Summary
"Proteins are essential and incredibly versatile actors for all biological processes in living organisms. Massive amounts of data describing their amino acid sequences and 3D shapes are now openly accessible. Yet, we still know little about how they interact and function with one another in vivo. Nor how they diversified throughout evolution to orchestrate highly specialised behavioural traits. This ERC CoG proposal, PROMISE, meets the urgent need for decrypting protein sequences and structures toward guiding biological intervention. It will assess and harness the protein variations naturally occurring in multi-cellular organisms to unlock critical questions across developmental, evolutionary, and molecular biology: - What are the sites in a protein crucial for selecting its cellular partners and binding to them? How can they be modified toward modulating protein functioning? - How did the proteomes responsible for the ability of humans and zebra finches to learn ""language"" from their peers expand in evolution? Are there convergent amino acid patterns, beyond the striking analogies in the underlying anatomical structures and in gene expression and regulation? PROMISE will leverage the tension between evolutionary divergence, gene duplication, and alternative splicing by combining massive high-throughput protein-related data integration and cutting-edge artificial intelligence techniques. It will bring a paradigm shift on assessing protein diversity and its impact on rewiring interaction networks in evolution. The expected results will provide efficient and scalable solutions to extract biological meaning from big data and transform it into interpretable and actionable models. This new knowledge will be transferable to a broad range of impactful societal issues, such as the inter-individual variability in disease susceptibility, the design of more specific drugs and more bio-compatible de novo proteins, or strategising human adaptation to climate change."
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101087830 |
| Start date: | 01-09-2023 |
| End date: | 31-08-2028 |
| Total budget - Public funding: | 1 952 762,50 Euro - 1 952 762,00 Euro |
Cordis data
Original description
"Proteins are essential and incredibly versatile actors for all biological processes in living organisms. Massive amounts of data describing their amino acid sequences and 3D shapes are now openly accessible. Yet, we still know little about how they interact and function with one another in vivo. Nor how they diversified throughout evolution to orchestrate highly specialised behavioural traits. This ERC CoG proposal, PROMISE, meets the urgent need for decrypting protein sequences and structures toward guiding biological intervention. It will assess and harness the protein variations naturally occurring in multi-cellular organisms to unlock critical questions across developmental, evolutionary, and molecular biology: - What are the sites in a protein crucial for selecting its cellular partners and binding to them? How can they be modified toward modulating protein functioning? - How did the proteomes responsible for the ability of humans and zebra finches to learn ""language"" from their peers expand in evolution? Are there convergent amino acid patterns, beyond the striking analogies in the underlying anatomical structures and in gene expression and regulation? PROMISE will leverage the tension between evolutionary divergence, gene duplication, and alternative splicing by combining massive high-throughput protein-related data integration and cutting-edge artificial intelligence techniques. It will bring a paradigm shift on assessing protein diversity and its impact on rewiring interaction networks in evolution. The expected results will provide efficient and scalable solutions to extract biological meaning from big data and transform it into interpretable and actionable models. This new knowledge will be transferable to a broad range of impactful societal issues, such as the inter-individual variability in disease susceptibility, the design of more specific drugs and more bio-compatible de novo proteins, or strategising human adaptation to climate change."Status
SIGNEDCall topic
ERC-2022-COGUpdate Date
31-07-2023
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