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Sequence Design of Random Heteropolymers as Protein Mimics

[Image: see text] Random heteropolymers (RHPs) have been computationally designed and experimentally shown to recapitulate protein-like phase behavior and function. However, unlike proteins, RHP sequences are only statistically defined and cannot be sequenced. Recent developments in reversible-deact...

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Autores principales: Jayapurna, Ivan, Ruan, Zhiyuan, Eres, Marco, Jalagam, Prajna, Jenkins, Spencer, Xu, Ting
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2023
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9930114/
https://www.ncbi.nlm.nih.gov/pubmed/36638823
http://dx.doi.org/10.1021/acs.biomac.2c01036
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author Jayapurna, Ivan
Ruan, Zhiyuan
Eres, Marco
Jalagam, Prajna
Jenkins, Spencer
Xu, Ting
author_facet Jayapurna, Ivan
Ruan, Zhiyuan
Eres, Marco
Jalagam, Prajna
Jenkins, Spencer
Xu, Ting
author_sort Jayapurna, Ivan
collection PubMed
description [Image: see text] Random heteropolymers (RHPs) have been computationally designed and experimentally shown to recapitulate protein-like phase behavior and function. However, unlike proteins, RHP sequences are only statistically defined and cannot be sequenced. Recent developments in reversible-deactivation radical polymerization allowed simulated polymer sequences based on the well-established Mayo–Lewis equation to more accurately reflect ground-truth sequences that are experimentally synthesized. This led to opportunities to perform bioinformatics-inspired analysis on simulated sequences to guide the design, synthesis, and interpretation of RHPs. We compared batches on the order of 10000 simulated RHP sequences that vary by synthetically controllable and measurable RHP characteristics such as chemical heterogeneity and average degree of polymerization. Our analysis spans across 3 levels: segments along a single chain, sequences within a batch, and batch-averaged statistics. We discuss simulator fidelity and highlight the importance of robust segment definition. Examples are presented that demonstrate the use of simulated sequence analysis for in-silico iterative design to mimic protein hydrophobic/hydrophilic segment distributions in RHPs and compare RHP and protein sequence segments to explain experimental results of RHPs that mimic protein function. To facilitate the community use of this workflow, the simulator and analysis modules have been made available through an open source toolkit, the RHPapp.
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spelling pubmed-99301142023-02-16 Sequence Design of Random Heteropolymers as Protein Mimics Jayapurna, Ivan Ruan, Zhiyuan Eres, Marco Jalagam, Prajna Jenkins, Spencer Xu, Ting Biomacromolecules [Image: see text] Random heteropolymers (RHPs) have been computationally designed and experimentally shown to recapitulate protein-like phase behavior and function. However, unlike proteins, RHP sequences are only statistically defined and cannot be sequenced. Recent developments in reversible-deactivation radical polymerization allowed simulated polymer sequences based on the well-established Mayo–Lewis equation to more accurately reflect ground-truth sequences that are experimentally synthesized. This led to opportunities to perform bioinformatics-inspired analysis on simulated sequences to guide the design, synthesis, and interpretation of RHPs. We compared batches on the order of 10000 simulated RHP sequences that vary by synthetically controllable and measurable RHP characteristics such as chemical heterogeneity and average degree of polymerization. Our analysis spans across 3 levels: segments along a single chain, sequences within a batch, and batch-averaged statistics. We discuss simulator fidelity and highlight the importance of robust segment definition. Examples are presented that demonstrate the use of simulated sequence analysis for in-silico iterative design to mimic protein hydrophobic/hydrophilic segment distributions in RHPs and compare RHP and protein sequence segments to explain experimental results of RHPs that mimic protein function. To facilitate the community use of this workflow, the simulator and analysis modules have been made available through an open source toolkit, the RHPapp. American Chemical Society 2023-01-13 /pmc/articles/PMC9930114/ /pubmed/36638823 http://dx.doi.org/10.1021/acs.biomac.2c01036 Text en © 2023 The Authors. Published by American Chemical Society https://creativecommons.org/licenses/by/4.0/Permits the broadest form of re-use including for commercial purposes, provided that author attribution and integrity are maintained (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Jayapurna, Ivan
Ruan, Zhiyuan
Eres, Marco
Jalagam, Prajna
Jenkins, Spencer
Xu, Ting
Sequence Design of Random Heteropolymers as Protein Mimics
title Sequence Design of Random Heteropolymers as Protein Mimics
title_full Sequence Design of Random Heteropolymers as Protein Mimics
title_fullStr Sequence Design of Random Heteropolymers as Protein Mimics
title_full_unstemmed Sequence Design of Random Heteropolymers as Protein Mimics
title_short Sequence Design of Random Heteropolymers as Protein Mimics
title_sort sequence design of random heteropolymers as protein mimics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9930114/
https://www.ncbi.nlm.nih.gov/pubmed/36638823
http://dx.doi.org/10.1021/acs.biomac.2c01036
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