[人工智能时代的全新蛋白质设计]。

Q4 Biochemistry, Genetics and Molecular Biology
Nan Liu, Xiaocheng Jin, Chongzhou Yang, Ziyang Wang, Xiaoping Min, Shengxiang Ge
{"title":"[人工智能时代的全新蛋白质设计]。","authors":"Nan Liu, Xiaocheng Jin, Chongzhou Yang, Ziyang Wang, Xiaoping Min, Shengxiang Ge","doi":"10.13345/j.cjb.240087","DOIUrl":null,"url":null,"abstract":"<p><p>Proteins with specific functions and characteristics play a crucial role in biomedicine and nanotechnology. <i>De novo</i> protein design enables the customization of sequences to produce proteins with desired structures that do not exist in the nature. In recent years, with the rapid development of artificial intelligence (AI), deep learning-based generative models have increasingly become powerful tools, enabling the design of functional proteins with atomic-level precision. This article provides an overview of the evolution of <i>de novo</i> protein design, with focus on the latest algorithmic models, and then analyzes existing challenges such as low design success rates, insufficient accuracy, and dependence on experimental validation. Furthermore, this article discusses the future trends in protein design, aiming to provide insights for researchers and practitioners in this field.</p>","PeriodicalId":21778,"journal":{"name":"Sheng wu gong cheng xue bao = Chinese journal of biotechnology","volume":"40 11","pages":"3912-3929"},"PeriodicalIF":0.0000,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"[<i>De novo</i> protein design in the age of artificial intelligence].\",\"authors\":\"Nan Liu, Xiaocheng Jin, Chongzhou Yang, Ziyang Wang, Xiaoping Min, Shengxiang Ge\",\"doi\":\"10.13345/j.cjb.240087\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Proteins with specific functions and characteristics play a crucial role in biomedicine and nanotechnology. <i>De novo</i> protein design enables the customization of sequences to produce proteins with desired structures that do not exist in the nature. In recent years, with the rapid development of artificial intelligence (AI), deep learning-based generative models have increasingly become powerful tools, enabling the design of functional proteins with atomic-level precision. This article provides an overview of the evolution of <i>de novo</i> protein design, with focus on the latest algorithmic models, and then analyzes existing challenges such as low design success rates, insufficient accuracy, and dependence on experimental validation. Furthermore, this article discusses the future trends in protein design, aiming to provide insights for researchers and practitioners in this field.</p>\",\"PeriodicalId\":21778,\"journal\":{\"name\":\"Sheng wu gong cheng xue bao = Chinese journal of biotechnology\",\"volume\":\"40 11\",\"pages\":\"3912-3929\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-11-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Sheng wu gong cheng xue bao = Chinese journal of biotechnology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.13345/j.cjb.240087\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"Biochemistry, Genetics and Molecular Biology\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Sheng wu gong cheng xue bao = Chinese journal of biotechnology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.13345/j.cjb.240087","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Biochemistry, Genetics and Molecular Biology","Score":null,"Total":0}
引用次数: 0

摘要

具有特定功能和特性的蛋白质在生物医学和纳米技术中发挥着至关重要的作用。全新蛋白质设计可以定制序列,生产出具有所需结构的蛋白质,而这种蛋白质在自然界中并不存在。近年来,随着人工智能(AI)的快速发展,基于深度学习的生成模型日益成为强大的工具,使原子级精度的功能蛋白质设计成为可能。本文概述了从头蛋白质设计的演变过程,重点介绍了最新的算法模型,然后分析了现有的挑战,如设计成功率低、准确性不足以及对实验验证的依赖等。此外,本文还讨论了蛋白质设计的未来趋势,旨在为该领域的研究人员和从业人员提供见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
[De novo protein design in the age of artificial intelligence].

Proteins with specific functions and characteristics play a crucial role in biomedicine and nanotechnology. De novo protein design enables the customization of sequences to produce proteins with desired structures that do not exist in the nature. In recent years, with the rapid development of artificial intelligence (AI), deep learning-based generative models have increasingly become powerful tools, enabling the design of functional proteins with atomic-level precision. This article provides an overview of the evolution of de novo protein design, with focus on the latest algorithmic models, and then analyzes existing challenges such as low design success rates, insufficient accuracy, and dependence on experimental validation. Furthermore, this article discusses the future trends in protein design, aiming to provide insights for researchers and practitioners in this field.

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
Sheng wu gong cheng xue bao = Chinese journal of biotechnology
Sheng wu gong cheng xue bao = Chinese journal of biotechnology Biochemistry, Genetics and Molecular Biology-Biotechnology
CiteScore
1.50
自引率
0.00%
发文量
298
期刊介绍: Chinese Journal of Biotechnology (Chinese edition) , sponsored by the Institute of Microbiology, Chinese Academy of Sciences and the Chinese Society for Microbiology, is a peer-reviewed international journal. The journal is cited by many scientific databases , such as Chemical Abstract (CA), Biology Abstract (BA), MEDLINE, Russian Digest , Chinese Scientific Citation Index (CSCI), Chinese Journal Citation Report (CJCR), and Chinese Academic Journal (CD version). The Journal publishes new discoveries, techniques and developments in genetic engineering, cell engineering, enzyme engineering, biochemical engineering, tissue engineering, bioinformatics, biochips and other fields of biotechnology.
×
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:481959085
Book学术官方微信