鞘脂信号网络中蛋白-蛋白相互作用的计算预测

Yasemen Gungormez, Elif Ozkirimli Olmez, Kutlu Ozergin Ulgen
{"title":"鞘脂信号网络中蛋白-蛋白相互作用的计算预测","authors":"Yasemen Gungormez, Elif Ozkirimli Olmez, Kutlu Ozergin Ulgen","doi":"10.1109/BIYOMUT.2009.5130307","DOIUrl":null,"url":null,"abstract":"Proteins carry out most of the work in the cell such as immunological recognition, DNA repair and replication, enzymatic activity, cell signaling by interacting with other proteins. Therefore, elucidation of the protein-protein interaction network will assist in understanding molecular mechanism of cellular activities. Recent advances in high-throughput experimental methods have provided a large amount of data that need to be sorted and interpreted to find biologically relevant interactions and pathways. In silico methods that can accurately predict properties of protein-protein interactions have gained increased interest. In this study, the network of sphingolipid (SL) signaling proteins was constructed using computational prediction methods to contribute to missing interactions among the components of sphingolipid protein-protein interaction (PPI) network. As a result of the studies by our group, the potential protein interactions between YER019W-YHL020C and YGR143W-YKL126W were identified. The new predictions proposed by this research can guide rational design of new experiments.","PeriodicalId":119026,"journal":{"name":"2009 14th National Biomedical Engineering Meeting","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Computational prediction of protein-protein interactions in sphingolipid signaling network\",\"authors\":\"Yasemen Gungormez, Elif Ozkirimli Olmez, Kutlu Ozergin Ulgen\",\"doi\":\"10.1109/BIYOMUT.2009.5130307\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Proteins carry out most of the work in the cell such as immunological recognition, DNA repair and replication, enzymatic activity, cell signaling by interacting with other proteins. Therefore, elucidation of the protein-protein interaction network will assist in understanding molecular mechanism of cellular activities. Recent advances in high-throughput experimental methods have provided a large amount of data that need to be sorted and interpreted to find biologically relevant interactions and pathways. In silico methods that can accurately predict properties of protein-protein interactions have gained increased interest. In this study, the network of sphingolipid (SL) signaling proteins was constructed using computational prediction methods to contribute to missing interactions among the components of sphingolipid protein-protein interaction (PPI) network. As a result of the studies by our group, the potential protein interactions between YER019W-YHL020C and YGR143W-YKL126W were identified. The new predictions proposed by this research can guide rational design of new experiments.\",\"PeriodicalId\":119026,\"journal\":{\"name\":\"2009 14th National Biomedical Engineering Meeting\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-05-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 14th National Biomedical Engineering Meeting\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/BIYOMUT.2009.5130307\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 14th National Biomedical Engineering Meeting","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BIYOMUT.2009.5130307","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

蛋白质在细胞中完成大部分的工作,如免疫识别、DNA修复和复制、酶活性、通过与其他蛋白质相互作用来传递细胞信号。因此,蛋白质-蛋白质相互作用网络的阐明将有助于理解细胞活动的分子机制。高通量实验方法的最新进展提供了大量需要分类和解释的数据,以发现生物学相关的相互作用和途径。能够准确预测蛋白质-蛋白质相互作用性质的计算机方法获得了越来越多的兴趣。本研究利用计算预测方法构建鞘脂(sphingolipid, SL)信号蛋白网络,以弥补鞘脂蛋白-蛋白相互作用(sphingolipid protein-protein interaction, PPI)网络中缺失的相互作用。通过本课题组的研究,我们确定了YER019W-YHL020C和YGR143W-YKL126W之间潜在的蛋白相互作用。本研究提出的新预测可以指导新实验的合理设计。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Computational prediction of protein-protein interactions in sphingolipid signaling network
Proteins carry out most of the work in the cell such as immunological recognition, DNA repair and replication, enzymatic activity, cell signaling by interacting with other proteins. Therefore, elucidation of the protein-protein interaction network will assist in understanding molecular mechanism of cellular activities. Recent advances in high-throughput experimental methods have provided a large amount of data that need to be sorted and interpreted to find biologically relevant interactions and pathways. In silico methods that can accurately predict properties of protein-protein interactions have gained increased interest. In this study, the network of sphingolipid (SL) signaling proteins was constructed using computational prediction methods to contribute to missing interactions among the components of sphingolipid protein-protein interaction (PPI) network. As a result of the studies by our group, the potential protein interactions between YER019W-YHL020C and YGR143W-YKL126W were identified. The new predictions proposed by this research can guide rational design of new experiments.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信