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A data-driven sequencer that unveils latent “codons” in synthetic copolymers

The recent emergence of sequence engineering in synthetic copolymers has been innovating polymer materials, where short sequences, hereinafter called “codons” using an analogy from nucleotide triads, play key roles in expressing functions. However, the codon compositions cannot be experimentally det...

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Detalles Bibliográficos
Autores principales: Hibi, Yusuke, Uesaka, Shiho, Naito, Masanobu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society of Chemistry 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10231333/
https://www.ncbi.nlm.nih.gov/pubmed/37265724
http://dx.doi.org/10.1039/d2sc06974a
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author Hibi, Yusuke
Uesaka, Shiho
Naito, Masanobu
author_facet Hibi, Yusuke
Uesaka, Shiho
Naito, Masanobu
author_sort Hibi, Yusuke
collection PubMed
description The recent emergence of sequence engineering in synthetic copolymers has been innovating polymer materials, where short sequences, hereinafter called “codons” using an analogy from nucleotide triads, play key roles in expressing functions. However, the codon compositions cannot be experimentally determined owing to the lack of efficient sequencing methods, hindering the integration of experiments and theories. Herein, we propose a polymer sequencer based on mass spectrometry of pyrolyzed oligomeric fragments. Despite the random fragmentation along copolymer main-chains, the characteristic fragment patterns of the codons are identified and quantified via unsupervised learning of a spectral dataset of random copolymers. The codon complexities increase with their length and monomer component number. Our data-driven approach accommodates the increasing complexities by expanding the dataset; the codon compositions of binary triads, binary pentads and ternary triads are quantifiable with small datasets (N < 100). The sequencer allows describing copolymers with their codon compositions/distributions, facilitating sequence engineering toward innovative polymer materials.
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spelling pubmed-102313332023-06-01 A data-driven sequencer that unveils latent “codons” in synthetic copolymers Hibi, Yusuke Uesaka, Shiho Naito, Masanobu Chem Sci Chemistry The recent emergence of sequence engineering in synthetic copolymers has been innovating polymer materials, where short sequences, hereinafter called “codons” using an analogy from nucleotide triads, play key roles in expressing functions. However, the codon compositions cannot be experimentally determined owing to the lack of efficient sequencing methods, hindering the integration of experiments and theories. Herein, we propose a polymer sequencer based on mass spectrometry of pyrolyzed oligomeric fragments. Despite the random fragmentation along copolymer main-chains, the characteristic fragment patterns of the codons are identified and quantified via unsupervised learning of a spectral dataset of random copolymers. The codon complexities increase with their length and monomer component number. Our data-driven approach accommodates the increasing complexities by expanding the dataset; the codon compositions of binary triads, binary pentads and ternary triads are quantifiable with small datasets (N < 100). The sequencer allows describing copolymers with their codon compositions/distributions, facilitating sequence engineering toward innovative polymer materials. The Royal Society of Chemistry 2023-03-20 /pmc/articles/PMC10231333/ /pubmed/37265724 http://dx.doi.org/10.1039/d2sc06974a Text en This journal is © The Royal Society of Chemistry https://creativecommons.org/licenses/by-nc/3.0/
spellingShingle Chemistry
Hibi, Yusuke
Uesaka, Shiho
Naito, Masanobu
A data-driven sequencer that unveils latent “codons” in synthetic copolymers
title A data-driven sequencer that unveils latent “codons” in synthetic copolymers
title_full A data-driven sequencer that unveils latent “codons” in synthetic copolymers
title_fullStr A data-driven sequencer that unveils latent “codons” in synthetic copolymers
title_full_unstemmed A data-driven sequencer that unveils latent “codons” in synthetic copolymers
title_short A data-driven sequencer that unveils latent “codons” in synthetic copolymers
title_sort data-driven sequencer that unveils latent “codons” in synthetic copolymers
topic Chemistry
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10231333/
https://www.ncbi.nlm.nih.gov/pubmed/37265724
http://dx.doi.org/10.1039/d2sc06974a
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