Year: 2025
Type: article
Abstract: MOTIVATION: 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. RESULTS: 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. AVAILABILITY AND IMPLEMENTATION: All data and codes are available at https://github.com/meilanglang/RNA-RNA-Interaction.
Source: Bioinformatics
Authors: Mei Lang, Thomas Litfin, Ke Chen, Jian Zhan, Yaoqi Zhou
Institutions: Shenzhen Bay Laboratory, Griffith University
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,989
Award: G8242590901
OpenAlex ID: W4410117713