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Calonaci, N.* ; Jones, A. ; Cuturello, F.* ; Sattler, M. ; Bussi, G.*

Machine learning a model for RNA structure prediction.

NAR Gen. Bioinfo. 2:lqaa090 (2020)
Verlagsversion DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
RNA function crucially depends on its structure. Thermodynamic models currently used for secondary structure prediction rely on computing the partition function of folding ensembles, and can thus estimate minimum free-energy structures and ensemble populations. These models sometimes fail in identifying native structures unless complemented by auxiliary experimental data. Here, we build a set of models that combine thermodynamic parameters, chemical probing data (DMS and SHAPE) and co-evolutionary data (direct coupling analysis) through a network that outputs perturbations to the ensemble free energy. Perturbations are trained to increase the ensemble populations of a representative set of known native RNA structures. In the chemical probing nodes of the network, a convolutional window combines neighboring reactivities, enlightening their structural information content and the contribution of local conformational ensembles. Regularization is used to limit overfitting and improve transferability. The most transferable model is selected through a cross-validation strategy that estimates the performance of models on systems on which they are not trained. With the selected model we obtain increased ensemble populations for native structures and more accurate predictions in an independent validation set. The flexibility of the approach allows the model to be easily retrained and adapted to incorporate arbitrary experimental information.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
ISSN (print) / ISBN 2631-9268
e-ISSN 2631-9268
Zeitschrift NAR: Genomics and Bioinformatics
Quellenangaben Band: 2, Heft: 4, Seiten: , Artikelnummer: lqaa090 Supplement: ,
Verlag Oxford University Press
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Structural Biology (STB)