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本文引用的文献

1
A novel method for GPCR recognition and family classification from sequence alone using signatures derived from profile hidden Markov models.一种仅利用从轮廓隐马尔可夫模型派生的特征，从序列中识别GPCR并进行家族分类的新方法。
SAR QSAR Environ Res. 2003 Oct-Dec;14(5-6):413-20. doi: 10.1080/10629360310001623999.
2
The SWISS-PROT protein knowledgebase and its supplement TrEMBL in 2003.2003年的SWISS-PROT蛋白质知识库及其补充TrEMBL。
Nucleic Acids Res. 2003 Jan 1;31(1):365-70. doi: 10.1093/nar/gkg095.
3
GPCRDB information system for G protein-coupled receptors.G蛋白偶联受体的GPCRDB信息系统。
Nucleic Acids Res. 2003 Jan 1;31(1):294-7. doi: 10.1093/nar/gkg103.
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Seven-transmembrane receptors.七跨膜受体
Nat Rev Mol Cell Biol. 2002 Sep;3(9):639-50. doi: 10.1038/nrm908.
5
Deriving structural and functional insights from a ligand-based hierarchical classification of G protein-coupled receptors.从基于配体的G蛋白偶联受体层次分类中获得结构和功能方面的见解。
Protein Eng. 2002 Jan;15(1):7-12. doi: 10.1093/protein/15.1.7.
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The Pfam protein families database.Pfam蛋白质家族数据库。
Nucleic Acids Res. 2002 Jan 1;30(1):276-80. doi: 10.1093/nar/30.1.276.
7
The PROSITE database, its status in 2002.PROSITE数据库及其2002年的状况。
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CAST: an iterative algorithm for the complexity analysis of sequence tracts. Complexity analysis of sequence tracts.CAST：一种用于序列片段复杂性分析的迭代算法。序列片段的复杂性分析。
Bioinformatics. 2000 Oct;16(10):915-22. doi: 10.1093/bioinformatics/16.10.915.
9
Increased coverage of protein families with the blocks database servers.通过blocks数据库服务器增加蛋白质家族的覆盖率。
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Profile hidden Markov models.轮廓隐马尔可夫模型
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PRED-GPCR：G蛋白偶联受体识别与家族分类服务器。

PRED-GPCR: GPCR recognition and family classification server.

作者信息

Papasaikas P K, Bagos P G, Litou Z I, Promponas V J, Hamodrakas S J

机构信息

Department of Cell Biology and Biophysics, Faculty of Biology, University of Athens, Panepistimiopolis, Athens 157 01, Greece.

出版信息

Nucleic Acids Res. 2004 Jul 1;32(Web Server issue):W380-2. doi: 10.1093/nar/gkh431.

DOI:10.1093/nar/gkh431
PMID:15215415
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC441569/
Abstract

The vast cell-surface receptor family of G-protein coupled receptors (GPCRs) is the focus of both academic and pharmaceutical research due to their key role in cell physiology along with their amenability to drug intervention. As the data flow rate from the various genome and proteome projects continues to grow, so does the need for fast, automated and reliable screening for new members of the various GPCR families. PRED-GPCR is a free Internet service for GPCR recognition and classification at the family level. A submitted sequence or set of sequences, is queried against the PRED-GPCR library, housing 265 signature profile HMMs corresponding to 67 well-characterized GPCR families. Users query the server through a web interface and results are presented in HTML output format. The server returns all single-motif matches along with the combined results for the corresponding families. The service is available online since October 2003 at http://bioinformatics.biol.uoa.gr/PRED-GPCR.

摘要

G蛋白偶联受体（GPCRs）庞大的细胞表面受体家族是学术研究和药物研发的焦点，因为它们在细胞生理学中发挥关键作用，并且易于进行药物干预。随着来自各种基因组和蛋白质组计划的数据流率不断增长，对各种GPCR家族新成员进行快速、自动化和可靠筛选的需求也在增加。PRED-GPCR是一项免费的互联网服务，用于在家族水平上识别和分类GPCR。将提交的一个或一组序列与PRED-GPCR文库进行比对，该文库包含对应于67个特征明确的GPCR家族的265个特征谱隐马尔可夫模型（HMMs）。用户通过网页界面查询服务器，结果以HTML输出格式呈现。服务器返回所有单基序匹配结果以及相应家族的综合结果。该服务自2003年10月起可在线获取，网址为http://bioinformatics.biol.uoa.gr/PRED-GPCR 。