Single-sequence protein structure prediction by integrating protein language models

doi:10.1073/pnas.2308788121

CSpace

	Single-sequence protein structure prediction by integrating protein language models
	Jing, Xiaoyang 1; Wu, Fandi 1,2; Luo, Xiao 3,4; Xu, Jinbo 1,3
	2024-03-20
发表期刊	PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
ISSN	0027-8424
卷号	121 期号:13 页码:7
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.
关键词	protein structure prediction protein language model single-sequence protein structure rediction antibody structure prediction single mutation effect
DOI	10.1073/pnas.2308788121
收录类别	SCI
语种	英语
WOS研究方向	Science & Technology - Other Topics
WOS类目	Multidisciplinary Sciences
WOS记录号	WOS:001207151800001
出版者	NATL ACAD SCIENCES
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/38707
专题	中国科学院计算技术研究所
通讯作者	Xu, Jinbo
作者单位	1.MoleculeMind Ltd, Beijing 100084, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 3.Toyota Technol Inst Chicago, Chicago, IL 60637 USA 4.Shanghai Artificial Intelligence Lab, Shanghai 200232, 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.