Generative aptamer discovery using RaptGen

Natsuki Iwano, Tatsuo Adachi, Kazuteru Aoki, Yoshikazu Nakamura, Michiaki Hamada*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

Nucleic acid aptamers are generated by an in vitro molecular evolution method known as systematic evolution of ligands by exponential enrichment (SELEX). Various candidates are limited by actual sequencing data from an experiment. Here we developed RaptGen, which is a variational autoencoder for in silico aptamer generation. RaptGen exploits a profile hidden Markov model decoder to represent motif sequences effectively. We showed that RaptGen embedded simulation sequence data into low-dimensional latent space on the basis of motif information. We also performed sequence embedding using two independent SELEX datasets. RaptGen successfully generated aptamers from the latent space even though they were not included in high-throughput sequencing. RaptGen could also generate a truncated aptamer with a short learning model. We demonstrated that RaptGen could be applied to activity-guided aptamer generation according to Bayesian optimization. We concluded that a generative method by RaptGen and latent representation are useful for aptamer discovery.

Original languageEnglish
Pages (from-to)378-386
Number of pages9
JournalNature Computational Science
Volume2
Issue number6
DOIs
Publication statusPublished - 2022 Jun

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Computer Science Applications
  • Computer Networks and Communications

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