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RNA-Diffusion

Financiado

RNA Dynamics prediction with Diffusion Models

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-dim... ver más

30/11/2026

SISSA

173K€

Presupuesto del proyecto: 173K€

Líder del proyecto

SCUOLA INTERNAZIONALE SUPERIORE DI STUDI AVAN... No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Ver 1 Participantes

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2024-05-22

Línea de financiación objetivo El proyecto se financió a través de la siguiente ayuda:

HORIZON-MSCA-2023-PF-01-01: MSCA Postdoctoral Fellowships 2023

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SISSA

172.75K€ | Lider

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Duración del proyecto: 30 meses Fecha Inicio: 2024-05-22
Fecha Fin: 2026-11-30

Presupuesto del proyecto 173K€

Descripción del proyecto 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.

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