• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

通过对人类蛋白质互作组的 PageRank 分析揭示信号发射和接收的系统差异。

Systematic differences in signal emitting and receiving revealed by PageRank analysis of a human protein interactome.

机构信息

Quantiative Study Group, Faculty of Business Administration, University of New Brunswick, Fredericton, New Brunswick, Canada.

出版信息

PLoS One. 2012;7(9):e44872. doi: 10.1371/journal.pone.0044872. Epub 2012 Sep 19.

DOI:10.1371/journal.pone.0044872
PMID:23028653
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3446998/
Abstract

Most protein PageRank studies do not use signal flow direction information in protein interactions because this information was not readily available in large protein databases until recently. Therefore, four questions have yet to be answered: A) What is the general difference between signal emitting and receiving in a protein interactome? B) Which proteins are among the top ranked in directional ranking? C) Are high ranked proteins more evolutionarily conserved than low ranked ones? D) Do proteins with similar ranking tend to have similar subcellular locations? In this study, we address these questions using the forward, reverse, and non-directional PageRank approaches to rank an information-directional network of human proteins and study their evolutionary conservation. The forward ranking gives credit to information receivers, reverse ranking to information emitters, and non-directional ranking mainly to the number of interactions. The protein lists generated by the forward and non-directional rankings are highly correlated, but those by the reverse and non-directional rankings are not. The results suggest that the signal emitting/receiving system is characterized by key-emittings and relatively even receivings in the human protein interactome. Signaling pathway proteins are frequent in top ranked ones. Eight proteins are both informational top emitters and top receivers. Top ranked proteins, except a few species-related novel-function ones, are evolutionarily well conserved. Protein-subunit ranking position reflects subunit function. These results demonstrate the usefulness of different PageRank approaches in characterizing protein networks and provide insights to protein interaction in the cell.

摘要

大多数蛋白质 PageRank 研究都没有使用蛋白质相互作用中的信号流方向信息,因为直到最近,这种信息在大型蛋白质数据库中还不容易获得。因此,仍有四个问题有待回答:A)蛋白质相互作用组中信号发射和接收的一般区别是什么?B)在定向排名中哪些蛋白质排名靠前?C)排名较高的蛋白质比排名较低的蛋白质更具进化保守性吗?D)具有相似排名的蛋白质是否倾向于具有相似的亚细胞位置?在这项研究中,我们使用正向、反向和非定向 PageRank 方法对人类蛋白质的信息定向网络进行排名,并研究它们的进化保守性,从而回答了这些问题。正向排名赋予信息接收者以信誉,反向排名赋予信息发送者以信誉,而非定向排名主要赋予交互的数量。正向和非定向排名生成的蛋白质列表高度相关,但反向和非定向排名生成的蛋白质列表则不相关。结果表明,在人类蛋白质相互作用组中,信号发射/接收系统的特点是关键发射者和相对均匀的接收者。信号通路蛋白在排名靠前的蛋白中很常见。有 8 种蛋白质既是信息的主要发射者,也是信息的主要接收者。排名靠前的蛋白质除了少数与物种相关的新功能蛋白质外,在进化上都很好地保存下来。蛋白质亚基排名位置反映了亚基功能。这些结果表明,不同的 PageRank 方法在表征蛋白质网络方面非常有用,并为细胞内的蛋白质相互作用提供了新的见解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bea9/3446998/fc5f357325f6/pone.0044872.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bea9/3446998/1a522ade7434/pone.0044872.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bea9/3446998/fc5f357325f6/pone.0044872.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bea9/3446998/1a522ade7434/pone.0044872.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bea9/3446998/fc5f357325f6/pone.0044872.g002.jpg

