{"title":"酶工程,选择和设计的机器学习。","authors":"Ryan Feehan, Daniel Montezano, Joanna S G Slusky","doi":"10.1093/protein/gzab019","DOIUrl":null,"url":null,"abstract":"<p><p>Machine learning is a useful computational tool for large and complex tasks such as those in the field of enzyme engineering, selection and design. In this review, we examine enzyme-related applications of machine learning. We start by comparing tools that can identify the function of an enzyme and the site responsible for that function. Then we detail methods for optimizing important experimental properties, such as the enzyme environment and enzyme reactants. We describe recent advances in enzyme systems design and enzyme design itself. Throughout we compare and contrast the data and algorithms used for these tasks to illustrate how the algorithms and data can be best used by future designers.</p>","PeriodicalId":54543,"journal":{"name":"Protein Engineering Design & Selection","volume":null,"pages":null},"PeriodicalIF":2.6000,"publicationDate":"2021-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8299298/pdf/gzab019.pdf","citationCount":"12","resultStr":"{\"title\":\"Machine learning for enzyme engineering, selection and design.\",\"authors\":\"Ryan Feehan, Daniel Montezano, Joanna S G Slusky\",\"doi\":\"10.1093/protein/gzab019\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Machine learning is a useful computational tool for large and complex tasks such as those in the field of enzyme engineering, selection and design. In this review, we examine enzyme-related applications of machine learning. We start by comparing tools that can identify the function of an enzyme and the site responsible for that function. Then we detail methods for optimizing important experimental properties, such as the enzyme environment and enzyme reactants. We describe recent advances in enzyme systems design and enzyme design itself. Throughout we compare and contrast the data and algorithms used for these tasks to illustrate how the algorithms and data can be best used by future designers.</p>\",\"PeriodicalId\":54543,\"journal\":{\"name\":\"Protein Engineering Design & Selection\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2021-02-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8299298/pdf/gzab019.pdf\",\"citationCount\":\"12\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Protein Engineering Design & Selection\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.1093/protein/gzab019\",\"RegionNum\":4,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"BIOCHEMISTRY & MOLECULAR BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Protein Engineering Design & Selection","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1093/protein/gzab019","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
Machine learning for enzyme engineering, selection and design.
Machine learning is a useful computational tool for large and complex tasks such as those in the field of enzyme engineering, selection and design. In this review, we examine enzyme-related applications of machine learning. We start by comparing tools that can identify the function of an enzyme and the site responsible for that function. Then we detail methods for optimizing important experimental properties, such as the enzyme environment and enzyme reactants. We describe recent advances in enzyme systems design and enzyme design itself. Throughout we compare and contrast the data and algorithms used for these tasks to illustrate how the algorithms and data can be best used by future designers.
期刊介绍:
Protein Engineering, Design and Selection (PEDS) publishes high-quality research papers and review articles relevant to the engineering, design and selection of proteins for use in biotechnology and therapy, and for understanding the fundamental link between protein sequence, structure, dynamics, function, and evolution.