Browsing by Author "Wu, LC"

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An agent-based system to discover protein-protein interactions, identify protein complexes and proteins with multiple peptide mass fingerprints
(2006-07-15)
Lee, TY
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Horng, JT
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Juan, HF
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Huang, HD
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Wu, LC
Proteins "work together" by actually binding to form multicomponent complexes that carry out specific functions. Proteomic analyses based on the mass spectrum are now key methods to determine the components in protein complexes. The protein-protein interaction or functional association may be known to exist among the extracted protein spots while analyzing the proteins on the 2D gel. In this study, we develop an agent-based system, namely AgentMultiProtIdent, which integrated two protein identification tools and a variety of databases storing relations among proteins and used to discover protein-protein interactions and protein functional associations, and identify protein complexes and proteins with multiple peptide mass fingerprints as input. The system takes Multiple Peptide Mass Fingerprints (PMFs) as a whole in the protein complex or protein identification. With the relations among proteins, it may greatly improve the accuracy of identification of protein complexes. Also, possible relationship of the multiple peptide mass fingerprints, such as ontology relation, can be discovered by our system, especially in the identification of protein complexes.
Analysis of Sun et al.'s linkability attack on some proxy blind signature schemes
(2006-02-01)
Wu, LC
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Yeh, YS
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Liu, TS
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資訊工程學系
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Department of Computer Science
The proxy blind signature scheme allows the designated proxy signer using the proxy secret key to generate a blind signature on behalf of the original signer. Tan et al. presented the DLP-based and ECDLP based blind signature schemes. Awasthi and Lal proposed a improved DLP-based scheme later. Recently, Sun et al. presented linkability attack on Tan et al.'s and Awasthi-Lal's proxy blind signature schemes respectively. In this paper, we show that Sun et al.'s attack is failed and these schemes are still satisfy the unlinkability property. (C) 2005 Elsevier Inc. All rights reserved.
Comment on traceability on RSA-based partially signature with low computation
(2005-11-15)
Wu, LC
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Yeh, YS
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資訊工程學系
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Department of Computer Science
Recently, Chien et al. proposed RSA-based partially blind signature with low computation for mobile and smart-card applications. Hwang et al. claimed that Chien et al.'s scheme cannot meet the untraceability property of the blind signature later. In this paper, we show that Hwang et al.'s claim is incorrect and Chien et al.'s scheme is still satisfy the untraceability property. (c) 2005 Elsevier Inc. All rights reserved.
Detection of discriminative sequence motifs in proteins obtained from prokaryotes grown at various temperatures
(2006-04-30)
Wu, LC
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Horng, JT
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Huang, SL
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Huang, HD
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Liu, BJ
Recent investigations on the stability of proteins have demonstrated various structural factors, but few have considered sequence factors such as protein motifs. These motifs represent highly conserved regions and describe critical regions that may only exist on proteins that remain functional at high temperatures. This investigation presents a method for identifying and comparing corresponding mesophilic and thermophilic sequence motifs between protein families. Discriminative motifs that are conserved only in the mesophilic or thermophilic subfamily are identified. Analysis of the results shows that, although the subfamilies of most protein families share similar motifs, some discriminative motifs are present in particular thermophilic/mesophilic subfamilies. The thermophilic discriminative motifs are conserved only in thermophilic organisms, revealing that physiochemical principles support thermostability. (c) 2006 Wiley Periodicals, Inc.
Discovering common structural motifs of ribosomal RNA secondary structures in prokaryotes
(2005-08-01)
Huang, HD
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Horng, JT
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Wu, LC
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Fang, SF
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生物資訊及系統生物研究所
Certain structural motifs, like tetra-loops, in ribosomal RNA are known to functionally implicate in virtually every aspect of protein synthesis. Ribosomal RNA molecules were also widely used as a tool in molecular evolutionary studies because of their ubiquity, size and low evolutionary rate. In this study, we adapt a data mining approach to discover common structural motifs, and then we use a machine learning approach to identify discriminating CSMs from groups of organisms. Finally, we construct phylogeneitc trees to investigate the evolution of ribosomal RNA by serving the CSMs discovered as targets, which are used to estimate the evolutionary relatedness between organisms. The aim of this study is to discover common structural motifs (CSMs), i.e., those single-strain regions shared in ribosomal RNA secondary structures by several organisms, which are related to specific domains or functions. We discover a set of common structural motifs from several data sets of Archaea and Bacteria. Significant CSMs are then induced by a decision tree. Furthermore, phylogenetic trees are constructed based on CSMs and primary sequences of SSU 16 S ribosomal RNA.
Identifying the combination of genetic factors that determine susceptibility to cervical cancer
(2004-03-01)
Horng, JT
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Hu, KC
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Wu, LC
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Huang, HD
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Lin, FM
Although infection with high-risk types of human papillomavirus (HPV) has been identified as the primary cause of cervical cancer, only some of those infected go on to develop cervical cancer. Obviously, the progression from HPV infection to cancer involves other environmental and host factors. Recent population-based twin and family studies have demonstrated the importance of the hereditary component of cervical cancer, associated with genetic susceptibility. Consequently, single-nucleotide polymorphism (SNP) markers and microsatellites should be considered genetic factors for determining what combinations of genetic factors are involved in precancerous changes to cervical cancer. This study employs a Bayesian network and four different decision tree algorithms, and compares the performance of these learning algorithms. The results of this study raise the possibility of investigations that could identify combinations of genetic factors, such as SNPs and microsatellites, that influence the risk associated with common complex multifactorial diseases, such as cervical cancer.
Incorporating hidden Markov models for identifying protein kinase-specific phosphorylation sites
(2005-07-30)
Huang, HD
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Lee, TY
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Tzeng, SW
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Wu, LC
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Horng, JT
Protein phosphorylation, which is an important mechanism in posttranslational modification, affects essential cellular processes such as metabolism, cell signaling, differentiation, and membrane transportation. Proteins are phosphorylated by a variety of protein kinases. In this investigation, we develop a novel tool to computationally predict catalytic kinase-specific phosphorylation sites. The known phosphorylation sites from public domain data sources are categorized by their annotated protein kinases. Based on the concepts of profile Hidden Markov Models (HMM), computational models are trained from the kinase-specific groups of phosphorylation sites. After evaluating the trained models, we select the model with highest accuracy in each kinase-specific group and provide a Web-based prediction tool for identifying protein phosphorylation sites. The main contribution here is that we have developed a kinase-specific phosphorylation site prediction tool with both high sensitivity and specificity. (c) 2005 Wiley Periodicals, Inc.
MultiProtident: Identifying proteins using database search and protein-protein interactions
(2005-05-01)
Huang, HD
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Lee, TY
;
Wu, LC
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Lin, FM
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Juan, HF
Protein identification is important in proteomics. Proteomic analyses based on mass spectra (MS) constitute innovative ways to identify the components of protein complexes. Instruments can obtain the mass spectrum to an accuracy of 0.01 Da or better, but identification errors are inevitable. This study shows a novel tool, MultiProtldent, which can identify proteins using additional information about protein-protein interactions and protein functional associations. Both single and multiple Peptide Mass Fingerprints (PMFs) are input to MultiProtldent, which matches the PMFs to a theoretical peptide mass database. The relationships or interactions among proteins are considered to reduce false positives in PMF matching. Experiments to identify protein complexes reveal that MultiProtldent is highly promising. The website associated with this study is http://dbms104.csie.ncu.edu.tw/.