呼吸道合胞病毒(RSV)是一种常见的呼吸道病原体,感染人的肺部和呼吸道,常引起类似普通感冒的症状。疫苗接种是管理病毒爆发的最有效策略。目前,广泛的努力集中在开发RSV疫苗上。传统的疫苗设计通常涉及使用减毒形式的病原体来引发免疫应答。相比之下,基于肽的疫苗(PBV)旨在鉴定和化学合成特异性免疫显性肽(IPs),称为T细胞表位(TCE),诱导靶向免疫反应。尽管它们具有增强疫苗安全性和免疫原性的潜力,PBV受到的关注相对较少。通过传统的湿实验室实验确定PBV设计的IP具有挑战性,昂贵的,而且耗时。机器学习(ML)技术提供了一种有前途的替代方案,准确预测TCE,并显著降低疫苗开发的时间和成本。本研究提出了通过两种分类方法的排列和组合创建的八种混合ML预测模型的开发和评估,两种特征加权技术,和两种特征选择算法,所有这些都旨在预测RSV的TCE。使用从细菌和病毒生物信息学资源中心(BV-BRC)库获得的实验确定的TCE和非TCE序列来训练模型。由XGBoost(XGB)分类器组成的混合模型,卡方(ChST)加权技术,后向搜索(BST)作为最佳特征选择算法(ChST-BST-XGB)被确定为最佳模型,实现准确性,灵敏度,特异性,F1得分,AUC,精度,MCC为97.10%,分别为0.98、0.97、0.98、0.99、0.99和0.96。此外,进行K折交叉验证(KFCV)以确保模型的可靠性,并且对于ChST-BST-XGB模型记录的平均准确度为97.21%。结果表明,混合XGBoost模型始终优于其他混合方法。所提出的模型预测的表位可以作为RSV的有希望的疫苗候选物,接受体外和体内科学评估。该模型可以帮助科学界加快筛选活跃的TCE候选RSV,最终节省了疫苗开发的时间和资源。
Respiratory syncytial virus (RSV) is a common respiratory pathogen that infects the human lungs and respiratory tract, often causing symptoms similar to the common cold. Vaccination is the most effective strategy for managing viral outbreaks. Currently, extensive efforts are focused on developing a vaccine for RSV. Traditional vaccine design typically involves using an attenuated form of the pathogen to elicit an immune response. In contrast, peptide-based vaccines (PBVs) aim to identify and chemically synthesize specific immunodominant peptides (IPs), known as T-cell epitopes (TCEs), to induce a targeted immune response. Despite their potential for enhancing vaccine safety and immunogenicity, PBVs have received comparatively less attention. Identifying IPs for PBV design through conventional wet-lab experiments is challenging, costly, and time-consuming. Machine learning (ML) techniques offer a promising alternative, accurately predicting TCEs and significantly reducing the time and cost of vaccine development. This study proposes the development and evaluation of eight hybrid ML predictive models created through the permutations and combinations of two classification methods, two feature weighting techniques, and two feature selection algorithms, all aimed at predicting the TCEs of RSV. The models were trained using the experimentally determined TCEs and non-TCE sequences acquired from the Bacterial and Viral Bioinformatics Resource Center (BV-BRC) repository. The hybrid model composed of the XGBoost (XGB) classifier, chi-squared (ChST) weighting technique, and backward search (BST) as the optimal feature selection algorithm (ChST-BST-XGB) was identified as the best model, achieving an accuracy, sensitivity, specificity, F1 score, AUC, precision, and MCC of 97.10%, 0.98, 0.97, 0.98, 0.99, 0.99, and 0.96, respectively. Additionally, K-fold cross-validation (KFCV) was performed to ensure the model\'s reliability and an average accuracy of 97.21% was recorded for the ChST-BST-XGB model. The results indicate that the hybrid XGBoost model consistently outperforms other hybrid approaches. The epitopes predicted by the proposed model may serve as promising vaccine candidates for RSV, subject to in vitro and in vivo scientific assessments. This model can assist the scientific community in expediting the screening of active TCE candidates for RSV, ultimately saving time and resources in vaccine development.