RNA, whose functionality is largely determined by its structure, plays an important role in many biological activities. The prediction of pairwise structural proximity between each nucleotide of an RNA sequence can characterize the structural information of the RNA. Historically, this problem has been tackled by machine learning models using expert-engineered features and trained on scarce labeled datasets. Here, we find that the knowledge learned by a protein-coevolution Transformer-based deep neural network can be transferred to the RNA contact prediction task. As protein datasets are orders of magnitude larger than those for RNA contact prediction, our findings and the subsequent framework greatly reduce the data scarcity bottleneck. Experiments confirm that RNA contact prediction through transfer learning using a publicly available protein model is greatly improved. Our findings indicate that the learned structural patterns of proteins can be transferred to RNAs, opening up potential new avenues for research.

翻译：RNA，其功能在很大程度上由其结构决定，在众多生物活动中发挥着重要作用。预测RNA序列中每个核苷酸之间的成对结构邻近性，能够表征该RNA的结构信息。历史上，这一问题通常基于专家设计的特征并使用稀缺标记数据集训练的机器学习模型来解决。本研究发现，基于蛋白质共进化Transformer的深度神经网络所习得的知识可迁移至RNA接触预测任务。由于蛋白质数据集的规模比RNA接触预测数据集大数个数量级，我们的发现及后续框架极大缓解了数据稀缺瓶颈。实验证实，通过迁移学习利用公开的蛋白质模型进行RNA接触预测，性能得到显著提升。这项研究表明，蛋白质中习得的结构模式可迁移至RNA，为相关领域的研究开辟了新的潜在途径。