PLMSearch: Protein language model powers accurate and fast sequence search for remote homology.

IF 14.7 1区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Nature Communications Pub Date : 2024-03-30 DOI:10.1038/s41467-024-46808-5
Wei Liu, Ziye Wang, Ronghui You, Chenghan Xie, Hong Wei, Yi Xiong, Jianyi Yang, Shanfeng Zhu
{"title":"PLMSearch: Protein language model powers accurate and fast sequence search for remote homology.","authors":"Wei Liu, Ziye Wang, Ronghui You, Chenghan Xie, Hong Wei, Yi Xiong, Jianyi Yang, Shanfeng Zhu","doi":"10.1038/s41467-024-46808-5","DOIUrl":null,"url":null,"abstract":"<p><p>Homologous protein search is one of the most commonly used methods for protein annotation and analysis. Compared to structure search, detecting distant evolutionary relationships from sequences alone remains challenging. Here we propose PLMSearch (Protein Language Model), a homologous protein search method with only sequences as input. PLMSearch uses deep representations from a pre-trained protein language model and trains the similarity prediction model with a large number of real structure similarity. This enables PLMSearch to capture the remote homology information concealed behind the sequences. Extensive experimental results show that PLMSearch can search millions of query-target protein pairs in seconds like MMseqs2 while increasing the sensitivity by more than threefold, and is comparable to state-of-the-art structure search methods. In particular, unlike traditional sequence search methods, PLMSearch can recall most remote homology pairs with dissimilar sequences but similar structures. PLMSearch is freely available at https://dmiip.sjtu.edu.cn/PLMSearch .</p>","PeriodicalId":19066,"journal":{"name":"Nature Communications","volume":"15 1","pages":"2775"},"PeriodicalIF":14.7000,"publicationDate":"2024-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10981738/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nature Communications","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.1038/s41467-024-46808-5","RegionNum":1,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 0

Abstract

Homologous protein search is one of the most commonly used methods for protein annotation and analysis. Compared to structure search, detecting distant evolutionary relationships from sequences alone remains challenging. Here we propose PLMSearch (Protein Language Model), a homologous protein search method with only sequences as input. PLMSearch uses deep representations from a pre-trained protein language model and trains the similarity prediction model with a large number of real structure similarity. This enables PLMSearch to capture the remote homology information concealed behind the sequences. Extensive experimental results show that PLMSearch can search millions of query-target protein pairs in seconds like MMseqs2 while increasing the sensitivity by more than threefold, and is comparable to state-of-the-art structure search methods. In particular, unlike traditional sequence search methods, PLMSearch can recall most remote homology pairs with dissimilar sequences but similar structures. PLMSearch is freely available at https://dmiip.sjtu.edu.cn/PLMSearch .

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
PLMSearch:蛋白质语言模型支持精确快速的远程同源序列搜索。
同源蛋白质搜索是蛋白质注释和分析最常用的方法之一。与结构搜索相比,仅从序列检测遥远的进化关系仍然具有挑战性。在此,我们提出了仅以序列为输入的同源蛋白质搜索方法 PLMSearch(蛋白质语言模型)。PLMSearch 使用预先训练好的蛋白质语言模型的深度表征,并使用大量真实结构相似性来训练相似性预测模型。这使得 PLMSearch 能够捕捉到隐藏在序列背后的远程同源信息。大量实验结果表明,PLMSearch 可以像 MMseqs2 一样在数秒内搜索数百万个查询-目标蛋白质对,同时将灵敏度提高三倍以上,与最先进的结构搜索方法不相上下。特别是,与传统的序列搜索方法不同,PLMSearch 可以调用序列不同但结构相似的大多数远端同源对。PLMSearch 可在 https://dmiip.sjtu.edu.cn/PLMSearch 免费获取。
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
Nature Communications
Nature Communications Biological Science Disciplines-
CiteScore
24.90
自引率
2.40%
发文量
6928
审稿时长
3.7 months
期刊介绍: Nature Communications, an open-access journal, publishes high-quality research spanning all areas of the natural sciences. Papers featured in the journal showcase significant advances relevant to specialists in each respective field. With a 2-year impact factor of 16.6 (2022) and a median time of 8 days from submission to the first editorial decision, Nature Communications is committed to rapid dissemination of research findings. As a multidisciplinary journal, it welcomes contributions from biological, health, physical, chemical, Earth, social, mathematical, applied, and engineering sciences, aiming to highlight important breakthroughs within each domain.
期刊最新文献
Reply: Muscle abnormalities in Long COVID Reply: Muscle abnormalities in Long COVID Cryo-EM structure of the botulinum neurotoxin A/SV2B complex and its implications for translocation Unveiling charge utilization mechanisms in ferroelectric for water splitting Unraveling metal effects on CO2 uptake in pyrene-based metal-organic frameworks
×
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:481959085
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