The accurate prediction of B-cell epitopes is critical for guiding vaccine development against infectious diseases, including SARS and COVID-19. This study explores the use of a deep neural network (DNN) model to predict B-cell epitopes for SARS-CoVandSARS-CoV-2,leveraging a dataset that incorporates essential protein and peptide features. Traditional sequence-based methods often struggle with large, complex datasets, but deep learning offers promising improvements in predictive accuracy. Our model employs regularization techniques, such as dropout and early stopping, to enhance generalization, while also analyzing key features, including isoelectric point and aromaticity, that influence epitope recognition. Results indicate an overall accuracy of 82% in predicting COVID-19 negative and positive cases, with room for improvement in detecting positive samples. This research demonstrates the applicability of deep learning in epitope mapping, suggesting that such approaches can enhance the speed and precision of vaccine design for emerging pathogens. Future work could incorporate structural data and diverse viral strains to further refine prediction capabilities.

翻译：B细胞表位的准确预测对于指导包括SARS和COVID-19在内的传染病的疫苗开发至关重要。本研究探索了使用深度神经网络模型来预测SARS-CoV和SARS-CoV-2的B细胞表位，所利用的数据集包含了关键的蛋白质和肽特征。传统的基于序列的方法在处理大型复杂数据集时常常面临困难，而深度学习在预测准确性方面提供了有前景的改进。我们的模型采用了正则化技术，如dropout和早停法，以增强泛化能力，同时还分析了影响表位识别的关键特征，包括等电点和芳香性。结果表明，在预测COVID-19阴性和阳性病例方面总体准确率达到82%，在检测阳性样本方面仍有改进空间。这项研究证明了深度学习在表位定位中的适用性，表明此类方法可以提高针对新发病原体的疫苗设计的速度和精确度。未来的工作可以整合结构数据和多样化的病毒株，以进一步完善预测能力。