Dina Listov, Casper A. Goverde, Bruno E. Correia, Sarel Jacob Fleishman
{"title":"设计和优化蛋白质功能的机遇与挑战","authors":"Dina Listov, Casper A. Goverde, Bruno E. Correia, Sarel Jacob Fleishman","doi":"10.1038/s41580-024-00718-y","DOIUrl":null,"url":null,"abstract":"The field of protein design has made remarkable progress over the past decade. Historically, the low reliability of purely structure-based design methods limited their application, but recent strategies that combine structure-based and sequence-based calculations, as well as machine learning tools, have dramatically improved protein engineering and design. In this Review, we discuss how these methods have enabled the design of increasingly complex structures and therapeutically relevant activities. Additionally, protein optimization methods have improved the stability and activity of complex eukaryotic proteins. Thanks to their increased reliability, computational design methods have been applied to improve therapeutics and enzymes for green chemistry and have generated vaccine antigens, antivirals and drug-delivery nano-vehicles. Moreover, the high success of design methods reflects an increased understanding of basic rules that govern the relationships among protein sequence, structure and function. However, de novo design is still limited mostly to α-helix bundles, restricting its potential to generate sophisticated enzymes and diverse protein and small-molecule binders. Designing complex protein structures is a challenging but necessary next step if we are to realize our objective of generating new-to-nature activities. Recent combinations of structure-based and sequence-based calculations and machine learning tools have dramatically improved protein engineering and design. Although designing complex protein structures remains challenging, these methods have enabled the design of therapeutically relevant activities, including vaccine antigens, antivirals and drug-delivery nano-vehicles.","PeriodicalId":19051,"journal":{"name":"Nature Reviews Molecular Cell Biology","volume":"25 8","pages":"639-653"},"PeriodicalIF":81.3000,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Opportunities and challenges in design and optimization of protein function\",\"authors\":\"Dina Listov, Casper A. Goverde, Bruno E. Correia, Sarel Jacob Fleishman\",\"doi\":\"10.1038/s41580-024-00718-y\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The field of protein design has made remarkable progress over the past decade. Historically, the low reliability of purely structure-based design methods limited their application, but recent strategies that combine structure-based and sequence-based calculations, as well as machine learning tools, have dramatically improved protein engineering and design. In this Review, we discuss how these methods have enabled the design of increasingly complex structures and therapeutically relevant activities. Additionally, protein optimization methods have improved the stability and activity of complex eukaryotic proteins. Thanks to their increased reliability, computational design methods have been applied to improve therapeutics and enzymes for green chemistry and have generated vaccine antigens, antivirals and drug-delivery nano-vehicles. Moreover, the high success of design methods reflects an increased understanding of basic rules that govern the relationships among protein sequence, structure and function. However, de novo design is still limited mostly to α-helix bundles, restricting its potential to generate sophisticated enzymes and diverse protein and small-molecule binders. Designing complex protein structures is a challenging but necessary next step if we are to realize our objective of generating new-to-nature activities. Recent combinations of structure-based and sequence-based calculations and machine learning tools have dramatically improved protein engineering and design. Although designing complex protein structures remains challenging, these methods have enabled the design of therapeutically relevant activities, including vaccine antigens, antivirals and drug-delivery nano-vehicles.\",\"PeriodicalId\":19051,\"journal\":{\"name\":\"Nature Reviews Molecular Cell Biology\",\"volume\":\"25 8\",\"pages\":\"639-653\"},\"PeriodicalIF\":81.3000,\"publicationDate\":\"2024-04-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Nature Reviews Molecular Cell Biology\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://www.nature.com/articles/s41580-024-00718-y\",\"RegionNum\":1,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CELL BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nature Reviews Molecular Cell Biology","FirstCategoryId":"99","ListUrlMain":"https://www.nature.com/articles/s41580-024-00718-y","RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CELL BIOLOGY","Score":null,"Total":0}
Opportunities and challenges in design and optimization of protein function
The field of protein design has made remarkable progress over the past decade. Historically, the low reliability of purely structure-based design methods limited their application, but recent strategies that combine structure-based and sequence-based calculations, as well as machine learning tools, have dramatically improved protein engineering and design. In this Review, we discuss how these methods have enabled the design of increasingly complex structures and therapeutically relevant activities. Additionally, protein optimization methods have improved the stability and activity of complex eukaryotic proteins. Thanks to their increased reliability, computational design methods have been applied to improve therapeutics and enzymes for green chemistry and have generated vaccine antigens, antivirals and drug-delivery nano-vehicles. Moreover, the high success of design methods reflects an increased understanding of basic rules that govern the relationships among protein sequence, structure and function. However, de novo design is still limited mostly to α-helix bundles, restricting its potential to generate sophisticated enzymes and diverse protein and small-molecule binders. Designing complex protein structures is a challenging but necessary next step if we are to realize our objective of generating new-to-nature activities. Recent combinations of structure-based and sequence-based calculations and machine learning tools have dramatically improved protein engineering and design. Although designing complex protein structures remains challenging, these methods have enabled the design of therapeutically relevant activities, including vaccine antigens, antivirals and drug-delivery nano-vehicles.
期刊介绍:
Nature Reviews Molecular Cell Biology is a prestigious journal that aims to be the primary source of reviews and commentaries for the scientific communities it serves. The journal strives to publish articles that are authoritative, accessible, and enriched with easily understandable figures, tables, and other display items. The goal is to provide an unparalleled service to authors, referees, and readers, and the journal works diligently to maximize the usefulness and impact of each article. Nature Reviews Molecular Cell Biology publishes a variety of article types, including Reviews, Perspectives, Comments, and Research Highlights, all of which are relevant to molecular and cell biologists. The journal's broad scope ensures that the articles it publishes reach the widest possible audience.