Integrating end-to-end learning with deep geometrical potentials for ab initio RNA structure prediction
Abstract 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 loc...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-09-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-023-41303-9 |