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政大機構典藏 > 商學院 > 科技管理與智慧財產研究所 > 期刊論文 > Item 140.119/71238

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/71238

Title:	Length Encoded Secondary Structure Profile for Remote Homologous Protein Detection
Authors:	鄭至甫 Jeng, Jyh‐Fu
Contributors:	科管智財所
Keywords:	protein sequence comparison;secondary structure element alignment;dynamic programming;length encoded profile;protein folding
Date:	2009
Issue Date:	2014-11-07 16:02:31 (UTC+8)
Abstract:	Protein data has an explosive increasing rate both in volume and diversity, yet many of its structures remain unresolved, as well their functions remain to be identified. The conventional sequence alignment tools are insufficient in remote homology detection, while the current structural alignment tools would encounter the difficulties for proteins of unresolved structure. Here, we aimed to overcome the combination of two major obstacles for detecting remote homologous proteins: proteins with unresolved structure, and proteins of low sequence identity but high structural similarity. We proposed a novel method for improving the performance of protein matching problem, especially for mining remote homologous proteins. In this study, existing secondary structure prediction techniques were applied to provide the locations of secondary structure elements of proteins. The proposed LESS (Length Encoded Secondary Structure) profile was then constructed for segment-based similarity comparison in parallel computing. As compared to a conventional residue-based sequence alignment tool, detection of remote protein homologies through LESS profile is favourable in terms of speed and high sequence diversity, and its accuracy and performance can improve the deficiencies of the traditional primary sequence alignment methodology. This method may further support biologists in protein folding, evolution, and function prediction.
Relation:	Lecture Notes in Computer Science, 5574, 1-11
Data Type:	article
Appears in Collections:	[科技管理與智慧財產研究所] 期刊論文

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