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RNA Dynamics prediction with Diffusion Models

CORDIS · observation · Publication date unknown

lecules 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 accordin

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recordType

award

status

SIGNED

region

EU

value

172750.08

unit

EUR