RNA function crucially depends on its structure. Thermodynamic models that are used for secondary structure prediction report a large number of structures in a limited energy range, often failing in identifying the correct native structure unless complemented by auxiliary experimental data. In this work we build an automatically trainable model that is based on a combination of thermodynamic parameters, chemical probing data ( Selective 2^\prime Hydroxyl Acylation analyzed via Primer Extension , SHAPE), and co-evolutionary data (Direct Coupling Analysis, DCA)  . Perturbations are trained on a suitable set of systems for which the native structure is known . A convolutional window is used to include neighboring reactivities in the SHAPE nodes of the network, and regularization terms limit overfitting improving transferability. The most transferable model is chosen with a cross-validation strategy that allows to automatically optimize the relative importance of heterogenous input datasets. The model architecture enlightens the structural information content of SHAPE reactivities and their dependence on local conformational ensembles. By using the selected model , we obtain enhanced populations for reference native structures and more sensitive and precise predicted structures in an independent validation set not seen during training . The flexibility of the approach allows the model to be easily retrained and adapted to incorporate arbitrary experimental information.
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 , SHAPE), and co-evolutionary data (Direct Coupling Analysis, DCA) 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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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
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in a limited energy range, often failing in identifying the correct native structure
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In this work we build an automatically trainable model that is based on a combination of
Here, we build a set of models that combine
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Selective 2^\prime Hydroxyl Acylation analyzed via Primer Extension
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through a network that outputs perturbations to the ensemble free energy
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on a suitable set of systems for which the native structure is known . A convolutional window is used to include neighboring reactivities in the SHAPE
to increase the ensemble populations of a representative set of known native RNA structures. In the chemical probing
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and regularization terms limit overfitting improving
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
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allows to automatically optimize the relative importance of heterogenous input datasets. The model architecture enlightens the structural information content of SHAPE reactivities and their dependence on local conformational ensembles. By using
estimates the performance of models on systems on which they are not trained. With
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we obtain increased ensemble populations for
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sensitive and precise predicted structures
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not seen during training