Integrating end-to-end learning with deep geometrical potentials for ab initio RNA structure prediction

Yang Li, Chengxin Zhang, Chenjie Feng, Robin Pearce, P. Lydia Freddolino () and Yang Zhang ()
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Yang Li: National University of Singapore
Chengxin Zhang: University of Michigan Medical School
Chenjie Feng: University of Michigan Medical School
Robin Pearce: University of Michigan Medical School
P. Lydia Freddolino: University of Michigan Medical School
Yang Zhang: National University of Singapore

Nature Communications, 2023, vol. 14, issue 1, 1-13

Abstract: 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 local frame rotations and geometric restraints from experimentally solved RNA structures, where the learned knowledge is converted into a hybrid energy potential to guide RNA structure assembly. The method significantly outperforms previous approaches by >73.3% in TM-score on a sequence-nonredundant dataset containing recently released structures. Detailed analyses showed that the major contribution to the improvements arise from the deep end-to-end learning supervised with the atom coordinates and the composite energy function integrating complementary information from geometry restraints and end-to-end learning models. The open-source DRfold program with fast training protocol allows large-scale application of high-resolution RNA structure modeling and can be further improved with future expansion of RNA structure databases.

Date: 2023
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DOI: 10.1038/s41467-023-41303-9

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