Papazoglou Ioannis, Chatzigoulas Alexios, Tsekenis George, Cournia Zoe
Biomedical Research Foundation, Academy of Athens, Athens 11527, Greece.
Department of Biology, National and Kapodistrian University of Athens, Athens 15784, Greece.
J Chem Inf Model. 2025 Jul 14;65(13):6483-6498. doi: 10.1021/acs.jcim.4c02094. Epub 2025 Jul 2.
RNA possesses functional significance that extends beyond the transport of genetic information. The functional roles of noncoding RNA can be mediated through their tertiary and secondary structure, and thus, predicting RNA structure holds great promise for unleashing their applications in diagnostics and therapeutics. However, predicting the three-dimensional (3D) structure of RNA remains challenging. Applying artificial intelligence techniques in the context of natural language processing and large language models (LLMs) could incorporate evolutionary information to RNA 3D structure predictions and address both resource and data scarcity limitations. This approach could achieve faster inference times, while keeping similar accuracy outcomes compared to employing time-consuming multiple sequence alignment schemes, akin to its successful application in protein structure prediction. Herein, we evaluate the suitability of currently available pretrained nucleic acid language models (RNABERT, ERNIE-RNA, RNA Foundational Model (RNA-FM), RiboNucleic Acid Language Model (RiNALMo), and DNABERT) to predict secondary and tertiary RNA structures. We demonstrate that current nucleic acid language models do not effectively capture structural information, mainly due to architectural constraints.
RNA具有超越遗传信息传递的功能意义。非编码RNA的功能作用可通过其三级和二级结构介导，因此，预测RNA结构对于释放其在诊断和治疗中的应用具有巨大潜力。然而，预测RNA的三维（3D）结构仍然具有挑战性。在自然语言处理和大语言模型（LLM）的背景下应用人工智能技术，可以将进化信息纳入RNA 3D结构预测，并解决资源和数据稀缺的限制。与采用耗时的多序列比对方案相比，这种方法可以实现更快的推理时间，同时保持相似的准确性结果，类似于其在蛋白质结构预测中的成功应用。在此，我们评估了目前可用的预训练核酸语言模型（RNABERT、ERNIE-RNA、RNA基础模型（RNA-FM）、核糖核酸语言模型（RiNALMo）和DNABERT）对预测RNA二级和三级结构的适用性。我们证明，目前的核酸语言模型不能有效地捕捉结构信息，主要是由于架构限制。
