Objective
Ribonucleic Acids (RNAs) are crucial polymers in biology: RNAs might be the key to understanding the origins of life and are extremely promising therapeutic tools. Just like proteins, RNAs numerous properties stem from their 3-dimensional structure, which itself arises from their nucleotide sequence. However, RNA molecules are particularly dynamical, making their structure noticeably difficult to study, both experimentally and theoretically. Moreover, RNA dynamics are critical for biological function.
Recently, deep learning (DL) methods have revolutionised computational biophysics by mastering the task of protein native structure prediction from sequence. Comparable success can be hoped for RNA, but it is impaired by the less abundant data and the lack of direct transferability of current methods. More importantly, no attempt has been made at developing a DL approach to directly predict RNA dynamics.
In this proposal we aim to adapt the emergent framework of diffusion models to the task of one-shot sampling of RNA conformational ensembles. Diffusion models constitute a new paradigm in generative machine learning (ML) that has attained astounding successes in conditional image generation, audio, graph and geometric shape synthesis. Our goal is to produce a neural network-based software which, given an arbitrary input sequence, efficiently samples the 3D coordinates of RNA conformations according to their equilibrium probabilities. To do so we suggest an original approach combining (1) coarse-grained internal coordinates, (2) a diffusion-based generative framework, (3) an attention-based architecture inspired by state-of-the-art DL biomolecular approaches and (4) a mixed training procedure based on experimental fragments and molecular dynamics simulations. Our approach could be used as a replacement to extensive MD simulations which are voracious both in human time and energy expense, hence accelerating biophysical research and decreasing its carbon footprint.
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 computer and information sciences artificial intelligence machine learning deep learning
- natural sciences biological sciences genetics nucleotides
- natural sciences biological sciences biophysics
- natural sciences biological sciences genetics RNA
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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-2023-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.
34136 Trieste
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.