Browsing by Author "Ku, Shih-Yen"

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Discovery of Structural Motifs Using Protein Structural Alphabets and 1D Motif-Finding Methods
(2010)
Ku, Shih-Yen
;
Hu, Yuh-Jyh
;
電控工程研究所
;
Institute of Electrical and Control Engineering
Although the increasing number of available 3D proteins structures has made a wide variety of computational protein structure research possible, yet the success is still hindered by the high 3D computational complexity. Based on 3D information, several 1D protein structural alphabets have been developed, which can not only describe the global folding structure of a protein as a 1D sequence, but can also characterize local structures in proteins. Instead of applying computationally intensive 3D structure alignment tools, we introduce an approach that combines standard 1D motif detection methods with structural alphabets to discover locally conserved protein motifs. These 1D structural motifs can characterize protein groups at different levels, e.g., families, super families, and folds in SCOP, as group features.
Protein structure search and local structure characterization
(2008-08-22)
Ku, Shih-Yen
;
Hu, Yuh-Jyh
;
分子醫學與生物工程研究所
;
資訊工程學系
;
Institute of Molecular Medicine and Bioengineering
Background: 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. Results: 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 http://140.113.166.178/safast/. Conclusion: 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.
Structural alphabet motif discovery and a structural motif database
(2012-01-01)
Ku, Shih-Yen
;
Hu, Yuh-Jyh
;
分子醫學與生物工程研究所
;
資訊工程學系
;
Institute of Molecular Medicine and Bioengineering
This study proposes a general framework for structural motif discovery. The framework is based on a modular design in which the system components can be modified or replaced independently to increase its applicability to various studies. It is a two-stage approach that first converts protein 3D structures into structural alphabet sequences, and then applies a sequence motif-finding tool to these sequences to detect conserved motifs. We named the structural motif database we built the SA-Motifbase, which provides the structural information conserved at different hierarchical levels in SCOP. For each motif, SA-Motifbase presents its 3D view; alphabet letter preference; alphabet letter frequency distribution; and the significance. SA-Motifbase is available at http://bioinfo.cis.nctu.edu.tw/samotifbase/. (C) 2011 Elsevier Ltd. All rights reserved.