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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Royal Society of Chemistry
2023
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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. |
format | Online Article Text |
id | pubmed-10231333 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | The Royal Society of Chemistry |
record_format | MEDLINE/PubMed |
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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