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MAGIIC-PRO：通过高效发现蛋白质序列中的长模式来检测功能特征。

MAGIIC-PRO: detecting functional signatures by efficient discovery of long patterns in protein sequences.

作者信息

Hsu Chen-Ming, Chen Chien-Yu, Liu Baw-Jhiune

机构信息

Department of Computer Science and Engineering, Yuan Ze University, Chung-Li, 320, Taiwan, Republic of China.

出版信息

Nucleic Acids Res. 2008 Mar;36(4):1400-6. doi: 10.1093/nar/gkm717.

DOI:10.1093/nar/gkm717
PMID:18314547
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3143912/
Abstract

This paper presents a web service named MAGIICPRO,which aims to discover functional signatures of a query protein by sequential pattern mining. Automatic discovery of patterns from unaligned biological sequences is an important problem in molecular biology. MAGIIC-PRO is different from several previously established methods performing similar tasks in two major ways. The first remarkable feature of MAGIIC-PRO is its efficiency in delivering long patterns. With incorporating a new type of gap constraints and some of the state-of-theart data mining techniques, MAGIIC-PRO usually identifies satisfied patterns within an acceptable response time. The efficiency of MAGIIC-PRO enables the users to quickly discover functional signatures of which the residues are not from only one region of the protein sequences or are only conserved in few members of a protein family. The second remarkable feature of MAGIIC-PRO is its effort in refining the mining results. Considering large flexible gaps improves the completeness of the derived functional signatures. The users can be directly guided to the patterns with as many blocks as that are conserved simultaneously. In this paper,we show by experiments that MAGIIC-PRO is efficient and effective in identifying ligand-binding sites and hot regions in protein-protein interactions directly from sequences. The web service is availableat http://biominer.bime.ntu.edu.tw/magiicproand a mirror site at http://biominer.cse.yzu.edu.tw/magiicpro.

摘要

本文介绍了一种名为MAGIICPRO的网络服务，其旨在通过序列模式挖掘来发现查询蛋白质的功能特征。从未比对的生物序列中自动发现模式是分子生物学中的一个重要问题。MAGIIC-PRO在两个主要方面与之前建立的几种执行类似任务的方法不同。MAGIIC-PRO的第一个显著特征是其在生成长模式方面的效率。通过纳入一种新型的间隙约束和一些最先进的数据挖掘技术，MAGIIC-PRO通常能在可接受的响应时间内识别出满足条件的模式。MAGIIC-PRO的效率使用户能够快速发现其残基并非仅来自蛋白质序列的一个区域或仅在蛋白质家族的少数成员中保守的功能特征。MAGIIC-PRO的第二个显著特征是其在完善挖掘结果方面所做的努力。考虑到大的灵活间隙可提高所推导功能特征的完整性。用户可以直接被引导至具有同时保守的多个模块的模式。在本文中，我们通过实验表明，MAGIIC-PRO在直接从序列中识别蛋白质-蛋白质相互作用中的配体结合位点和热点区域方面是高效且有效的。该网络服务可在http://biominer.bime.ntu.edu.tw/magiicpro获取，其镜像站点为http://biominer.cse.yzu.edu.tw/magiicpro。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e86c/3143912/e6864fa97c34/gkm717f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e86c/3143912/7e3d4107a65f/gkm717f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e86c/3143912/519b03054c8e/gkm717f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e86c/3143912/e6864fa97c34/gkm717f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e86c/3143912/7e3d4107a65f/gkm717f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e86c/3143912/519b03054c8e/gkm717f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e86c/3143912/e6864fa97c34/gkm717f3.jpg

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