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De novo Design of G Protein-Coupled Receptor 40 Peptide Agonists for Type 2 Diabetes Mellitus Based on Artificial Intelligence and Site-Directed Mutagenesis

G protein-coupled receptor 40 (GPR40), one of the G protein-coupled receptors that are available to sense glucose metabolism, is an attractive target for the treatment of type 2 diabetes mellitus (T2DM). Despite many efforts having been made to discover small-molecule agonists, there is limited rese...

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Autores principales: Chen, Xu, Chen, Zhidong, Xu, Daiyun, Lyu, Yonghui, Li, Yongxiao, Li, Shengbin, Wang, Junqing, Wang, Zhe
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8236607/
https://www.ncbi.nlm.nih.gov/pubmed/34195182
http://dx.doi.org/10.3389/fbioe.2021.694100
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author Chen, Xu
Chen, Zhidong
Xu, Daiyun
Lyu, Yonghui
Li, Yongxiao
Li, Shengbin
Wang, Junqing
Wang, Zhe
author_facet Chen, Xu
Chen, Zhidong
Xu, Daiyun
Lyu, Yonghui
Li, Yongxiao
Li, Shengbin
Wang, Junqing
Wang, Zhe
author_sort Chen, Xu
collection PubMed
description G protein-coupled receptor 40 (GPR40), one of the G protein-coupled receptors that are available to sense glucose metabolism, is an attractive target for the treatment of type 2 diabetes mellitus (T2DM). Despite many efforts having been made to discover small-molecule agonists, there is limited research focus on developing peptides acting as GPR40 agonists to treat T2DM. Here, we propose a novel strategy for peptide design to generate and determine potential peptide agonists against GPR40 efficiently. A molecular fingerprint similarity (MFS) model combined with a deep neural network (DNN) and convolutional neural network was applied to predict the activity of peptides constructed by unnatural amino acids (UAAs). Site-directed mutagenesis (SDM) further optimized the peptides to form specific favorable interactions, and subsequent flexible docking showed the details of the binding mechanism between peptides and GPR40. Molecular dynamics (MD) simulations further verified the stability of the peptide–protein complex. The R-square of the machine learning model on the training set and the test set reached 0.87 and 0.75, respectively; and the three candidate peptides showed excellent performance. The strategy based on machine learning and SDM successfully searched for an optimal design with desirable activity comparable with the model agonist in phase III clinical trials.
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spelling pubmed-82366072021-06-29 De novo Design of G Protein-Coupled Receptor 40 Peptide Agonists for Type 2 Diabetes Mellitus Based on Artificial Intelligence and Site-Directed Mutagenesis Chen, Xu Chen, Zhidong Xu, Daiyun Lyu, Yonghui Li, Yongxiao Li, Shengbin Wang, Junqing Wang, Zhe Front Bioeng Biotechnol Bioengineering and Biotechnology G protein-coupled receptor 40 (GPR40), one of the G protein-coupled receptors that are available to sense glucose metabolism, is an attractive target for the treatment of type 2 diabetes mellitus (T2DM). Despite many efforts having been made to discover small-molecule agonists, there is limited research focus on developing peptides acting as GPR40 agonists to treat T2DM. Here, we propose a novel strategy for peptide design to generate and determine potential peptide agonists against GPR40 efficiently. A molecular fingerprint similarity (MFS) model combined with a deep neural network (DNN) and convolutional neural network was applied to predict the activity of peptides constructed by unnatural amino acids (UAAs). Site-directed mutagenesis (SDM) further optimized the peptides to form specific favorable interactions, and subsequent flexible docking showed the details of the binding mechanism between peptides and GPR40. Molecular dynamics (MD) simulations further verified the stability of the peptide–protein complex. The R-square of the machine learning model on the training set and the test set reached 0.87 and 0.75, respectively; and the three candidate peptides showed excellent performance. The strategy based on machine learning and SDM successfully searched for an optimal design with desirable activity comparable with the model agonist in phase III clinical trials. Frontiers Media S.A. 2021-06-14 /pmc/articles/PMC8236607/ /pubmed/34195182 http://dx.doi.org/10.3389/fbioe.2021.694100 Text en Copyright © 2021 Chen, Chen, Xu, Lyu, Li, Li, Wang and Wang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Bioengineering and Biotechnology
Chen, Xu
Chen, Zhidong
Xu, Daiyun
Lyu, Yonghui
Li, Yongxiao
Li, Shengbin
Wang, Junqing
Wang, Zhe
De novo Design of G Protein-Coupled Receptor 40 Peptide Agonists for Type 2 Diabetes Mellitus Based on Artificial Intelligence and Site-Directed Mutagenesis
title De novo Design of G Protein-Coupled Receptor 40 Peptide Agonists for Type 2 Diabetes Mellitus Based on Artificial Intelligence and Site-Directed Mutagenesis
title_full De novo Design of G Protein-Coupled Receptor 40 Peptide Agonists for Type 2 Diabetes Mellitus Based on Artificial Intelligence and Site-Directed Mutagenesis
title_fullStr De novo Design of G Protein-Coupled Receptor 40 Peptide Agonists for Type 2 Diabetes Mellitus Based on Artificial Intelligence and Site-Directed Mutagenesis
title_full_unstemmed De novo Design of G Protein-Coupled Receptor 40 Peptide Agonists for Type 2 Diabetes Mellitus Based on Artificial Intelligence and Site-Directed Mutagenesis
title_short De novo Design of G Protein-Coupled Receptor 40 Peptide Agonists for Type 2 Diabetes Mellitus Based on Artificial Intelligence and Site-Directed Mutagenesis
title_sort de novo design of g protein-coupled receptor 40 peptide agonists for type 2 diabetes mellitus based on artificial intelligence and site-directed mutagenesis
topic Bioengineering and Biotechnology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8236607/
https://www.ncbi.nlm.nih.gov/pubmed/34195182
http://dx.doi.org/10.3389/fbioe.2021.694100
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