Geometric deep learning of RNA structure
By Raphael J. L. Townshend, Stephan Eismann, Andrew M. Watkins, R. Rangan, Masha Karelina, Rhiju Das, Ron O. Dror. 2021
339 citations recorded by the source corpus.
Machine learning solves RNA puzzles RNA molecules fold into complex three-dimensional shapes that are difficult to determine experimentally or predict computationally. Understanding these structures may aid in the discovery of drugs for currently untreatable diseases. Townshend et al. introduced a machine-learning method that significantly improves prediction of RNA structures (see the Perspective by Weeks). Most other recent advances in deep learning have required a tremendous amount of data for training. The fact that this method succeeds given very little training data suggests that related methods could address unsolved problems in many fields where data are scarce. Science, abe5650, this issue p. 1047; see also abk1971, p. 964 A machine learning method significantly improves scoring of RNA structural models, despite being trained on very few structures. RNA molecules adopt three-dimensional structures that are critical to their function and of interest in drug discovery. Few RNA structures are known, however, and predicting them computationally has proven challenging. We introduce a machine learning approach that enables identification of accurate structural models without assumptions about their defining characteristics, despite being trained with only 18 known RNA structures. The resulting scoring function, the Atomic Rotationally Equivariant Scorer (ARES), substantially outperforms previous methods and consistently produces the best results in community-wide blind RNA structure prediction challenges. By learning effectively even from a small amount of data, our approach overcomes a major limitation of standard deep neural networks. Because it uses only atomic coordinates as inputs and incorporates no RNA-specific information, this approach is applicable to diverse problems in structural biology, chemistry, materials science, and beyond.
Subfields: Materials Chemistry, Molecular Biology
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Materials Chemistry
- David Baker
- Frank DiMaio
- Ivan V. Anishchenko
- Minkyung Baek
- Sergey Ovchinnikov
- Yang Zhang
- Tanja Kortemme
- Jeffrey J. Gray
In Molecular Biology
- Frank DiMaio
- David Baker
- Ivan V. Anishchenko
- Minkyung Baek
- Ryan McHugh
- A. Ren
- Sergey Ovchinnikov
- Yang Zhang
Citations in the corpus
- Accurate prediction of nucleic acid and protein-nucleic acid complexes using RoseTTAFoldNA (2022, methodology): “More recently, deep learning methods have been used for contact prediction to aid in RNA structure determination [8], and to select models from ensembles generated using other structure sampling approaches [9].”
- Accurate prediction of protein–nucleic acid complexes using RoseTTAFoldNA (2023, methodology): “For predicting protein components, machine learning-guided approaches like RoseTTAFold 4 and Alpha-Fold 5 are highly accurate, while RNA structure prediction has used a combination of Monte Carlo sampling approaches 6–9 as well as deep learning methods 10,11 .”
- Interpretable RNA Foundation Model from Unannotated Data for Highly Accurate RNA Structure and Function Predictions (2022, background): “ARES, which consists of many processing layers, was proposed to score the predicted RNA structures [10].”
- Recent advances in RNA structurome (2022, background): “Recently, a novel machine learning approach, ARES, was able to identify accurate RNA structural models after training with only 18 known RNA structures (Townshend et al., 2021).”