| アイテムタイプ |
itemtype_ver1(1) |
| 公開日 |
2024-07-04 |
| タイトル |
|
|
タイトル |
Automatic generation of functional peptides with desired bioactivity and membrane permeability using Bayesian optimization |
|
言語 |
en |
| 著者 |
Fukunaga, Itsuki
Matsukiyo, Yuki
Kaitoh, Kazuma
Yamanishi, Yoshihiro
|
| アクセス権 |
|
|
アクセス権 |
open access |
|
アクセス権URI |
http://purl.org/coar/access_right/c_abf2 |
| 権利 |
|
|
権利情報 |
"This is the peer reviewed version of the following article: [I. Fukunaga, Y. Matsukiyo, K. Kaitoh, Y. Yamanishi, Molecular Informatics 2024, 43, e202300148. https://doi.org/10.1002/minf.202300148], which has been published in final form at [https://doi.org/10.1002/minf.202300148]. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited." |
|
言語 |
en |
| 内容記述 |
|
|
内容記述タイプ |
Abstract |
|
内容記述 |
Peptides are potentially useful modalities of drugs; however, cell membrane permeability is an obstacle in peptide drug discovery. The identification of bioactive peptides for a therapeutic target is also challenging because of the huge amino acid sequence patterns of peptides. In this study, we propose a novel computational method, PEptide generation system using Neural network Trained on Amino acid sequence data and Gaussian process-based optimizatiON (PENTAGON), to automatically generate new peptides with desired bioactivity and cell membrane permeability. In the algorithm, we mapped peptide amino acid sequences onto the latent space constructed using a variational autoencoder and searched for peptides with desired bioactivity and cell membrane permeability using Bayesian optimization. We used our proposed method to generate peptides with cell membrane permeability and bioactivity for each of the nine therapeutic targets, such as the estrogen receptor (ER). Our proposed method outperformed a previously developed peptide generator in terms of similarity to known active peptide sequences and the length of generated peptide sequences. |
|
言語 |
en |
| 出版者 |
|
|
出版者 |
Wiley |
|
言語 |
en |
| 言語 |
|
|
言語 |
eng |
| 資源タイプ |
|
|
資源タイプresource |
http://purl.org/coar/resource_type/c_6501 |
|
タイプ |
journal article |
| 出版タイプ |
|
|
出版タイプ |
AM |
|
出版タイプResource |
http://purl.org/coar/version/c_ab4af688f83e57aa |
| 関連情報 |
|
|
関連タイプ |
isVersionOf |
|
|
識別子タイプ |
DOI |
|
|
関連識別子 |
https://doi.org/10.1002/minf.202300148 |
| 収録物識別子 |
|
|
収録物識別子タイプ |
PISSN |
|
収録物識別子 |
1868-1743 |
| 書誌情報 |
en : Molecular Informatics
巻 43,
号 4,
p. e202300148,
発行日 2024-04
|
| ファイル公開日 |
|
|
日付 |
2025-04-01 |
|
日付タイプ |
Available |
fukunaga_MolInform2024_accepted.pdf (1.1 MB)
