Work

ROMET-RNA benchmarks and selects RNA 3D structures using multiple predictors and alignment strategies

Year: 2026

Type: article

Abstract: Accurate prediction of RNA tertiary structure is essential for understanding RNA function, yet systematic, controlled benchmarking and integration of RNA three-dimensional structure prediction methods, particularly with respect to multiple sequence alignment strategies used by deep learning models, remain limited. Here, we present ROMET-RNA, an automated benchmarking framework and meta-selection toolkit that benchmarks eleven state-of-the-art RNA structure predictors within a standardized workflow and evaluates three complementary MSA pipelines, rMSA, RNAcmap3 MARS, and Infernal RNAcentral. Model selection is performed using a consensus scoring strategy combining TM-scoreRNA based structural similarity with the deep learning derived statistical potential lociPARSE. Benchmarking across four non-redundant test sets comprising 205 RNAs, including CASP15, RNA Puzzles, and recent PDB structures, demonstrates that ROMET-RNA consistently selects more accurate models than individual methods would on its own and we confirm that this is not an artefact of training-test overlap. Further analysis reveals that coevolutionary information substantially improves accuracy, that optimal MSA strategies are method specific, and that MSA depth exhibits a nonlinear relationship with performance. ROMET-RNA provides an open-source standardized framework that enhances RNA 3D structure model selection, supporting advances in RNA structural biology and genomics.

Source: Genome biology

DOI: 10.1186/s13059-026-04308-3