相似文献

1
Systematic differences in signal emitting and receiving revealed by PageRank analysis of a human protein interactome.通过对人类蛋白质互作组的 PageRank 分析揭示信号发射和接收的系统差异。
PLoS One. 2012;7(9):e44872. doi: 10.1371/journal.pone.0044872. Epub 2012 Sep 19.
2
Identification of Top-ranked Proteins within a Directional Protein Interaction Network using the PageRank Algorithm: Applications in Humans and Plants.使用PageRank算法在定向蛋白质相互作用网络中识别排名靠前的蛋白质:在人类和植物中的应用
Curr Issues Mol Biol. 2016;20:13-28. Epub 2015 Dec 4.
3
Interactome of the hepatitis C virus: Literature mining with ANDSystem.丙型肝炎病毒的相互作用组:使用 ANDSystem 进行文献挖掘。
Virus Res. 2016 Jun 15;218:40-8. doi: 10.1016/j.virusres.2015.12.003. Epub 2015 Dec 7.
4
A method to pinpoint undiscovered links in genetic and protein networks.一种在基因和蛋白质网络中精准定位未发现联系的方法。
Stud Health Technol Inform. 2015;210:771-5.
5
Information flow analysis of interactome networks.相互作用组网络的信息流分析
PLoS Comput Biol. 2009 Apr;5(4):e1000350. doi: 10.1371/journal.pcbi.1000350. Epub 2009 Apr 10.
6
Dynamic programming re-ranking for PPI interactor and pair extraction in full-text articles.在全文文章中重新排名 PPI 相互作用体和对的动态编程。
BMC Bioinformatics. 2011 Feb 23;12:60. doi: 10.1186/1471-2105-12-60.
7
The CASP13-CAPRI targets as case studies to illustrate a novel scoring pipeline integrating CONSRANK with clustering and interface analyses.将 CASP13-CAPRI 靶标作为案例研究,以说明一种将 CONSRANK 与聚类和界面分析相结合的新型评分流程。
BMC Bioinformatics. 2020 Sep 16;21(Suppl 8):262. doi: 10.1186/s12859-020-03600-8.
8
Structure-based prediction of West Nile virus-human protein-protein interactions.基于结构的西尼罗河病毒-人类蛋白质-蛋白质相互作用预测。
J Biomol Struct Dyn. 2019 Jun;37(9):2310-2321. doi: 10.1080/07391102.2018.1479659. Epub 2018 Nov 17.
9
Bacterial protein meta-interactomes predict cross-species interactions and protein function.细菌蛋白质元相互作用组可预测跨物种相互作用及蛋白质功能。
BMC Bioinformatics. 2017 Mar 16;18(1):171. doi: 10.1186/s12859-017-1585-0.
10
POINT: a database for the prediction of protein-protein interactions based on the orthologous interactome.POINT:一个基于直系同源相互作用组预测蛋白质-蛋白质相互作用的数据库。
Bioinformatics. 2004 Nov 22;20(17):3273-6. doi: 10.1093/bioinformatics/bth366. Epub 2004 Jun 24.

引用本文的文献

1
Aberration hubs in protein interaction networks highlight actionable targets in cancer.蛋白质相互作用网络中的畸变中心突出了癌症中可操作的靶点。
Oncotarget. 2018 May 18;9(38):25166-25180. doi: 10.18632/oncotarget.25382.
2
Network propagation: a universal amplifier of genetic associations.网络传播:遗传关联的通用放大器。
Nat Rev Genet. 2017 Sep;18(9):551-562. doi: 10.1038/nrg.2017.38. Epub 2017 Jun 12.
3
Genome-Wide Identification and Function Analyses of Heat Shock Transcription Factors in Potato.马铃薯热激转录因子的全基因组鉴定与功能分析

