Project description
Antimicrobial peptide discovery through deep optimised generation
The overuse of antibiotics has led to the emergence of multi-drug-resistant microbes, posing a health threat. It is projected that by 2050, antimicrobial resistance could result in 10 million deaths annually. Antimicrobial peptides (AMPs) have shown promise in selectively killing antibiotic-resistant pathogens. However, their clinical success has been limited due to lower activity and safety compared to traditional antibiotics. In this context, the ERC-funded DOG-AMP project is developing methods for AMP discovery using Deep Optimised Generation (DOG). It will use a model that combines autoencoder, probabilistic modelling, and Pareto optimisation techniques. This model will be integrated into an AMP design framework, which will then be applied to identify and validate safer AMPs.
Objective
The DOG-AMP project will develop cutting-edge methods for Deep Optimized Generation (DOG), and use them to transform the emerging field of AntiMicrobial Peptide (AMP) discovery.
Continuous overuse of antibiotics fuels the outgrowth and spread of multi-drug resistant microbial strains. Increasing antimicrobial resistance is already now a major health and economic hazard, and is expected to account for 10 million deaths globally per year by 2050, exceeding deaths caused by cancer.
AMPs are short peptides that can actively and selectively kill antibiotic-resistant pathogens, and as such are considered the most promising strategy for fighting antimicrobial resistance. Still, intensive research on AMP did not translate to their success in the clinic, mostly due to their lower activity and safety compared to existing antibiotics. Deep optimized generation has the potential to radically advance AMP discovery, but only once unsolved problems are attacked and open research directions in this area are further explored to reach three major objectives of the DOG-AMP project:
i) develop a novel model, geared for deep optimized generation, combining the variational autoencoder framework with probabilistic modeling and algorithms for Pareto (conflicting multi-target) optimization, dealing with data scarcity and bias, generation diversity, and model interpretability;
ii) combine the deep optimized generation model into a framework tailored for the specific needs of AMP design, e.g. accounting for AMP clustering, or conflicting features that make AMPs active or toxic;
iii) apply the newly developed framework to explore and navigate the space of peptides to select and experimentally validate the best candidates that will supersede existing AMPs and antibiotics in their activity against hazardous microbes and safety.
DOG-AMP has the potential to bring breakthroughs in the broad research areas of deep generative modeling, sequence optimization, and AMP discovery.
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.
- natural sciences biological sciences biochemistry biomolecules
- agricultural sciences animal and dairy science domestic animals
- medical and health sciences basic medicine pharmacology and pharmacy pharmaceutical drugs antibiotics
- medical and health sciences clinical medicine oncology
- engineering and technology environmental engineering energy and fuels
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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)
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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.
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Call for proposal
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(opens in new window) ERC-2023-COG
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85764 Neuherberg
Germany
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