Project description
Predicting interactions of proteins by machine learning
Conventionally, the 3D structure of a protein is defined using experimental methods such as X-ray crystallography and cryo-electron microscopy. Recent advances in AI offer the potential to predict protein structure from its amino acid sequence with great accuracy. This is not the case, however, with protein dynamics and protein interactions that are central for predicting protein function. Funded by the Marie Skłodowska-Curie Actions programme, the MARMOTTE project aims to develop methods for predicting the interaction of a dynamic molecular structure. This will provide important insight into protein complexes and facilitate faster and more advanced drug discovery.
Objective
This year has seen a breakthrough in structural bioinformatics - deep learning-based methods, most notably Google DeepMind's AlphaFold2, have demonstrated near-experimental accuracy of protein structure predictions. However, even the best protein structure prediction methods do not automatically provide knowledge about protein dynamics and protein interactions, which is often essential to understand or predict the biological functions of proteins. Those functions are performed via intermolecular interactions, and such interactions almost always involve conformational changes of engaged partners. The problem of modeling dynamic protein structures and their complexes is still largely unsolved - this project aims to significantly contribute towards its future solution by exploring the link between computational geometry, statistical physics, and machine learning. The postdoctoral researcher will develop novel methods that: given a dynamic (moving) molecular structure, efficiently compute tessellation-derived contact areas; given a starting structure and its tessellation-derived contacts areas, predict (using a graph neural network) how the interatomic contact areas will change upon motion; given a protein complex model generated by docking, use the predicted statistical properties of the contact areas to estimate (using a graph neural network) the protein-protein binding energy score. If successfully developed, such methods will provide unique data about the dynamics of tessellation-derived interatomic contact areas. Most importantly, they will provide effective dynamics-aware scores for assessing and ranking structural models of protein complexes.
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 proteins
- natural sciences mathematics pure mathematics geometry
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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.1.2 - Marie Skłodowska-Curie Actions (MSCA)
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-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships
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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) HORIZON-MSCA-2021-PF-01
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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.
75794 PARIS
France
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.