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RNA, whose functionality is largely determined by its structure, plays animportant role in many biological activities. The prediction of pairwisestructural proximity between each nucleotide of an RNA sequence cancharacterize the structural information of the RNA. Historically, this problemhas been tackled by machine learning models using expert-engineered featuresand trained on scarce labeled datasets. Here, we find that the knowledgelearned by a protein-coevolution Transformer-based deep neural network can betransferred to the RNA contact prediction task. As protein datasets are ordersof magnitude larger than those for RNA contact prediction, our findings and thesubsequent framework greatly reduce the data scarcity bottleneck. Experimentsconfirm that RNA contact prediction through transfer learning using a publiclyavailable protein model is greatly improved. Our findings indicate that thelearned structural patterns of proteins can be transferred to RNAs, opening uppotential new avenues for research.

Knowledge from Large-Scale Protein Contact Prediction Models Can Be Transferred to the Data-Scarce RNA Contact Prediction Task