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

Author

Listed:
  • Yang Li

    (National University of Singapore
    University of Michigan Medical School)

  • Chengxin Zhang

    (University of Michigan Medical School
    Yale University)

  • Chenjie Feng

    (University of Michigan Medical School
    Ningxia Medical University)

  • Robin Pearce

    (University of Michigan Medical School
    National University of Singapore)

  • P. Lydia Freddolino

    (University of Michigan Medical School
    University of Michigan Medical School)

  • Yang Zhang

    (National University of Singapore
    University of Michigan Medical School
    National University of Singapore
    University of Michigan Medical School)

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.

Suggested Citation

  • Yang Li & Chengxin Zhang & Chenjie Feng & Robin Pearce & P. Lydia Freddolino & Yang Zhang, 2023. "Integrating end-to-end learning with deep geometrical potentials for ab initio RNA structure prediction," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-41303-9
    DOI: 10.1038/s41467-023-41303-9
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    References listed on IDEAS

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    1. Peter Eastman & Jason Swails & John D Chodera & Robert T McGibbon & Yutong Zhao & Kyle A Beauchamp & Lee-Ping Wang & Andrew C Simmonett & Matthew P Harrigan & Chaya D Stern & Rafal P Wiewiora & Bernar, 2017. "OpenMM 7: Rapid development of high performance algorithms for molecular dynamics," PLOS Computational Biology, Public Library of Science, vol. 13(7), pages 1-17, July.
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    3. Jaswinder Singh & Jack Hanson & Kuldip Paliwal & Yaoqi Zhou, 2019. "RNA secondary structure prediction using an ensemble of two-dimensional deep neural networks and transfer learning," Nature Communications, Nature, vol. 10(1), pages 1-13, December.
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    Cited by:

    1. Jiaxing Yang, 2024. "Predicting Distance matrix with large language models," Papers 2409.16333, arXiv.org.

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