Prediction of antigenic epitopes on protein surfaces by consensus scoring.
Prediction of antigenic epitopes on protein surfaces by consensus scoring.
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DOI：
10.1186/1471-2105-10-302
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发表时间：
2009-09-22
影响因子：
3
通讯作者：
Zacharias M
中科院分区：
文献类型：
--
作者：
Liang S;Zheng D;Zhang C;Zacharias M
蛋白质表面抗原表位的预测对于疫苗设计具有重要意义。现有的大多数表位预测方法都是基于蛋白质序列来预测连续的线性表位。只有少数基于结构的表位预测算法是可用的，他们还没有表现出令人满意的性能。我们提出了一种新的抗原表位预测方法，该方法使用ConsEnsus评分（EPCES）从六个不同的评分功能-残基表位倾向，保守性评分，侧链能量评分，接触数，表面平面性评分和二级结构组成。应用于来自独立测试集的无界抗原结构，EPCES能够预测抗原表位，灵敏度为47.8%，特异性为69.5%，AUC值为0.632。该方法的性能在统计学上与其他已发表的方法相似。与现有算法的最佳结果相比，EPCES的AUC值略高约0.034。我们的工作表明，多个特征的共识评分比任何单个术语都具有更好的性能。成功的预测也是由于基于原子溶剂可及性的残基表位倾向的新评分。
Prediction of antigenic epitopes on protein surfaces is important for vaccine design. Most existing epitope prediction methods focus on protein sequences to predict continuous epitopes linear in sequence. Only a few structure-based epitope prediction algorithms are available and they have not yet shown satisfying performance. We present a new antigen Epitope Prediction method, which uses ConsEnsus Scoring (EPCES) from six different scoring functions - residue epitope propensity, conservation score, side-chain energy score, contact number, surface planarity score, and secondary structure composition. Applied to unbounded antigen structures from an independent test set, EPCES was able to predict antigenic eptitopes with 47.8% sensitivity, 69.5% specificity and an AUC value of 0.632. The performance of the method is statistically similar to other published methods. The AUC value of EPCES is slightly higher compared to the best results of existing algorithms by about 0.034. Our work shows consensus scoring of multiple features has a better performance than any single term. The successful prediction is also due to the new score of residue epitope propensity based on atomic solvent accessibility.
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DOI：
10.1073/pnas.89.22.10915
发表时间：
1992-11-15
影响因子：
11.1
作者：
HENIKOFF, S;HENIKOFF, JG
通讯作者：
HENIKOFF, JG
影响因子：
1.6
作者：
Caoili, Salvador Eugenio C.
通讯作者：
Caoili, Salvador Eugenio C.
影响因子：
2.7
作者：
Rapberger, Ronald;Lukas, Arno;Mayer, Bernd
通讯作者：
Mayer, Bernd
影响因子：
2.7
作者：
El-Manzalawy, Yasser;Dobbs, Drena;Honavar, Vasant
通讯作者：
Honavar, Vasant
DOI：
10.1073/pnas.78.6.3824
发表时间：
1981-01-01
期刊：
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA-BIOLOGICAL SCIENCES
影响因子：
--
作者：
HOPP, TP;WOODS, KR
通讯作者：
WOODS, KR