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Single-sequence protein structure prediction by integrating protein language models

文献类型：期刊论文

作者Jing, Xiaoyang4; Wu, Fandi3,4; Luo, Xiao1,2; Xu, Jinbo2,4
刊名PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
出版日期2024-03-20
卷号121期号:13页码:7
关键词protein structure prediction protein language model single-sequence protein structure rediction antibody structure prediction single mutation effect
ISSN号0027-8424
DOI10.1073/pnas.2308788121
英文摘要Protein structure prediction has been greatly improved by deep learning in the past few years. However, the most successful methods rely on multiple sequence alignment (MSA) of the sequence homologs of the protein under prediction. In nature, a protein folds in the absence of its sequence homologs and thus, a MSA-free structure prediction method is desired. Here, we develop a single-sequence-based protein structure prediction method RaptorX-Single by integrating several protein language models and a structure generation module and then study its advantage over MSA-based methods. Our experimental results indicate that in addition to running much faster than MSA-based methods such as AlphaFold2, RaptorX-Single outperforms AlphaFold2 and other MSA-free methods in predicting the structure of antibodies (after fine-tuning on antibody data), proteins of very few sequence homologs, and single mutation effects. By comparing different protein language models, our results show that not only the scale but also the training data of protein language models will impact the performance. RaptorX-Single also compares favorably to MSA-based AlphaFold2 when the protein under prediction has a large number of sequence homologs.
WOS研究方向Science & Technology - Other Topics
语种英语
WOS记录号WOS:001207151800001
出版者NATL ACAD SCIENCES
源URL[http://119.78.100.204/handle/2XEOYT63/38706]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Xu, Jinbo
作者单位1.Shanghai Artificial Intelligence Lab, Shanghai 200232, Peoples R China
2.Toyota Technol Inst Chicago, Chicago, IL 60637 USA
3.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China
4.MoleculeMind Ltd, Beijing 100084, Peoples R China
推荐引用方式
GB/T 7714		 Jing, Xiaoyang,Wu, Fandi,Luo, Xiao,et al. Single-sequence protein structure prediction by integrating protein language models[J]. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA,2024,121(13):7.
APA Jing, Xiaoyang,Wu, Fandi,Luo, Xiao,&Xu, Jinbo.(2024).Single-sequence protein structure prediction by integrating protein language models.PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA,121(13),7.
MLA Jing, Xiaoyang,et al."Single-sequence protein structure prediction by integrating protein language models".PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA 121.13(2024):7.

入库方式： OAI收割

来源：计算技术研究所

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