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首页> 外文期刊>American Journal of Bioinformatics Research >Prediction of Antigenic Epitope Patches on Protein Surface Using Antigen Structure Information and Support Vector Machine
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Prediction of Antigenic Epitope Patches on Protein Surface Using Antigen Structure Information and Support Vector Machine

机译：利用抗原结构信息和支持向量机预测蛋白质表面的抗原决定簇

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Identification of antigen-antibody interacting sites is an important task for vaccine design, and hence reliable computer based prediction methods are highly desirable. The prediction performances of the current existing methods to predict the conformational B-cell epitope residues are still not satisfying and remain far from ideal. This is a new approach in the area of vaccine development to predict the antigenic surface patches that hold the majority number of epitope residues in the surface of the antigen protein structure. The proposed method is a support vector machine based model to predict the epitope patches in the antigen structures by combining the accessible surface area and B-factor structural features. The Predictions are made for the known structures of benchmark dataset after removing antigens sequence redundancy where no two antigen sequences have more than 40% sequence identity. The predictions are successful for 70% of the antigen structure chains of the benchmark dataset. We compared the prediction performance of our model with a protein – protein interaction prediction server “Sharp2” using the same antigen structures of the benchmark dataset and observed that our model outperforms on Sharp2 by more than 40% accuracy. This paper demonstrates that the identification of the antigenic determinant sites in the protein surface using the antigen structural information outperforms the traditional protein-protein interaction algorithms to predict the interacting sites in the antigen protein surface. It provides a new approach for the scientists to only use the predicted antigenic epitope surface patch from the target antigen structure in vaccine development rather than using the predicted epitope residues. A web server “PatchTope” has been developed for predicting antigenic epitope surface patches on an antigen protein structure surface and is available at http://www.fci.cu.edu.eg:8080/PatchTope/.
机译：鉴定抗原-抗体相互作用位点是疫苗设计的重要任务，因此非常需要可靠的基于计算机的预测方法。当前预测构象B细胞表位残基的现有方法的预测性能仍然不能令人满意，并且与理想情况相去甚远。这是疫苗开发领域中预测抗原表面蛋白的一种新方法，该抗原表面蛋白在抗原蛋白结构的表面上具有大多数表位残基。所提出的方法是一种基于支持向量机的模型，通过结合可及表面积和B因子结构特征来预测抗原结构中的抗原决定簇。在删除没有两个抗原序列具有超过40％序列同一性的抗原序列冗余后，对基准数据集的已知结构进行预测。对于基准数据集的70％抗原结构链，预测是成功的。我们使用基准数据集的相同抗原结构，将我们的模型与蛋白质-蛋白质相互作用预测服务器“ Sharp2”的预测性能进行了比较，并观察到我们的模型在Sharp2上的表现优于40％。本文证明了使用抗原结构信息鉴定蛋白质表面抗原决定簇的能力优于传统的蛋白质-蛋白质相互作用算法来预测抗原蛋白质表面的相互作用位点。它为科学家提供了一种新的方法，使其仅在疫苗开发中使用目标抗原结构中的预测抗原表位表面补丁，而不是使用预测表位残基。已经开发了用于预测抗原蛋白结构表面上的抗原表位表面补丁的网络服务器“ PatchTope”，并且可在http://www.fci.cu.edu.eg:8080/PatchTope/上获得。

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