Table3_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）是一类具有高度多样性的病毒家族，包含多种致病型别，可感染广泛的宿主类群。阐明腺病毒感染的发病机制，不仅具有临床指导价值，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，以病毒配体与宿主细胞表面受体的结合为必要前提，从这一角度而言，该结合过程是判断腺病毒感染是否可能发生的核心判定标准。病毒与对应宿主之间的相互作用属于一类特殊的蛋白质-蛋白质相互作用（protein-protein interaction, PPI），目前已有包括高通量技术在内的多种实验手段被用于探索此类相互作用。由此，病毒-宿主相互作用相关的数据不断累积，其中相当一部分已在公开的生物信息学资源中发布。然而，目前尚未存在能够整合并解读现有数据、从而得出简洁判定结论（例如感染是否会发生）的计算模型。本研究以腺病毒的细胞入侵作为感染性的决定性参数，开发了一种基于机器学习（machine learning）、更具体为支持向量机（support vector machine, SVM）的方法，用于预测特定宿主中是否可能发生腺病毒感染。为此，我们采集了已知腺病毒受体的序列数据，鉴定了腺病毒配体及其对应宿主物种的集合，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工具完成了相互作用预测，并使用搭载径向基函数（radial basis function, RBF）核的支持向量机构建了腺病毒感染预测模型，该模型的整体灵敏度、特异性和曲线下面积（area under the curve, AUC）分别为0.88 ± 0.011、0.83 ± 0.064和0.86 ± 0.030。ML-AdVInfect是首款能够有效筛选感染能力并预测跨宿主转移的同类预测工具。我们预期，本研究开发的ML-AdVInfect方法可被推广应用于其他病毒感染的预测工作。