Table5_ML-AdVInfect: A Machine-Learning Based Adenoviral Infection Predictor.XLSX

Adenoviruses (AdVs) constitute a diverse family with many pathogenic types that infect a broad range of hosts. Understanding the pathogenesis of adenoviral infections is not only clinically relevant but also important to elucidate the potential use of AdVs as vectors in therapeutic applications. For an adenoviral infection to occur, attachment of the viral ligand to a cellular receptor on the host organism is a prerequisite and, in this sense, it is a criterion to decide whether an adenoviral infection can potentially happen. The interaction between any virus and its corresponding host organism is a specific kind of protein-protein interaction (PPI) and several experimental techniques, including high-throughput methods are being used in exploring such interactions. As a result, there has been accumulating data on virus-host interactions including a significant portion reported at publicly available bioinformatics resources. There is not, however, a computational model to integrate and interpret the existing data to draw out concise decisions, such as whether an infection happens or not. In this study, accepting the cellular entry of AdV as a decisive parameter for infectivity, we have developed a machine learning, more precisely support vector machine (SVM), based methodology to predict whether adenoviral infection can take place in a given host. For this purpose, we used the sequence data of the known receptors of AdVs, we identified sets of adenoviral ligands and their respective host species, and eventually, we have constructed a comprehensive adenovirus–host interaction dataset. Then, we committed interaction predictions through publicly available virus-host PPI tools and constructed an AdV infection predictor model using SVM with RBF kernel, with the overall sensitivity, specificity, and AUC of 0.88 ± 0.011, 0.83 ± 0.064, and 0.86 ± 0.030, respectively. ML-AdVInfect is the first of its kind as an effective predictor to screen the infection capacity along with anticipating any cross-species shifts. We anticipate our approach led to ML-AdVInfect can be adapted in making predictions for other viral infections.

腺病毒（Adenoviruses, AdVs）是一类多样性丰富的病毒家族，包含多种致病型别，宿主感染谱广泛。阐明腺病毒感染的致病机制，不仅具备临床研究价值，同时对于解析腺病毒作为治疗载体的潜在应用场景亦具有重要意义。腺病毒感染的发生，以病毒配体结合宿主细胞表面受体为先决条件；从这一角度而言，该结合事件是判断腺病毒能否潜在引发感染的核心标准。任何病毒与其对应宿主之间的相互作用均属于一类特殊的蛋白质-蛋白质相互作用（protein-protein interaction, PPI），目前已有多种实验技术（包括高通量方法）被用于探索这类相互作用。因此，病毒-宿主相互作用相关数据持续积累，其中相当一部分已在公开生物信息学资源中发表。然而，目前尚无能够整合并解读现有数据，以得出简洁明确判断（如感染是否会发生）的计算模型。本研究以腺病毒的细胞侵入作为感染性的决定性参数，开发了一种基于机器学习（specifically 支持向量机（support vector machine, SVM））的预测方法，用于预判腺病毒能否在特定宿主中引发感染。为此，我们利用了已知腺病毒受体的序列数据，识别出多组腺病毒配体及其对应的宿主物种，最终构建了一套全面的腺病毒-宿主相互作用数据集。随后，我们通过公开可用的病毒-宿主PPI工具完成相互作用预测，并采用带有径向基函数（RBF）核的支持向量机构建了腺病毒感染预测模型，其整体灵敏度、特异性及曲线下面积（Area Under Curve, AUC）分别为0.88 ± 0.011、0.83 ± 0.064及0.86 ± 0.030。ML-AdVInfect是首款能够有效筛选感染能力并预测跨物种传播风险的腺病毒感染预测工具。我们预计，本研究开发的ML-AdVInfect方法可被适配用于其他病毒感染的预测任务。