酶序列优化通过Gromov-Wasserstein自动编码器集成MSA技术。

IF 5.4 2区 医学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY
Xuze Wang, Yangyang Li, Xiancong Hou, Hao Liu
{"title":"酶序列优化通过Gromov-Wasserstein自动编码器集成MSA技术。","authors":"Xuze Wang, Yangyang Li, Xiancong Hou, Hao Liu","doi":"10.1080/14756366.2025.2524742","DOIUrl":null,"url":null,"abstract":"<p><p>Enzyme sequence design has always been a challenging task, particularly in optimising key properties such as enzyme solubility, stability, and activity. This study proposes an innovative approach by utilising a variational autoencoder (VAE) model integrated with the Gromov-Wasserstein (GW) distance for enzyme sequence optimisation. The GWAE model improves representation learning by using the GW distance, thereby generating functional variants with desired characteristics. We also introduce an innovative enzyme dataset construction method that incorporates multiple sequence alignment (MSA) techniques to address sequence length discrepancies, enhancing the accuracy of the optimisation process. Experimental results show that the GWAE model outperforms the traditional VAE on multiple metrics. The generated enzyme sequences demonstrate superior solubility, stability, and hydrophobicity. Additionally, by integrating AlphaFold3 for structural prediction, we verify the structural stability of the generated sequences, further enhancing their practical applicability.</p>","PeriodicalId":15769,"journal":{"name":"Journal of Enzyme Inhibition and Medicinal Chemistry","volume":"40 1","pages":"2524742"},"PeriodicalIF":5.4000,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12231317/pdf/","citationCount":"0","resultStr":"{\"title\":\"Enzyme sequence optimisation via Gromov-Wasserstein Autoencoders integrating MSA techniques.\",\"authors\":\"Xuze Wang, Yangyang Li, Xiancong Hou, Hao Liu\",\"doi\":\"10.1080/14756366.2025.2524742\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Enzyme sequence design has always been a challenging task, particularly in optimising key properties such as enzyme solubility, stability, and activity. This study proposes an innovative approach by utilising a variational autoencoder (VAE) model integrated with the Gromov-Wasserstein (GW) distance for enzyme sequence optimisation. The GWAE model improves representation learning by using the GW distance, thereby generating functional variants with desired characteristics. We also introduce an innovative enzyme dataset construction method that incorporates multiple sequence alignment (MSA) techniques to address sequence length discrepancies, enhancing the accuracy of the optimisation process. Experimental results show that the GWAE model outperforms the traditional VAE on multiple metrics. The generated enzyme sequences demonstrate superior solubility, stability, and hydrophobicity. Additionally, by integrating AlphaFold3 for structural prediction, we verify the structural stability of the generated sequences, further enhancing their practical applicability.</p>\",\"PeriodicalId\":15769,\"journal\":{\"name\":\"Journal of Enzyme Inhibition and Medicinal Chemistry\",\"volume\":\"40 1\",\"pages\":\"2524742\"},\"PeriodicalIF\":5.4000,\"publicationDate\":\"2025-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12231317/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Enzyme Inhibition and Medicinal Chemistry\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1080/14756366.2025.2524742\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/7/3 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"BIOCHEMISTRY & MOLECULAR BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Enzyme Inhibition and Medicinal Chemistry","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1080/14756366.2025.2524742","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/7/3 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
引用次数: 0

摘要

酶序列设计一直是一项具有挑战性的任务,特别是在优化酶的溶解度、稳定性和活性等关键特性方面。本研究提出了一种创新的方法,利用结合Gromov-Wasserstein (GW)距离的变分自编码器(VAE)模型进行酶序列优化。GWAE模型通过使用GW距离来改进表征学习,从而生成具有所需特征的函数变体。我们还介绍了一种创新的酶数据集构建方法,该方法结合了多序列比对(MSA)技术来解决序列长度差异,提高了优化过程的准确性。实验结果表明,GWAE模型在多个指标上优于传统的VAE。所生成的酶序列表现出优异的溶解度、稳定性和疏水性。此外,通过整合AlphaFold3进行结构预测,验证了生成序列的结构稳定性,进一步增强了其实用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Enzyme sequence optimisation via Gromov-Wasserstein Autoencoders integrating MSA techniques.

Enzyme sequence optimisation via Gromov-Wasserstein Autoencoders integrating MSA techniques.

Enzyme sequence optimisation via Gromov-Wasserstein Autoencoders integrating MSA techniques.

Enzyme sequence optimisation via Gromov-Wasserstein Autoencoders integrating MSA techniques.

Enzyme sequence design has always been a challenging task, particularly in optimising key properties such as enzyme solubility, stability, and activity. This study proposes an innovative approach by utilising a variational autoencoder (VAE) model integrated with the Gromov-Wasserstein (GW) distance for enzyme sequence optimisation. The GWAE model improves representation learning by using the GW distance, thereby generating functional variants with desired characteristics. We also introduce an innovative enzyme dataset construction method that incorporates multiple sequence alignment (MSA) techniques to address sequence length discrepancies, enhancing the accuracy of the optimisation process. Experimental results show that the GWAE model outperforms the traditional VAE on multiple metrics. The generated enzyme sequences demonstrate superior solubility, stability, and hydrophobicity. Additionally, by integrating AlphaFold3 for structural prediction, we verify the structural stability of the generated sequences, further enhancing their practical applicability.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
10.30
自引率
10.70%
发文量
195
审稿时长
4-8 weeks
期刊介绍: Journal of Enzyme Inhibition and Medicinal Chemistry publishes open access research on enzyme inhibitors, inhibitory processes, and agonist/antagonist receptor interactions in the development of medicinal and anti-cancer agents. Journal of Enzyme Inhibition and Medicinal Chemistry aims to provide an international and interdisciplinary platform for the latest findings in enzyme inhibition research. The journal’s focus includes current developments in: Enzymology; Cell biology; Chemical biology; Microbiology; Physiology; Pharmacology leading to drug design; Molecular recognition processes; Distribution and metabolism of biologically active compounds.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信