Developing machine learning tools to investigate evolutionary trajectories in emerging viral infectious diseases

了解新出现的病毒病原体的进化轨迹对于监测和治疗干预措施的发展至关重要。该项目旨在促进我们对病毒适应其宿主时所遵循的突变途径的理解。为应对当前的SARS-CoV-2大流行而产生的与表型健康数据相关的前所未有的大量基因组和分子数据，以及来自流感和其他病毒的现有数据，使我们能够在分子水平上解决这个问题。使用计算技术的组合，包括结构生物信息学和机器学习/深度学习，将评估和预测参与病毒/宿主相互作用的蛋白质中突变的连续组合的影响。为了测试和评估计算工具，将采用一系列基于实验室的分子病毒学技术。包括体外病毒培养、克隆、定点诱变、反向遗传学、重组蛋白表达和纯化、假病毒构建、经典和中和试验。该项目的计算工具和见解可以应用于未来新兴的病毒病原体。在项目中开发的一系列备受追捧的技能将为未来在学术界和/或工业界的职业生涯奠定坚实的基础。

TBCUnderstanding the evolutionary trajectory of an emerging viral pathogen is crucial to both surveillance and the development of therapeutic interventions. This project aims to advance our understanding of the mutational pathways followed as a virus adapts to its host. The unprecedented and large quantity of genomic and molecular data linked to phenotypic health data that has been generated in response to the current Sars-CoV-2 pandemic, as well as existing data from influenza and other viruses, allows us to address this question at the molecular level. Using a combination of computational techniques, including structural bioinformatics and machine learning / deep learning, the effects of successive combinations of mutations in proteins involved in viral/host interactions will be evaluated and predicted. To test and appraise the computation tools a range of lab based molecular virology techniques will be employed. Including in vitro virus culture, cloning, site-directed mutagenesis, reverse genetics, recombinant protein expression and purification, pseudovirus construction, classical and pseudoneutralisation assays. The computational tools and insights from this project can be applied to future emerging viral pathogens. The range of highly sought after skills developed within the project will give a firm bases for a future career in academia and/or industry.