Has AlphaFold 3 reached its success for RNAs?

Abstract

Predicting the 3D structure of RNA is a significant challenge despite ongoing advancements in the field. Although AlphaFold has successfully addressed this problem for proteins, RNA structure prediction raises difficulties due to fundamental differences between proteins and RNAs, which hinder direct adaptation. The latest release of AlphaFold, AlphaFold 3, has broadened its scope to include multiple different molecules like DNA, ligands and RNA. While the article discusses the results of the last CASP-RNA dataset, the scope of performances and the limitations for RNAs are unclear. In this article, we provide a comprehensive analysis of the performances of AlphaFold 3 in the prediction of RNA 3D structures. Through an extensive benchmark over five different test sets, we discuss the performances and limitations of AlphaFold 3. We also compare its performances with ten existing state-of-the-artab initio, template-based and deep-learning approaches. Our results are freely available on the EvryRNA platform:https://evryrna.ibisc.univ-evry.fr/evryrna/alphafold3/.