An antigen is a protein capable of triggering an effective immune system response. Protective antigens are the ones that can invoke specific and enhanced adaptive immune response to subsequent exposure to the specific pathogen or related organisms. Such proteins are therefore of immense importance in vaccine preparation and drug design. However, the laboratory experiments to isolate and identify antigens from a microbial pathogen are expensive, time consuming and often unsuccessful. This is why Reverse Vaccinology has become the modern trend of vaccine search, where computational methods are first applied to predict protective antigens or their determinants, known as epitopes. In this paper, we propose a novel, accurate computational model to identify protective antigens efficiently. Our model extracts features directly from the protein sequences, without any dependence on functional domain or structural information. After relevant features are extracted, we have used Random Forest algorithm to rank the features. Then Recursive Feature Elimination (RFE) and minimum redundancy maximum relevance (mRMR) criterion were applied to extract an optimal set of features. The learning model was trained using Random Forest algorithm. Named as Antigenic, our proposed model demonstrates superior performance compared to the state-of-the-art predictors on a benchmark dataset. Antigenic achieves accuracy, sensitivity and specificity values of 78.04%, 78.99% and 77.08% in 10-fold cross-validation testing respectively. In jackknife cross-validation, the corresponding scores are 80.03%, 80.90% and 79.16% respectively. The source code of Antigenic, along with relevant dataset and detailed experimental results, can be found at https://github.com/srautonu/AntigenPredictor. A publicly accessible web interface has also been established at: http://antigenic.research.buet.ac.bd.

抗原是一种能够触发有效免疫系统反应的蛋白质。保护性抗原是可以对随后暴露于特定病原体或相关生物体引起特异性和增强的适应性免疫反应的那些。因此，这些蛋白质在疫苗制备和药物设计中具有极其重要的意义。然而，从微生物病原体分离和鉴定抗原的实验室实验是昂贵，费时的并且通常是不成功的。这就是为什么反向疫苗学已成为疫苗搜索的现代趋势的原因，在这种趋势中，首先应用计算方法来预测保护性抗原或其决定簇（称为表位）。在本文中，我们提出了一种新颖，准确的计算模型来有效地识别保护性抗原。我们的模型直接从蛋白质序列中提取特征，不依赖功能域或结构信息。提取相关特征后，我们使用随机森林算法对特征进行排序。然后应用递归特征消除（RFE）和最小冗余最大相关性（mRMR）标准来提取一组最佳特征。使用随机森林算法训练了学习模型。命名为与标准数据集上的最新预测指标相比，我们提出的模型具有更高的抗原性。在10倍交叉验证测试中，抗原分别达到78.04％，78.99％和77.08％的准确性，敏感性和特异性值。在折刀交叉验证中，相应得分分别为80.03％，80.90％和79.16％。可以在https://github.com/srautonu/AntigenPredictor上找到Antigenic的源代码以及相关的数据集和详细的实验结果。还可以通过以下网址建立可公开访问的Web界面：http：//antigenic.research.buet.ac.bd。