RNA Contact Prediction by Data Efficient Deep Learning

Abstract On the path to full understanding of the structure-function relationship or even design of RNA, structure prediction would offer an intriguing complement to experimental efforts. Any deep learning on RNA structure, however, is hampered by the sparsity of labeled training data. Utilizing the limited data available, we here focus on predicting spatial adjacencies (”contact maps”) as a proxy for 3D structure. We explore the space of self-supervised learning for RNA multiple sequence alignments and focus on downstream contact prediction from latent attention maps. Boosted decision trees in particular advance contact prediction quality and can be further enhanced by finetuning the pretrained backbone. Impressively, they double the precision of contact prediction/ reduce false positives by a factor of five over the baseline. We name our model BARNACLE. Our conceptional advance could prove a breakthrough in decreasing the sequence-structure gap for RNA and is generalize-able to other tasks.