Knotted artifacts in predicted 3D RNA structures

Abstract

AbstractUnlike proteins, RNAs deposited in the Protein Data Bank do not contain topological knots. Recently, admittedly, the first trefoil knot and some lasso-type conformations have been found in experimental RNA structures, but these are still exceptional cases. Meanwhile, algorithms predicting 3D RNA models have happened to form knotted structures not so rarely. Interestingly, machine learning-based predictors seem to be more prone to generate knotted RNA folds than traditional methods. A similar situation is observed for the entanglements of structural elements. In this paper, we analyze all models submitted to the CASP15 competition in the 3D RNA structure prediction category. We show what types of topological knots and structure element entanglements appear in the submitted models and highlight what methods are behind the generation of such conformations. We also study the structural aspect of susceptibility to entanglement. We suggest that predictors take care of an evaluation of RNA models to avoid publishing structures with artifacts, such as unusual entanglements, that result from hallucinations of predictive algorithms.Author summary3D RNA structure prediction contests such as CASP and RNA-Puzzles lack measures for topology-wise evaluation of predicted models. Thus, predictors happen to submit potentially inappropriate conformations, for example, containing entanglements that are prediction artifacts.Automated identification of entanglements in 3D RNA structures is computationally hard. Distinguishing correct from incorrectly entangled conformations is not trivial and often requires expert knowledge.We analyzed 3D RNA models submitted to CASP15 and found that all entanglements in these models are artifacts.Compared to non-ML, machine learning-based methods are more prone to generating entanglements that are not present in natural RNAs.To increase the reliability of 3D RNA structure prediction, it is necessary to reject abnormally entangled structures in the modeling stage.