Integrating end-to-end learning with deep geometrical potentials for ab initio RNA structure predict...
Integrating end-to-end learning with deep geometrical potentials for ab initio RNA structure prediction
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Publisher
London: Nature Publishing Group UK
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Language
English
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London: Nature Publishing Group UK
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Contents
RNAs are fundamental in living cells and perform critical functions determined by their tertiary architectures. However, accurate modeling of 3D RNA structure remains a challenging problem. We present a novel method, DRfold, to predict RNA tertiary structures by simultaneous learning of local frame rotations and geometric restraints from experiment...
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Full title
Integrating end-to-end learning with deep geometrical potentials for ab initio RNA structure prediction
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TN_cdi_doaj_primary_oai_doaj_org_article_dfd8edec979243e8aee69699803f3095
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_dfd8edec979243e8aee69699803f3095
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ISSN
2041-1723
E-ISSN
2041-1723
DOI
10.1038/s41467-023-41303-9
