{"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.
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.