What question did this study set out to answer?

To develop an automated method, DeepRMSF, for predicting atomic-level flexibility in RNA structures.

January 16, 2026Briefings in Bioinformatics0 citationsOpen Access

DeepRMSF: a deep learning-based automated approach for predicting atomic-level flexibility in RNA structure

CFChenjie FengNingxia Medical University XSXiaowen SunQingdao Binhai University XSXintao SongQingdao Binhai University

Key Points

To develop an automated method, DeepRMSF, for predicting atomic-level flexibility in RNA structures.
Developed a deep learning model leveraging atomic-level descriptions of RNA.
Trained on root-mean-square fluctuations derived from molecular dynamics simulations.
Benchmarking involved 371 non-redundant RNAs with five-fold cross-validation and an independent test set.
Achieved predictions of RNA flexibility in approximately 8.2 seconds.
DeepRMSF demonstrated a Pearson correlation coefficient (PCC) of 0.7219–0.7464 during cross-validation.
Independent testing showed a PCC of 0.734, indicating strong prediction accuracy.
Achieved over 3000-fold speed-up compared to traditional molecular dynamics simulations.

Abstract

Abstract Understanding RNA conformational dynamics is essential to understand its roles in complex biological processes. While computational methods have revolutionized the prediction of static 3D RNA structures, predicting local flexibility directly from structure remains a significant challenge. We developed DeepRMSF, a deep learning-based method that leverages atomic-level descriptions of RNA to predict vibrational flexibility given a tertiary structure. Trained on MD-derived root-mean-square fluctuations(RMSF), DeepRMSF was benchmarked on 371 nonredundant RNAs, with 311 RNAs used for five-fold cross-validation (PCC = 0.7219–0.7464) and 60 RNAs as an independent test set (PCC = 0.734), ensuring minimal sequence/structural similarity between sets. DeepRMSF predicts the local flexibility of medium-sized RNAs (~75 nucleotides) in ~8.2 s, achieving 3000-fold speed-up over MD simulations while maintaining strong extrapolative accuracy. Rather than replacing MD, DeepRMSF offers a scalable and practical alternative for transcriptome-scale screening of RNA flexibility, facilitating studies on RNA structure-dynamics-function relationships and supporting computational modeling in RNA biology.

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