Deep generative design of RNA aptamers using structural predictions

Felix Wong; Dongchen He; Aarti Krishnan; Liang Hong; Alexander Z Wang; Jiuming Wang; Zhihang Hu; Satotaka Omori; Alicia Li; Jiahua Rao; Qinze Yu; Wengong Jin; Tianqing Zhang; Katherine Ilia; Jack X Chen; Shuangjia Zheng; Irwin King; Yu Li; James J Collins

doi:10.1038/s43588-024-00720-6

Deep generative design of RNA aptamers using structural predictions

Nat Comput Sci. 2024 Nov;4(11):829-839. doi: 10.1038/s43588-024-00720-6. Epub 2024 Nov 6.

Authors

Felix Wong^#^{1

2

3}, Dongchen He^#^{4

5}, Aarti Krishnan^{1

2}, Liang Hong⁴, Alexander Z Wang^{1

2}, Jiuming Wang⁴, Zhihang Hu⁴, Satotaka Omori^{1

3}, Alicia Li³, Jiahua Rao⁶, Qinze Yu⁴, Wengong Jin^{7

8}, Tianqing Zhang², Katherine Ilia², Jack X Chen², Shuangjia Zheng⁹, Irwin King⁴, Yu Li^{10

11

12

13

14}, James J Collins^{15

16

17}

Affiliations

¹ Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
² Institute for Medical Engineering & Science and Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
³ Integrated Biosciences, Redwood City, CA, USA.
⁴ Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China.
⁵ Shanghai Artificial Intelligence Laboratory, Shanghai, China.
⁶ School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China.
⁷ Eric and Wendy Schmidt Center, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
⁸ Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA.
⁹ Global Institute of Future Technology, Shanghai Jiao Tong University, Shanghai, China.
¹⁰ Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA. [email protected].
¹¹ Institute for Medical Engineering & Science and Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA. [email protected].
¹² Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China. [email protected].
¹³ The CUHK Shenzhen Research Institute, Shenzhen, China. [email protected].
¹⁴ Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA. [email protected].
¹⁵ Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA. [email protected].
¹⁶ Institute for Medical Engineering & Science and Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA. [email protected].
¹⁷ Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA. [email protected].

^# Contributed equally.

PMID: 39506080
DOI: 10.1038/s43588-024-00720-6

Abstract

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.

MeSH terms

Aptamers, Nucleotide* / chemistry
Computational Biology / methods
Computer Simulation
Deep Learning*
Nucleic Acid Conformation*
RNA / chemistry

Substances

Aptamers, Nucleotide
RNA

Abstract

MeSH terms

Substances

Grants and funding