蛋白质科学与工程的机器学习。

IF 6.9 2区生物学 Q1 CELL BIOLOGY

Cold Spring Harbor perspectives in biology Pub Date : 2025-05-05 DOI:10.1101/cshperspect.a041877

Peter K Koo, Christian Dallago, Ananthan Nambiar, Kevin K Yang

{"title":"蛋白质科学与工程的机器学习。","authors":"Peter K Koo, Christian Dallago, Ananthan Nambiar, Kevin K Yang","doi":"10.1101/cshperspect.a041877","DOIUrl":null,"url":null,"abstract":"Recent years have seen significant breakthroughs at the intersection of machine learning and protein science. Tools such as AlphaFold have revolutionized protein structure prediction. They are also enabling variant effect prediction and functional annotation of proteins, as well as opening up new possibilities for protein design. However, these technological advances must be balanced with sustainable computing practices.","PeriodicalId":10494,"journal":{"name":"Cold Spring Harbor perspectives in biology","volume":" ","pages":""},"PeriodicalIF":6.9000,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Machine Learning for Protein Science and Engineering.\",\"authors\":\"Peter K Koo, Christian Dallago, Ananthan Nambiar, Kevin K Yang\",\"doi\":\"10.1101/cshperspect.a041877\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Recent years have seen significant breakthroughs at the intersection of machine learning and protein science. Tools such as AlphaFold have revolutionized protein structure prediction. They are also enabling variant effect prediction and functional annotation of proteins, as well as opening up new possibilities for protein design. However, these technological advances must be balanced with sustainable computing practices.\",\"PeriodicalId\":10494,\"journal\":{\"name\":\"Cold Spring Harbor perspectives in biology\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":6.9000,\"publicationDate\":\"2025-05-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cold Spring Harbor perspectives in biology\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.1101/cshperspect.a041877\",\"RegionNum\":2,\"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":"Cold Spring Harbor perspectives in biology","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1101/cshperspect.a041877","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CELL BIOLOGY","Score":null,"Total":0}

引用次数: 0

摘要

近年来，机器学习和蛋白质科学的交叉领域取得了重大突破。像AlphaFold这样的工具已经彻底改变了蛋白质结构预测。它们还使蛋白质的变异效应预测和功能注释成为可能，并为蛋白质设计开辟了新的可能性。然而，这些技术进步必须与可持续计算实践相平衡。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Machine Learning for Protein Science and Engineering.

Recent years have seen significant breakthroughs at the intersection of machine learning and protein science. Tools such as AlphaFold have revolutionized protein structure prediction. They are also enabling variant effect prediction and functional annotation of proteins, as well as opening up new possibilities for protein design. However, these technological advances must be balanced with sustainable computing practices.

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

Cold Spring Harbor perspectives in biology CELL BIOLOGY-

CiteScore

15.00

自引率

1.40%

发文量

审稿时长

3-8 weeks

期刊介绍： Cold Spring Harbor Perspectives in Biology offers a comprehensive platform in the molecular life sciences, featuring reviews that span molecular, cell, and developmental biology, genetics, neuroscience, immunology, cancer biology, and molecular pathology. This online publication provides in-depth insights into various topics, making it a valuable resource for those engaged in diverse aspects of biological research.