The promises of large language models for protein design and modeling.

IF 2.8 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Frontiers in bioinformatics Pub Date : 2023-11-23 eCollection Date: 2023-01-01 DOI:10.3389/fbinf.2023.1304099
Giorgio Valentini, Dario Malchiodi, Jessica Gliozzo, Marco Mesiti, Mauricio Soto-Gomez, Alberto Cabri, Justin Reese, Elena Casiraghi, Peter N Robinson
{"title":"The promises of large language models for protein design and modeling.","authors":"Giorgio Valentini, Dario Malchiodi, Jessica Gliozzo, Marco Mesiti, Mauricio Soto-Gomez, Alberto Cabri, Justin Reese, Elena Casiraghi, Peter N Robinson","doi":"10.3389/fbinf.2023.1304099","DOIUrl":null,"url":null,"abstract":"<p><p>The recent breakthroughs of Large Language Models (LLMs) in the context of natural language processing have opened the way to significant advances in protein research. Indeed, the relationships between human natural language and the \"language of proteins\" invite the application and adaptation of LLMs to protein modelling and design. Considering the impressive results of GPT-4 and other recently developed LLMs in processing, generating and translating human languages, we anticipate analogous results with the language of proteins. Indeed, protein language models have been already trained to accurately predict protein properties, generate novel functionally characterized proteins, achieving state-of-the-art results. In this paper we discuss the promises and the open challenges raised by this novel and exciting research area, and we propose our perspective on how LLMs will affect protein modeling and design.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":null,"pages":null},"PeriodicalIF":2.8000,"publicationDate":"2023-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10701588/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in bioinformatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3389/fbinf.2023.1304099","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"MATHEMATICAL & COMPUTATIONAL BIOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

The recent breakthroughs of Large Language Models (LLMs) in the context of natural language processing have opened the way to significant advances in protein research. Indeed, the relationships between human natural language and the "language of proteins" invite the application and adaptation of LLMs to protein modelling and design. Considering the impressive results of GPT-4 and other recently developed LLMs in processing, generating and translating human languages, we anticipate analogous results with the language of proteins. Indeed, protein language models have been already trained to accurately predict protein properties, generate novel functionally characterized proteins, achieving state-of-the-art results. In this paper we discuss the promises and the open challenges raised by this novel and exciting research area, and we propose our perspective on how LLMs will affect protein modeling and design.

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
大语言模型在蛋白质设计和建模方面的前景。
大语言模型(LLMs)最近在自然语言处理方面取得的突破为蛋白质研究的重大进展开辟了道路。事实上,人类自然语言与 "蛋白质语言 "之间的关系促使人们将大型语言模型应用于蛋白质建模和设计。考虑到 GPT-4 和其他最近开发的 LLM 在处理、生成和翻译人类语言方面取得的令人印象深刻的成果,我们预计蛋白质语言也会取得类似的成果。事实上,蛋白质语言模型已经经过训练,可以准确预测蛋白质特性,生成具有功能特征的新型蛋白质,取得了最先进的成果。在本文中,我们将讨论这一令人兴奋的新研究领域所带来的前景和挑战,并就 LLM 将如何影响蛋白质建模和设计提出我们的看法。
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
CiteScore
2.60
自引率
0.00%
发文量
0
期刊最新文献
The quantum hypercube as a k-mer graph. A review of model evaluation metrics for machine learning in genetics and genomics. Visual analysis of multi-omics data. Molecular docking and molecular dynamic simulation studies to identify potential terpenes against Internalin A protein of Listeria monocytogenes. PhIP-Seq: methods, applications and challenges.
×
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