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RNA3DB: A structurally-dissimilar dataset split for training and benchmarking deep learning models for RNA structure

Marcell Szikszai¹, Marcin Magnus¹, Siddhant Sanghi^2,3

¹Department of Molecular and Cellular Biology, Harvard University, Cambridge, 02138, MA, USA.

Biorxiv : the Preprint Server for Biology

|February 14, 2024

Summary

RNA structure prediction faces challenges due to limited data and overlapping sets. RNA3DB offers a non-redundant dataset and robust splitting method for reliable deep learning model benchmarking.

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Area of Science:

Computational biology
Structural biology
Bioinformatics

Background:

Deep learning models like AlphaFold have advanced protein structure prediction, increasing interest in RNA structure prediction.
RNA structure prediction faces challenges due to limited experimentally resolved structures and lower structural diversity compared to proteins.
Existing literature often reports inflated performance by using training and testing sets with significant structural overlap, and deep learning models currently underperform traditional methods in RNA structure prediction (CASP15).

Approach:

RNA3DB is a novel dataset of structured RNAs derived from the Protein Data Bank (PDB).
It categorizes RNA 3D chains into distinct, non-redundant Components based on sequence and structure.
This approach ensures robust division of training, validation, and testing sets, guaranteeing structural and sequence dissimilarity.

Key Points:

RNA3DB provides a curated dataset designed for training and benchmarking deep learning models for RNA structure prediction.
The dataset's Components ensure that any split into training, validation, and testing sets results in structurally and sequentially distinct subsets.
A specific 70/30 train/test split of RNA3DB Components is provided and will be updated periodically.

Conclusions:

RNA3DB addresses the critical need for high-quality, non-redundant datasets in RNA structure prediction research.
The methodology and source code are available, promoting reproducible and customizable dataset generation.
This resource aims to facilitate the development of more accurate and reliable deep learning models for RNA structure prediction.