用深度学习预测新冠病毒抗体结合位点，加速疫苗设计
Deep Neural Network-Based Prediction of B-Cell Epitopes for SARS-CoV and SARS-CoV-2: Enhancing Vaccine Design through Machine Learning
- 构建深度神经网络模型，融合蛋白特性预测B细胞表位
- 整体预测准确率达82%，对阳性样本识别仍有提升空间
- 适合疫苗研发人员快速筛选候选抗原靶点
准确预测B细胞表位对于指导传染病疫苗开发至关重要，尤其针对SARS和新冠。本研究利用深度神经网络（DNN）模型，基于包含关键蛋白与肽段特征的数据集，预测SARS-CoV与SARS-CoV-2的B细胞表位。传统序列方法在处理大规模复杂数据时表现受限，而深度学习显著提升了预测精度。模型采用丢弃法（dropout）与早停（early stopping）等正则化技术增强泛化能力，并分析了等电点、芳香性等影响表位识别的关键特征。结果显示，对新冠阴性和阳性样本的整体预测准确率为82%，但阳性样本检测仍需优化。研究证明深度学习在表位定位中的可行性，表明该方法可提升新兴病原体疫苗设计的速度与精度。未来工作可引入结构数据及更多病毒株以进一步优化预测性能。
原文摘要 · Abstract (English)
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.
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