Researchers introduce RhoFold+, a groundbreaking deep learning model that predicts RNA 3D structures with unprecedented accuracy and efficiency, addressing critical challenges in structural biology.
By Vijay Kumar MalesuReviewed by Susha Cheriyedath, M.Sc.Nov 24 2024 RhoFold+ delivers a leap in RNA 3D structure prediction, combining speed and accuracy to tackle data scarcity and unlock new possibilities in drug development and synthetic biology .
Background RNA molecules play a central role in molecular biology, influencing gene regulation and serving as promising targets for drug development and synthetic biology. Despite the importance of RNA structure in understanding function, the majority of RNA molecules remain structurally uncharacterized, with less than 1% of RNA-only structures available in the Protein Data Bank as of December 2023.
Central to RhoFold+ is the RNA Foundation Model , built on a transformer architecture inspired by Bidirectional Encoder Representations from Transformers . Pretrained on ~23.7 million non-coding RNA sequences from RNAcentral, RNA-FM effectively captured sequence dependencies through masked token prediction. A self-distillation dataset, combining experimental annotations with pseudo-structural labels, further enhanced the model's accuracy.
Generalization Capabilities: The model demonstrates strong generalizability, accurately predicting RNA structures from unseen families and types, and even performing well on newly determined structures not included in its training set. A comprehensive evaluation across all experimentally determined RNA structures showed that RhoFold+ demonstrated high cross-validation performance. It maintained consistent accuracy across different data splits and generalized to unseen RNA structures, including new PDB entries. However, challenges remain in predicting RNA junctions and pseudoknots, which exhibit significant conformational flexibility.
RNA Crystallography Electron Electron Microscopy Gene Language Microscopy Molecular Biology Protein Ribonucleic Acid Synthetic Biology X-Ray
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