Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Institute for Medical Engineering & Science and Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
Nat Comput Sci. 2024 Nov;4(11):829-839. doi: 10.1038/s43588-024-00720-6. Epub 2024 Nov 6.
RNAs represent a class of programmable biomolecules capable of performing diverse biological functions. Recent studies have developed accurate RNA three-dimensional structure prediction methods, which may enable new RNAs to be designed in a structure-guided manner. Here, we develop a structure-to-sequence deep learning platform for the de novo generative design of RNA aptamers. We show that our approach can design RNA aptamers that are predicted to be structurally similar, yet sequence dissimilar, to known light-up aptamers that fluoresce in the presence of small molecules. We experimentally validate several generated RNA aptamers to have fluorescent activity, show that these aptamers can be optimized for activity in silico, and find that they exhibit a mechanism of fluorescence similar to that of known light-up aptamers. Our results demonstrate how structural predictions can guide the targeted and resource-efficient design of new RNA sequences.
RNAs 是一类可编程的生物分子，能够发挥多种生物学功能。最近的研究开发了精确的 RNA 三维结构预测方法，这可能使新的 RNA 能够以结构为导向进行设计。在这里，我们开发了一个基于结构到序列的深度学习平台，用于从头生成 RNA 适体的设计。我们表明，我们的方法可以设计 RNA 适体，这些适体在结构上与小分子存在时荧光增强的已知亮适体相似，但在序列上不同。我们通过实验验证了几个生成的 RNA 适体具有荧光活性，表明这些适体可以在计算机上进行优化以提高活性，并发现它们表现出与已知亮适体相似的荧光机制。我们的结果表明，结构预测如何能够指导新 RNA 序列的有针对性和资源高效的设计。
