Improvement in protein sequence-structure alignment using insertion/deletion frequency arrays.

Kyle Ellrott, Jun-tao Guo, Victor Olman, Ying Xu
{"title":"Improvement in protein sequence-structure alignment using insertion/deletion frequency arrays.","authors":"Kyle Ellrott,&nbsp;Jun-tao Guo,&nbsp;Victor Olman,&nbsp;Ying Xu","doi":"","DOIUrl":null,"url":null,"abstract":"<p><p>As a protein evolves, not every part of the amino acid sequence has an equal probability of being deleted or for allowing insertions, because not every amino acid plays an equally important role in maintaining the protein structure. However the most prevalent models in fold recognition methods treat every amino acid deletion and insertion as equally probable events. We have analyzed the alignment patterns for homologous and analogous sequences to determine patterns of insertion and deletions, and used that information to determine the statistics of insertions and deletions for different amino acids of a target sequence. We define these patterns as Insertion/Deletion (Indel) Frequency Arrays (IFA). By applying IFA to the protein threading problem, we have been able to improve the alignment accuracy, especially for proteins with low sequence identity.</p>","PeriodicalId":72665,"journal":{"name":"Computational systems bioinformatics. Computational Systems Bioinformatics Conference","volume":" ","pages":"335-42"},"PeriodicalIF":0.0000,"publicationDate":"2007-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computational systems bioinformatics. Computational Systems Bioinformatics Conference","FirstCategoryId":"1085","ListUrlMain":"","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

As a protein evolves, not every part of the amino acid sequence has an equal probability of being deleted or for allowing insertions, because not every amino acid plays an equally important role in maintaining the protein structure. However the most prevalent models in fold recognition methods treat every amino acid deletion and insertion as equally probable events. We have analyzed the alignment patterns for homologous and analogous sequences to determine patterns of insertion and deletions, and used that information to determine the statistics of insertions and deletions for different amino acids of a target sequence. We define these patterns as Insertion/Deletion (Indel) Frequency Arrays (IFA). By applying IFA to the protein threading problem, we have been able to improve the alignment accuracy, especially for proteins with low sequence identity.

分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
利用插入/删除频率阵列改进蛋白质序列结构比对。
随着蛋白质的进化,并不是氨基酸序列的每个部分都有相同的被删除或允许插入的概率,因为不是每个氨基酸在维持蛋白质结构方面都起着同样重要的作用。然而,在折叠识别方法中最流行的模型将每个氨基酸缺失和插入视为等概率事件。我们分析了同源和类似序列的比对模式,以确定插入和缺失的模式,并使用该信息来确定目标序列中不同氨基酸的插入和缺失统计。我们将这些模式定义为插入/删除(Indel)频率阵列(IFA)。通过将IFA应用于蛋白质穿线问题,我们能够提高比对精度,特别是对序列同源性较低的蛋白质。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Novel Gene Discovery in the Human Malaria Parasite using Nucleosome Positioning Data. Estimating support for protein-protein interaction data with applications to function prediction. On the accurate construction of consensus genetic maps. Efficient haplotype inference from pedigrees with missing data using linear systems with disjoint-set data structures. Knowledge representation and data mining for biological imaging.
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1