级联支持向量机（SVM）的细菌毒力蛋白预测方法。第一层SVM分类器用不同的个体蛋白质序列特征如氨基酸组成、二肽组成（第i和第i+1个氨基酸残基的可能配对的出现）、高阶二肽组成（第i和第i+2个残基的配对）和位置特异性迭代BLAST（PSI-BLAST）生成的位置特异性评分矩阵（PSSM）进行训练和优化。此外，还开发了一个基于相似性搜索的模块，使用的数据集的毒力和无毒力的蛋白作为BLAST数据库。在本研究中，五重交叉验证技术用于评估各种预测策略。将第一层的结果（SVM分数和PSI-BLAST结果）级联到第二层SVM分类器以训练和生成最终分类器。级联SVM分类器能够实现81.8%的准确性，覆盖86%的面积在受试者操作特征（ROC）图，优于基于单个或多个序列特征的第一层SVM分类器。VirulentPred是一种基于支持向量机的预测细菌毒力蛋白序列的方法，可用于蛋白质组中毒力蛋白的筛选。与实验验证的毒力蛋白一起，几个推定的、未注释的和假设的蛋白质序列已经通过预测方法被预测为高分毒力蛋白。VirulentPred是一个可免费访问的万维网服务器- VirulentPred，网址为http://bioinfo.icgeb.res.in/virulent/。
Prediction of bacterial virulent protein sequences has implications for identification and characterization of novel virulence-associated factors, finding novel drug/vaccine targets against proteins indispensable to pathogenicity, and understanding the complex virulence mechanism in pathogens. In the present study we propose a bacterial virulent protein prediction method based on bi-layer cascade Support Vector Machine (SVM). The first layer SVM classifiers were trained and optimized with different individual protein sequence features like amino acid composition, dipeptide composition (occurrences of the possible pairs of ith and i+1th amino acid residues), higher order dipeptide composition (pairs of ith and i+2nd residues) and Position Specific Iterated BLAST (PSI-BLAST) generated Position Specific Scoring Matrices (PSSM). In addition, a similarity-search based module was also developed using a dataset of virulent and non-virulent proteins as BLAST database. A five-fold cross-validation technique was used for the evaluation of various prediction strategies in this study. The results from the first layer (SVM scores and PSI-BLAST result) were cascaded to the second layer SVM classifier to train and generate the final classifier. The cascade SVM classifier was able to accomplish an accuracy of 81.8%, covering 86% area in the Receiver Operator Characteristic (ROC) plot, better than that of either of the layer one SVM classifiers based on single or multiple sequence features. VirulentPred is a SVM based method to predict bacterial virulent proteins sequences, which can be used to screen virulent proteins in proteomes. Together with experimentally verified virulent proteins, several putative, non annotated and hypothetical protein sequences have been predicted to be high scoring virulent proteins by the prediction method. VirulentPred is available as a freely accessible World Wide Web server – VirulentPred, at http://bioinfo.icgeb.res.in/virulent/.
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影响因子：
3
作者：
Hastings, IM;Paget-McNicol, S;Saul, A
通讯作者：
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