Protein structure search and local structure characterization

<p>Abstract</p> <p>Background</p> <p>Structural similarities among proteins can provide valuable insight into their functional mechanisms and relationships. As the number of available three-dimensional (3D) protein structures increases, a greater variety of studies can...

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Main Authors: Ku Shih-Yen, Hu Yuh-Jyh
Format: Article
Language:English
Published: BMC 2008-08-01
Series:BMC Bioinformatics
Online Access:http://www.biomedcentral.com/1471-2105/9/349
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spelling doaj-855ad5315ef54c05964749b099ea9da52020-11-25T00:23:23ZengBMCBMC Bioinformatics1471-21052008-08-019134910.1186/1471-2105-9-349Protein structure search and local structure characterizationKu Shih-YenHu Yuh-Jyh<p>Abstract</p> <p>Background</p> <p>Structural similarities among proteins can provide valuable insight into their functional mechanisms and relationships. As the number of available three-dimensional (3D) protein structures increases, a greater variety of studies can be conducted with increasing efficiency, among which is the design of protein structural alphabets. Structural alphabets allow us to characterize local structures of proteins and describe the global folding structure of a protein using a one-dimensional (1D) sequence. Thus, 1D sequences can be used to identify structural similarities among proteins using standard sequence alignment tools such as BLAST or FASTA.</p> <p>Results</p> <p>We used self-organizing maps in combination with a minimum spanning tree algorithm to determine the optimum size of a structural alphabet and applied the k-means algorithm to group protein fragnts into clusters. The centroids of these clusters defined the structural alphabet. We also developed a flexible matrix training system to build a substitution matrix (TRISUM-169) for our alphabet. Based on FASTA and using TRISUM-169 as the substitution matrix, we developed the SA-FAST alignment tool. We compared the performance of SA-FAST with that of various search tools in database-scale search tasks and found that SA-FAST was highly competitive in all tests conducted. Further, we evaluated the performance of our structural alphabet in recognizing specific structural domains of EGF and EGF-like proteins. Our method successfully recovered more EGF sub-domains using our structural alphabet than when using other structural alphabets. SA-FAST can be found at <url>http://140.113.166.178/safast/</url>.</p> <p>Conclusion</p> <p>The goal of this project was two-fold. First, we wanted to introduce a modular design pipeline to those who have been working with structural alphabets. Secondly, we wanted to open the door to researchers who have done substantial work in biological sequences but have yet to enter the field of protein structure research. Our experiments showed that by transforming the structural representations from 3D to 1D, several 1D-based tools can be applied to structural analysis, including similarity searches and structural motif finding.</p> http://www.biomedcentral.com/1471-2105/9/349
collection DOAJ
language English
format Article
sources DOAJ
author Ku Shih-Yen
Hu Yuh-Jyh
spellingShingle Ku Shih-Yen
Hu Yuh-Jyh
Protein structure search and local structure characterization
BMC Bioinformatics
author_facet Ku Shih-Yen
Hu Yuh-Jyh
author_sort Ku Shih-Yen
title Protein structure search and local structure characterization
title_short Protein structure search and local structure characterization
title_full Protein structure search and local structure characterization
title_fullStr Protein structure search and local structure characterization
title_full_unstemmed Protein structure search and local structure characterization
title_sort protein structure search and local structure characterization
publisher BMC
series BMC Bioinformatics
issn 1471-2105
publishDate 2008-08-01
description <p>Abstract</p> <p>Background</p> <p>Structural similarities among proteins can provide valuable insight into their functional mechanisms and relationships. As the number of available three-dimensional (3D) protein structures increases, a greater variety of studies can be conducted with increasing efficiency, among which is the design of protein structural alphabets. Structural alphabets allow us to characterize local structures of proteins and describe the global folding structure of a protein using a one-dimensional (1D) sequence. Thus, 1D sequences can be used to identify structural similarities among proteins using standard sequence alignment tools such as BLAST or FASTA.</p> <p>Results</p> <p>We used self-organizing maps in combination with a minimum spanning tree algorithm to determine the optimum size of a structural alphabet and applied the k-means algorithm to group protein fragnts into clusters. The centroids of these clusters defined the structural alphabet. We also developed a flexible matrix training system to build a substitution matrix (TRISUM-169) for our alphabet. Based on FASTA and using TRISUM-169 as the substitution matrix, we developed the SA-FAST alignment tool. We compared the performance of SA-FAST with that of various search tools in database-scale search tasks and found that SA-FAST was highly competitive in all tests conducted. Further, we evaluated the performance of our structural alphabet in recognizing specific structural domains of EGF and EGF-like proteins. Our method successfully recovered more EGF sub-domains using our structural alphabet than when using other structural alphabets. SA-FAST can be found at <url>http://140.113.166.178/safast/</url>.</p> <p>Conclusion</p> <p>The goal of this project was two-fold. First, we wanted to introduce a modular design pipeline to those who have been working with structural alphabets. Secondly, we wanted to open the door to researchers who have done substantial work in biological sequences but have yet to enter the field of protein structure research. Our experiments showed that by transforming the structural representations from 3D to 1D, several 1D-based tools can be applied to structural analysis, including similarity searches and structural motif finding.</p>
url http://www.biomedcentral.com/1471-2105/9/349
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