Cascade Open Call RN1: AI-DENINFECT

Artificial intelligence (AI) can be used to enhance the surveillance of viruses with pandemic potential, extending beyond the use of predictive models that assess demographic, environmental, and pathogen-specific data to track the spread of emerging pathogens. AI can also be crucial in unraveling the biological complexities during infection, which arise from the interactions between the host and pathogen, influencing transcriptomic profiles and post-translational modifications of proteins. This proposal, aligned with the INF-ACT program, presents a project aimed at investigating the pathogenicity of viral infections through bioinformatics-based predictions and mathematical models to study viral molecular interactions. The Dengue virus (DENV) has been selected as the viral model for this study, as it is emerging as a new pandemic threat in Southern Italy, demanding an immediate response. The AI-DENInfect project will examine, through the development of various AI models, the genomic and structural adaptations that mediate the molecular interactions between viral proteins and host protein targets. Special emphasis will be placed on the early stages of DENV infection. The first aim of this project is the development of an AI-powered predictor, to create the first comprehensive and effective tool to assess the pathogenesis of arboviral diseases. In addition the application of bioinformatics and AI will be use to integrate genetic and structural data, deep learning algorithms, and advanced computational techniques to address key issues in infection biology, such as host-pathogen interactions that influence host species transmission, cellular tropism, replication dynamics, and viral fitness