Computational prediction of RNA tertiary structures using machine learning methods
This work addresses the challenge of predicting RNA structures for biologists and computational researchers, but it is incremental as it primarily reviews existing advances rather than introducing new methods.
The paper reviews the application of machine learning methods to predict RNA tertiary structures, highlighting their novelty in this domain and discussing their advantages, limitations, and potential for advancing RNA functional understanding and design.
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.