[Intelligent mining, engineering, and de novo design of proteins].

Q4 Biochemistry, Genetics and Molecular Biology Sheng wu gong cheng xue bao = Chinese journal of biotechnology Pub Date : 2025-03-25 DOI:10.13345/j.cjb.240629
Cui Liu, Zhenkun Shi, Hongwu Ma, Xiaoping Liao
{"title":"[Intelligent mining, engineering, and <i>de novo</i> design of proteins].","authors":"Cui Liu, Zhenkun Shi, Hongwu Ma, Xiaoping Liao","doi":"10.13345/j.cjb.240629","DOIUrl":null,"url":null,"abstract":"<p><p>Natural components serve the survival instincts of cells that are obtained through long-term evolution, while they often fail to meet the demands of engineered cells for efficiently performing biological functions in special industrial environments. Enzymes, as biological catalysts, play a key role in biosynthetic pathways, significantly enhancing the rate and selectivity of biochemical reactions. However, the catalytic efficiency, stability, substrate specificity, and tolerance of natural enzymes often fall short of industrial production requirements. Therefore, exploring and modifying enzymes to suit specific biomanufacturing processes has become crucial. In recent years, artificial intelligence (AI) has played an increasingly important role in the discovery, evaluation, engineering, and <i>de novo</i> design of proteins. AI can accelerate the discovery and optimization of proteins by analyzing large amounts of bioinformatics data and predicting protein functions and characteristics by machine learning and deep learning algorithms. Moreover, AI can assist researchers in designing new protein structures by simulating and predicting their performance under different conditions, providing guidance for protein design. This paper reviews the latest research advances in protein discovery, evaluation, engineering, and <i>de novo</i> design for biomanufacturing and explores the hot topics, challenges, and emerging technical methods in this field, aiming to provide guidance and inspiration for researchers in related fields.</p>","PeriodicalId":21778,"journal":{"name":"Sheng wu gong cheng xue bao = Chinese journal of biotechnology","volume":"41 3","pages":"993-1010"},"PeriodicalIF":0.0000,"publicationDate":"2025-03-25","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.240629","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

Abstract

Natural components serve the survival instincts of cells that are obtained through long-term evolution, while they often fail to meet the demands of engineered cells for efficiently performing biological functions in special industrial environments. Enzymes, as biological catalysts, play a key role in biosynthetic pathways, significantly enhancing the rate and selectivity of biochemical reactions. However, the catalytic efficiency, stability, substrate specificity, and tolerance of natural enzymes often fall short of industrial production requirements. Therefore, exploring and modifying enzymes to suit specific biomanufacturing processes has become crucial. In recent years, artificial intelligence (AI) has played an increasingly important role in the discovery, evaluation, engineering, and de novo design of proteins. AI can accelerate the discovery and optimization of proteins by analyzing large amounts of bioinformatics data and predicting protein functions and characteristics by machine learning and deep learning algorithms. Moreover, AI can assist researchers in designing new protein structures by simulating and predicting their performance under different conditions, providing guidance for protein design. This paper reviews the latest research advances in protein discovery, evaluation, engineering, and de novo design for biomanufacturing and explores the hot topics, challenges, and emerging technical methods in this field, aiming to provide guidance and inspiration for researchers in related fields.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
[蛋白质的智能挖掘、工程和从头设计]。
天然成分服务于细胞通过长期进化获得的生存本能,而它们往往不能满足工程细胞在特殊工业环境中有效执行生物功能的要求。酶作为生物催化剂,在生物合成途径中起着关键作用,显著提高了生物化学反应的速率和选择性。然而,天然酶的催化效率、稳定性、底物特异性和耐受性往往达不到工业生产的要求。因此,探索和修饰酶以适应特定的生物制造过程变得至关重要。近年来,人工智能(AI)在蛋白质的发现、评估、工程和从头设计中发挥着越来越重要的作用。人工智能可以通过分析大量生物信息学数据,通过机器学习和深度学习算法预测蛋白质的功能和特性,加速蛋白质的发现和优化。此外,人工智能可以通过模拟和预测蛋白质在不同条件下的性能,帮助研究人员设计新的蛋白质结构,为蛋白质设计提供指导。本文综述了生物制造中蛋白质发现、评价、工程和从头设计的最新研究进展,探讨了该领域的热点、挑战和新兴技术方法,旨在为相关领域的研究人员提供指导和启发。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 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.
期刊最新文献
[Enhancement of monensin production by metabolic engineering of the redox cofactor supply in Streptomyces cinnamonensis]. [Exploration and practice of integrating "dual carbon" concept into the teaching of Microbiology]. [Expression of human nerve growth factor in tobacco BY-2 cells]. [Expression of recombinant fibronectin FNIII8-10 in Pichia pastoris and fermentation process optimization]. [Purification, characterization and substrate selectivity analysis of transaminase from marine actinomycetes].
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1