Deep generative design of RNA aptamers using structural predictions.

Journal: Nature computational science
PMID: 39506080

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.

Authors

  • Felix Wong
    Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • Dongchen He
    Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China.
  • Aarti Krishnan
    Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • Liang Hong
    Department of Computer Science and Engineering, The Chinese University of Hong Kong, Sha Tin, Hong Kong SAR 999077, China.
  • Alexander Z Wang
    Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • Jiuming Wang
    Department of Computer Science and Engineering, The Chinese University of Hong Kong, Sha Tin, Hong Kong SAR 999077, China.
  • Zhihang Hu
  • Satotaka Omori
    Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • Alicia Li
    Integrated Biosciences, San Carlos, CA, USA.
  • Jiahua Rao
    School of Data and Computer Science , Sun Yat-sen University , Guangzhou 510006 , China.
  • Qinze Yu
    Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China.
  • Wengong Jin
    Computer Science and Artificial Intelligence Laboratory , Massachusetts Institute of Technology , 77 Massachusetts Avenue , Cambridge , MA 02139 , USA . Email: regina@csail.mit.edu.
  • Tianqing Zhang
    Institute for Medical Engineering & Science and Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
  • Katherine Ilia
    Institute for Medical Engineering & Science and Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
  • Jack X Chen
    Institute for Medical Engineering & Science and Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
  • Shuangjia Zheng
    Research Center for Drug Discovery, School of Pharmaceutical Sciences , Sun Yat-sen University , 132 East Circle at University City , Guangzhou 510006 , China.
  • Irwin King
    Shenzhen Key Laboratory of Rich Media Big Data Analytics and Application, Shenzhen Research Institute, The Chinese University of Hong Kong, Hong Kong; Computer Science & Engineering, The Chinese University of Hong Kong, Hong Kong.
  • Yu Li
    Department of Public Health, Shihezi University School of Medicine, 832000, China.
  • James J Collins
    Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA.

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