Deciphering RNA Secondary Structure Prediction: A Probabilistic K-Rook Matching Perspective

TL;DR

This paper introduces RFold, a novel probabilistic K-Rook matching approach to RNA secondary structure prediction, significantly improving efficiency while maintaining competitive accuracy.

Contribution

It reformulates RNA secondary structure prediction as a K-Rook matching problem and proposes RFold, a simple method with a bi-dimensional optimization strategy for faster inference.

Findings

RFold achieves about eight times faster inference than state-of-the-art methods.

RFold maintains competitive prediction accuracy.

The K-Rook matching perspective simplifies the prediction process.

Abstract

The secondary structure of ribonucleic acid (RNA) is more stable and accessible in the cell than its tertiary structure, making it essential for functional prediction. Although deep learning has shown promising results in this field, current methods suffer from poor generalization and high complexity. In this work, we reformulate the RNA secondary structure prediction as a K-Rook problem, thereby simplifying the prediction process into probabilistic matching within a finite solution space. Building on this innovative perspective, we introduce RFold, a simple yet effective method that learns to predict the most matching K-Rook solution from the given sequence. RFold employs a bi-dimensional optimization strategy that decomposes the probabilistic matching problem into row-wise and column-wise components to reduce the matching complexity, simplifying the solving process while guaranteeing the validity of the output. Extensive experiments demonstrate that RFold achieves competitive performance and about eight times faster inference efficiency than the state-of-the-art approaches. The code and Colab demo are available in (http://github.com/A4Bio/RFold).