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

Li, Y.; Zhang, C.; Feng, C.; Freddolino, P. L.; Zhang, Y.

doi:10.1101/2022.12.30.522296

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bioRxiv · 10.1101/2022.12.30.522296

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

Li, Y.·Zhang, C.·Feng, C.·Freddolino, P. L.·Zhang, Y.

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Abstract

RNAs are fundamental in living cells and perform critical functions determined by the tertiary architectures. However, accurate modeling of 3D RNA structure remains a challenging problem. Here 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 subsequent RNA structure constructions. The method significantly outperforms previous approaches by >75.6% in TM-score on a 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 allows large-scale application of high-resolution RNA structure modeling and can be further improved with future release of RNA structure databases.

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Li, Y., Zhang, C., Feng, C., Freddolino, P. L., Zhang, Y.. 2022-12-30. Integrating end-to-end learning with deep geometrical potentials for ab initio RNA structure prediction. https://doi.org/10.1101/2022.12.30.522296

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