bioRxivBioinformaticsPreprint
De Novo Prediction of RNA 3D Structures with Deep Learning
Julius RamakersiD, Christopher Frederik BlumiD, Sabrina König, Stefan Harmeling BiD, Markus KollmanniD
Abstract
We present a Deep Learning approach to predict 3D folding structures of RNAs from their nucleic acid sequence. Our approach combines an autoregressive Deep Generative Model, Monte Carlo Tree Search, and a Score Model to find and rank the most likely folding structures for a given RNA sequence. We confirm the predictive power of our approach by setting new benchmarks for some longer sequences in a simulated blind test of the RNA Puzzles prediction challenge.
- Citations
- 2
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- De Novo RNA Tertiary Structure Prediction at Atomic Resolution Using Geometric Potentials from Deep Learning
Robin Pearce, Gilbert S. Omenn, Yang Zhang·bioRxiv·May 15, 2022
- State-of-the-RNArt: benchmarking current methods for RNA 3D structure prediction
Clément Bernard, Guillaume Postic, … Fariza Tahi·NAR Genomics and BioinformaticsPeer-reviewed·Apr 4, 2024
- Accurate de novo prediction of RNA 3D structure with transformer network
Chenjie Feng, Wenkai Wang, … Jianyi Yang·bioRxiv·Oct 24, 2022
- On inputs to deep learning for RNA 3D structure prediction
Marcell Szikszai, Marcin Magnus, … Elena Rivas·bioRxiv·Feb 14, 2025
- A Root Mean Square Deviation Estimation Algorithm (REA) and its use for improved RNA Structure Prediction
Agoritsa Kalampaliki, Alexandros C. Dimopoulos, Martin Reczko·bioRxiv·Feb 28, 2024
- UFold: Fast and Accurate RNA Secondary Structure Prediction with Deep Learning
Laiyi Fu, Yingxin Cao, … Xiaohui S. Xie·Nucleic Acids ResearchPeer-reviewed·Oct 20, 2021