An assessment on epitope prediction methods for protozoa genomes.
An assessment on epitope prediction methods for protozoa genomes.
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DOI：
10.1186/1471-2105-13-309
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发表时间：
2012-11-21
影响因子：
3
通讯作者：
Ruiz JC
中科院分区：
文献类型：
--
作者：
Resende DM;Rezende AM;Oliveira NJ;Batista IC;Corrêa-Oliveira R;Reis AB;Ruiz JC
使用计算方法进行表位预测是疫苗开发中最有前景的方法之一。减少时间、成本和获得完全测序的基因组是关于反向疫苗接种的关键和高度激励因素。利什曼原虫分布广泛，是利什曼病的病原体。目前，还没有针对这种病原体的有效疫苗，药物治疗毒性很大。缺乏足够大的实验验证寄生虫表位的数据集是一个严重的限制，特别是对锥虫基因组来说。在这项工作中，我们重点介绍了几种算法的预测性能，这些算法通过开发MySQL数据库进行评估，目的是：a)通过AUC(曲线下面积)性能和使用混淆矩阵的阈值依赖方法来评估CD8+T细胞表位、B细胞表位和亚细胞定位的单个算法及其组合的预测性能；b)整合来自实验验证和电子预测表位的数据；以及c)整合亚细胞定位预测和实验数据。NetCTL、NetMHC、BepiPred、BCPred12和AAP12算法用于电子表位预测，Wolf PSORT、Sigcleave和TargetP算法用于针对锥虫基因组的电子亚细胞定位预测。开发了一种数据库驱动的表位预测方法，其内置的函数能够：a)消除实验数据冗余；b)解析算法预测并存储实验验证和预测数据；c)评估算法性能。结果表明，当考虑组合预测时，取得了较好的效果。对于B细胞表位预测者尤其如此，AAP12和BCPred12的联合预测达到了0.77的AUC值。对于T CD8+表位预测值，NetCTL和NetMHC的联合预测值达到0.64。最后，在亚细胞定位预测方面，Sigcleave、TargetP和Wolf PSORT的组合预测效果最好。我们的研究表明，B细胞表位预测因子的组合是预测原生动物寄生虫蛋白表位的最佳工具。在亚细胞定位方面，当三种算法预测相结合时，结果最好。开发的管道可根据作者的要求提供。
Epitope prediction using computational methods represents one of the most promising approaches to vaccine development. Reduction of time, cost, and the availability of completely sequenced genomes are key points and highly motivating regarding the use of reverse vaccinology. Parasites of genus Leishmania are widely spread and they are the etiologic agents of leishmaniasis. Currently, there is no efficient vaccine against this pathogen and the drug treatment is highly toxic. The lack of sufficiently large datasets of experimentally validated parasites epitopes represents a serious limitation, especially for trypanomatids genomes. In this work we highlight the predictive performances of several algorithms that were evaluated through the development of a MySQL database built with the purpose of: a) evaluating individual algorithms prediction performances and their combination for CD8+ T cell epitopes, B-cell epitopes and subcellular localization by means of AUC (Area Under Curve) performance and a threshold dependent method that employs a confusion matrix; b) integrating data from experimentally validated and in silico predicted epitopes; and c) integrating the subcellular localization predictions and experimental data. NetCTL, NetMHC, BepiPred, BCPred12, and AAP12 algorithms were used for in silico epitope prediction and WoLF PSORT, Sigcleave and TargetP for in silico subcellular localization prediction against trypanosomatid genomes. A database-driven epitope prediction method was developed with built-in functions that were capable of: a) removing experimental data redundancy; b) parsing algorithms predictions and storage experimental validated and predict data; and c) evaluating algorithm performances. Results show that a better performance is achieved when the combined prediction is considered. This is particularly true for B cell epitope predictors, where the combined prediction of AAP12 and BCPred12 reached an AUC value of 0.77. For T CD8+ epitope predictors, the combined prediction of NetCTL and NetMHC reached an AUC value of 0.64. Finally, regarding the subcellular localization prediction, the best performance is achieved when the combined prediction of Sigcleave, TargetP and WoLF PSORT is used. Our study indicates that the combination of B cells epitope predictors is the best tool for predicting epitopes on protozoan parasites proteins. Regarding subcellular localization, the best result was obtained when the three algorithms predictions were combined. The developed pipeline is available upon request to authors.
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影响因子：
4.3
作者：
Korber B;LaBute M;Yusim K
通讯作者：
Yusim K
影响因子：
5.4
作者：
Larsen, MV;Lundegaard, C;Nielsen, M
通讯作者：
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Wolf Psort：蛋白质定位预测因子。
影响因子：
14.9
作者：
Horton, Paul;Park, Keun-Joon;Obayashi, Takeshi;Fujita, Naoya;Harada, Hajime;Adams-Collier, C J;Nakai, Kenta
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Nakai, Kenta
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2.7
作者：
El-Manzalawy, Yasser;Dobbs, Drena;Honavar, Vasant
通讯作者：
Honavar, Vasant
影响因子：
5.8
作者：
Nielsen, M;Lundegaard, C;Lund, O
通讯作者：
Lund, O