Project description
A proactive immunology approach to tackle viral variants before they emerge
The COVID-19 pandemic exposed vulnerability to rapidly evolving viruses, with emerging variants that outpaced vaccines and diverse treatments. The ERC-funded PROACTIVE project will focus on developing vaccines and monoclonal antibodies that could neutralise existing and future variants of SARS-CoV-2 and other coronaviruses. The emphasis will be on regions of the virus less likely to mutate. AI systems will analyse the immune system’s response to the virus, identifying stable parts of the virus that do not change much across different variants. Computer simulations will be used to design proteins that can bind to the virus effectively. These proteins (immunogens) will be placed on nanoparticles to deliver the vaccine more effectively. If successful, PROACTIVE could transform how we combat highly variable pathogens, including influenza and HIV.
Objective
The COVID-19 pandemic caught the world unprepared. Vaccines and monoclonal antibodies (mAbs), developed in record time, mitigated the health and economic damages, however our reaction has always been one step behind the virus evolution, and emerging variants repeatedly escaped our interventions. The omicron variant escaped humoral immunity generated by most vaccines and mAbs by mutating immunodominant epitopes. The extremely potent mAb developed by our laboratory also lost potency against omicron. Here we propose to develop vaccines and monoclonals neutralizing existing and future variants of SARS-CoV-2 and other coronaviruses, by targeting immunologically subdominant regions which are less susceptible to antigenic variation. We will isolate mAbs from individuals who had infection and multiple vaccinations, whose repertoire is enriched in B cells encoding broadly neutralizing antibodies, to build a map of the broadly shared epitopes. Structural prediction and clustering of the immune repertoire through deep neural networks will be used to improve the breadth of coverage of the mAbs. The Monte Carlo-based sequence design of Rosetta and free energy perturbation calculations will be used to in-silico design protein-binding proteins and identify newly designed immunogens which can be loaded on nanoparticles and used as vaccines. This approach will provide broadly protective mAbs and vaccines proactively designed to neutralize all variants of SARS-CoV-2 and new coronaviruses that are very likely to jump from animals to humans in the future. If successful, the approach to map subdominant epitopes and use of genomic and structural information to design mAbs and vaccines targeting subdominant, broadly conserved epitopes, will pave the way to approach other pathogens with high antigenic variability such as influenza and HIV viruses, Plasmodium spp. and antibiotic resistant bacteria. This will strongly increase European competitiveness in fighting infections.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- medical and health sciences health sciences public health epidemiology pandemics
- medical and health sciences health sciences infectious diseases viral diseases RNA viruses influenza
- medical and health sciences health sciences infectious diseases viral diseases RNA viruses HIV
- medical and health sciences health sciences infectious diseases viral diseases RNA viruses coronaviruses
- medical and health sciences basic medicine pharmacology and pharmacy pharmaceutical drugs vaccines
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Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.1 - European Research Council (ERC)
MAIN PROGRAMME
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Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-ERC - HORIZON ERC Grants
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) ERC-2022-ADG
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
53100 Siena
Italy
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.