Lang Mei, Litfin Thomas, Chen Ke, Zhan Jian, Zhou Yaoqi
Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen, 518107, China.
Institute for Glycomics, Griffith University, Parklands Dr, Southport, QLD 4222, Australia.
Bioinformatics. 2025 May 6. doi: 10.1093/bioinformatics/btaf289.
The intricate network of RNA-RNA interactions, crucial for orchestrating essential cellular processes like transcriptional and translational regulations, has been unveiling through high-throughput techniques and computational predictions. As experimental determination of RNA-RNA interactions at the base-pair resolution remains challenging, a timely update for assessing complementary computational tools is necessary, particularly given the recent emergence of deep learning-based methods.
Here, we employed base pairs derived from three-dimensional RNA complex structures as a gold standard benchmark to assess the performance of 23 different methods ranging from alignment-based methods, free-energy-based minimization to deep-learning techniques. The result indicates that a deep-learning-based method, SPOT-RNA, can be generalized to make accurate zero-shot predictions of RNA-RNA interactions not only between previously unseen RNA structures but also between RNAs without monomeric structures. The finding underscores the potential of deep learning as a robust tool for advancing our understanding of these complex molecular interactions.
All data and codes are available at https://github.com/meilanglang/RNA-RNA-Interaction.
Supplementary data are available at Bioinformatics online.
RNA - RNA相互作用的复杂网络对于协调转录和翻译调控等重要细胞过程至关重要，通过高通量技术和计算预测已逐渐被揭示。由于在碱基对分辨率下对RNA - RNA相互作用进行实验测定仍然具有挑战性，因此有必要及时更新以评估互补的计算工具，特别是考虑到最近基于深度学习的方法的出现。
在这里，我们采用源自三维RNA复合物结构的碱基对作为金标准基准，以评估23种不同方法的性能，这些方法涵盖了从基于比对的方法、基于自由能的最小化到深度学习技术。结果表明，基于深度学习的方法SPOT - RNA不仅可以推广到对以前未见过的RNA结构之间，而且对没有单体结构的RNA之间的RNA - RNA相互作用进行准确的零样本预测。这一发现强调了深度学习作为推进我们对这些复杂分子相互作用理解的强大工具的潜力。
所有数据和代码可在https://github.com/meilanglang/RNA - RNA - Interaction获取。
补充数据可在《生物信息学》在线获取。
