Biomedical subjects

Li-Cheng Wu

Publications and source records attributed to Li-Cheng Wu.

10 recordsLinked to original sources

PubMed2006

RINGdb: an integrated database for G protein-coupled receptors and regulators of G protein signaling.

BACKGROUND: Many marketed therapeutic agents have been developed to modulate the function of G protein-coupled receptors (GPCRs). The regulators of G-protein signaling (RGS proteins) are also being examined as potential drug targets. To facilitate clinical and pharmacological research, we have developed a novel integrated biological database called RINGdb to provide comprehensive and organized RGS protein and GPCR information. RESULTS: RINGdb contains information on mutations, tissue distributions, protein-protein interactions, diseases/disorders and other features, which has been automatically collected from the Internet and manually extracted from the literature. In addition, RINGdb offers various user-friendly query functions to answer different questions about RGS proteins and GPCRs such as their possible contribution to disease processes, the putative direct or indirect relationship between RGS proteins and GPCRs. RINGdb also integrates organized database cross-references to allow users direct access to detailed information. The database is now available at http://ringdb.csie.ncu.edu.tw/ringdb/. CONCLUSION: RINGdb is the only integrated database on the Internet to provide comprehensive RGS protein and GPCR information. This knowledge base will be useful for clinical research, drug discovery and GPCR signaling pathway research.

Amino Acid Sequence↗

PubMed2006

An agent-based system to discover protein-protein interactions, identify protein complexes and proteins with multiple peptide mass fingerprints.

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. The agent-based system is now available on the Web at http://dbms104.csie.ncu.edu.tw/ approximately protein/NEW2/.

Animals↗

PubMed2006

Detection of discriminative sequence motifs in proteins obtained from prokaryotes grown at various temperatures.

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.

Amino Acid Motifs↗

PubMed2006

Database to dynamically aid probe design for virus identification.

Viral infection poses a major problem for public health, horticulture, and animal husbandry, possibly causing severe health crises and economic losses. Viral infections can be identified by the specific detection of viral sequences in many ways. The microarray approach not only tolerates sequence variations of newly evolved virus strains, but can also simultaneously diagnose many viral sequences. Many chips have so far been designed for clinical use. Most are designed for special purposes, such as typing enterovirus infection, and compare fewer than 30 different viral sequences. None considers primer design, increasing the likelihood of cross hybridization to similar sequences from other viruses. To prevent this possibility, this work establishes a platform and database that provides users with specific probes of all known viral genome sequences to facilitate the design of diagnostic chips. This work develops a system for designing probes online. A user can select any number of different viruses and set the experimental conditions such as melting temperature and length of probe. The system then returns the optimal sequences from the database. We have also developed a heuristic algorithm to calculate the probe correctness and show the correctness of the algorithm. (The system that supports probe design for identifying viruses has been published on our web page http://bioinfo.csie.ncu.edu.tw/.)

Algorithms↗

PubMed2005

Incorporating hidden Markov models for identifying protein kinase-specific phosphorylation sites.

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.

Computational Biology↗

PubMed2004

PGTdb: a database providing growth temperatures of prokaryotes.

UNLABELLED: Included in Prokaryotic Growth Temperature database (PGTdb) are a total of 1334 temperature data from 1072 prokaryotic organisms, Bacteria and Archaea: PGTdb integrates microbial growth temperature data from literature survey with their nucleotide/protein sequence and protein structure data from related databases. A direct correlation is observed between the average growth temperature of an organism and the melting temperature of proteins from the organism. Therefore, this database is useful not only for microbiologists to obtain cultivation condition, but also for biochemists and structure biologists to study the correlation between protein sequences/structures and their thermostability. In addition, the taxonomy and ribosomal RNA sequence(s) of an organism are linked through NCBI Taxonomy and the Ribosomal RNA Operon Copy Number Database umdb, respectively. PGTdb is the only integrated database on the Internet to provide the growth temperature data of the prokaryotes and the combined information of their nucleotide/protein sequences, protein structures, taxonomy and phylogeny. AVAILABILITY: http://pgtdb.csie.ncu.edu.tw

Archaea↗

PubMed2004

Identifying the combination of genetic factors that determine susceptibility to cervical cancer.

Cervical cancer is common among women all over the world. 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.

Algorithms↗

PubMed2004

A probabilistic method to correlate ion pairs with protein thermostability.

Recent developments in research on the stability of proteins - specifically, comparisons of the ion pairs of homologous structures - show that ion pairs potentially contribute to the thermostability of proteins. This study proposes a probabilistic Bayesian statistical method to efficiently predict the thermostability of proteins based on the properties of ion pairs. The experimental results suggest that the numbers, types and bond lengths of ion pairs can be used to predict with high accuracy (up to 80%) the thermostability of functionally similar proteins. The predictions have high precision (99%), especially for hyperthermophilic proteins. Results for proteins with differing functions also indicate that the number of ion pairs is related to the thermostability of proteins, and that predictions of thermostability can also be made for proteins with different functions.

Algorithms↗

PubMed2002

The repetitive sequence database and mining putative regulatory elements in gene promoter regions.

At least 43% of the human genome is occupied by repetitive elements. Moreover, around 51% of the rice genome is occupied by repetitive elements. The analysis of repetitive elements reveals that repetitive elements in our genome may have been very important in the evolutionary genomics. The first part of this study is to describe a database of repetitive elements - RSDB. The RSDB database contains repetitive elements, which are classified into the following categories: exact, tandem, and similar. The interfaces needed to query and show the results and statistical data, such as the relationship between repetitive elements and genes, cross-references of repetitive elements among different organisms, and so on, are provided. The second part of this study then attempts to mine the putative binding site for information on how combinations of the known regulatory sites and overrepresented repetitive elements in RSDB are distributed in the promoter regions of groups of functionally related genes. The overrepresented repetitive elements appearing in the associations are possible transcription factor binding sites. Our proposed approach is applied to Saccharomyces cerevisiae and the promoter regions of Yeast ORFs. The complete contents of RSDB and partial putative binding sites are available to the public at www.rsdb.csie.ncu.edu.tw. The readers may download partial query results.

Animals↗

PubMedDate not supplied

MultiProtIdent: identifying proteins using database search and protein-protein interactions.

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, MultiProtIdent, 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 MultiProtIdent, 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 MultiProtIdent is highly promising. The website associated with this study is http://dbms104.csie.ncu.edu.tw/.

Algorithms↗