本文引用的文献

1
The evolution of protein structures and structural ensembles under functional constraint.功能约束下蛋白质结构和结构集合的演变。
Genes (Basel). 2011 Oct 28;2(4):748-62. doi: 10.3390/genes2040748.
2
Mammalian MAPK signal transduction pathways activated by stress and inflammation: a 10-year update.应激和炎症激活的哺乳动物 MAPK 信号转导通路:10 年更新。
Physiol Rev. 2012 Apr;92(2):689-737. doi: 10.1152/physrev.00028.2011.
3
Protein phosphatases and their regulation in the control of mitosis.蛋白质磷酸酶及其在有丝分裂调控中的作用。
Front Plant Sci. 2016 Apr 19;7:490. doi: 10.3389/fpls.2016.00490. eCollection 2016.
4
Motif types, motif locations and base composition patterns around the RNA polyadenylation site in microorganisms, plants and animals.微生物、植物和动物中RNA多聚腺苷酸化位点周围的基序类型、基序位置和碱基组成模式。
BMC Evol Biol. 2014 Jul 23;14:162. doi: 10.1186/s12862-014-0162-7.
5
Comparative analysis of the base compositions of the pre-mRNA 3' cleaved-off region and the mRNA 3' untranslated region relative to the genomic base composition in animals and plants.相对于动植物基因组碱基组成,对前体mRNA 3' 切割区域和mRNA 3' 非翻译区域的碱基组成进行比较分析。
PLoS One. 2014 Jun 18;9(6):e99928. doi: 10.1371/journal.pone.0099928. eCollection 2014.
6
New directions for diffusion-based network prediction of protein function: incorporating pathways with confidence.基于扩散的蛋白质功能网络预测的新方向:置信度整合途径。
Bioinformatics. 2014 Jun 15;30(12):i219-27. doi: 10.1093/bioinformatics/btu263.
7
Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review.分子网络的结构与动态:药物发现的新范例:全面综述。
Pharmacol Ther. 2013 Jun;138(3):333-408. doi: 10.1016/j.pharmthera.2013.01.016. Epub 2013 Feb 4.
EMBO Rep. 2012 Mar;13(3):197-203. doi: 10.1038/embor.2011.263.
4
The Protein-Protein Interaction tasks of BioCreative III: classification/ranking of articles and linking bio-ontology concepts to full text.BioCreative III 的蛋白质-蛋白质相互作用任务:文章的分类/排序和将生物本体论概念链接到全文。
BMC Bioinformatics. 2011 Oct 3;12 Suppl 8(Suppl 8):S3. doi: 10.1186/1471-2105-12-S8-S3.
5
Focus issue: recruiting players for a game of ERK.重点议题:招募玩家参与 ERK 游戏。
Sci Signal. 2011 Oct 25;4(196):eg9. doi: 10.1126/scisignal.2002601.
6
A 2-D gel reference map of the basic human heart proteome.人类心脏基础蛋白质组的二维凝胶参考图谱。
Proteomics. 2011 Sep;11(17):3582-6. doi: 10.1002/pmic.201000182. Epub 2011 Aug 4.
7
Selective loss of cysteine residues and disulphide bonds in a potato proteinase inhibitor II family.马铃薯蛋白酶抑制剂 II 家族中半胱氨酸残基和二硫键的选择性丧失。
PLoS One. 2011 Apr 11;6(4):e18615. doi: 10.1371/journal.pone.0018615.
8
When the Web meets the cell: using personalized PageRank for analyzing protein interaction networks.当网络遇到细胞:使用个性化 PageRank 分析蛋白质相互作用网络。
Bioinformatics. 2011 Feb 1;27(3):405-7. doi: 10.1093/bioinformatics/btq680. Epub 2010 Dec 12.
9
Proteome-wide prediction of signal flow direction in protein interaction networks based on interacting domains.基于相互作用结构域的蛋白质相互作用网络中信号流方向的全蛋白质组预测。
Mol Cell Proteomics. 2009 Sep;8(9):2063-70. doi: 10.1074/mcp.M800354-MCP200. Epub 2009 Jun 5.
10
Up-regulation of C-terminal tensin-like molecule promotes the tumorigenicity of colon cancer through beta-catenin.C 末端张力蛋白样分子的上调通过β-连环蛋白促进结肠癌的致瘤性。
Cancer Res. 2009 Jun 1;69(11):4563-6. doi: 10.1158/0008-5472.CAN-09-0117.