中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 计算技术研究所
  3. 中国科学院计算技术研究所
  4. 中国科学院计算技术研究所期刊论文
  5. 英文
Improving protein structure prediction using templates and sequence embedding

文献类型：期刊论文

作者Wu, Fandi1,2,3; Jing, Xiaoyang1; Luo, Xiao1; Xu, Jinbo1
刊名BIOINFORMATICS
出版日期2023
卷号39期号:1页码:8
ISSN号1367-4803
DOI10.1093/bioinformatics/btac723
英文摘要Motivation: Protein structure prediction has been greatly improved by deep learning, but the contribution of different information is yet to be fully understood. This article studies the impacts of two kinds of information for structure prediction: template and multiple sequence alignment (MSA) embedding. Templates have been used by some methods before, such as AlphaFold2, RoseTTAFold and RaptorX. AlphaFold2 and RosetTTAFold only used templates detected by HHsearch, which may not perform very well on some targets. In addition, sequence embedding generated by pre-trained protein language models has not been fully explored for structure prediction. In this article, we study the impact of templates (including the number of templates, the template quality and how the templates are generated) on protein structure prediction accuracy, especially when the templates are detected by methods other than HHsearch. We also study the impact of sequence embedding (generated by MSATransformer and ESM-1b) on structure prediction. Results: We have implemented a deep learning method for protein structure prediction that may take templates and MSA embedding as extra inputs. We study the contribution of templates and MSA embedding to structure prediction accuracy. Our experimental results show that templates can improve structure prediction on 71 of 110 CASP13 (13th Critical Assessment of Structure Prediction) targets and 47 of 91 CASP14 targets, and templates are particularly useful for targets with similar templates. MSA embedding can improve structure prediction on 63 of 91 CASP14 (14th Critical Assessment of Structure Prediction) targets and 87 of 183 CAMEO targets and is particularly useful for proteins with shallow MSAs. When both templates and MSA embedding are used, our method can predict correct folds (TMscore>0.5) for 16 of 23 CASP14 FM targets and 14 of 18 Continuous Automated Model Evaluation (CAMEO) targets, outperforming RoseTTAFold by 5% and 7%, respectively. Availability and implementation : Available at https://github.com/xluo233/RaptorXFold. Supplementary information: Supplementary data are available at Bioinformatics online.
资助项目National Institutes of Health[R01GM089753] ; National Science Foundation[DBI1564955] ; CSC Scholarship ; NSF of China[61925208] ; NSF of China[62222214] ; NSF of China[U22A2028] ; CAS Project for Young Scientists in Basic Research[YSBR-029] ; Youth Innovation Promotion Association CAS ; Xplore Prize
WOS研究方向Biochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Computer Science ; Mathematical & Computational Biology ; Mathematics
语种英语
WOS记录号WOS:001025519200001
出版者OXFORD UNIV PRESS
源URL[http://119.78.100.204/handle/2XEOYT63/21269]  
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.Toyota Technol Inst Chicago, Chicago, IL 60637 USA
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Chinese Acad Sci, Inst Comp Technol, Beijing 626011, Peoples R China
推荐引用方式
GB/T 7714		 Wu, Fandi,Jing, Xiaoyang,Luo, Xiao,et al. Improving protein structure prediction using templates and sequence embedding[J]. BIOINFORMATICS,2023,39(1):8.
APA Wu, Fandi,Jing, Xiaoyang,Luo, Xiao,&Xu, Jinbo.(2023).Improving protein structure prediction using templates and sequence embedding.BIOINFORMATICS,39(1),8.
MLA Wu, Fandi,et al."Improving protein structure prediction using templates and sequence embedding".BIOINFORMATICS 39.1(2023):8.

入库方式： OAI收割

来源：计算技术研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。