Computational prediction of RNA tertiary structures using machine learning methods

TL;DR

This paper reviews recent advances in applying machine learning techniques to predict RNA tertiary structures, highlighting their potential to enhance understanding and design of RNA molecules.

Contribution

It provides a comprehensive overview of the emerging use of machine learning in RNA tertiary structure prediction, discussing benefits, challenges, and future prospects.

Findings

ML methods show promise in RNA structure prediction

Current approaches face limitations in accuracy and data availability

Potential for improved RNA design and understanding

Abstract

RNAs play crucial and versatile roles in biological processes. Computational prediction approaches can help to understand RNA structures and their stabilizing factors, thus providing information on their functions, and facilitating the design of new RNAs. Machine learning (ML) techniques have made tremendous progress in many fields in the past few years. Although their usage in protein-related fields has a long history, the use of ML methods in predicting RNA tertiary structures is new and rare. Here, we review the recent advances of using ML methods on RNA structure predictions and discuss the advantages and limitation, the difficulties and potentials of these approaches when applied in the field.