Li-Cheng Wu

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

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.

An expert system to classify microarray gene expression data using gene selection by decision tree

Gene selection can help the analysis of microarray gene expression data. However, it is very difficult to obtain a satisfactory classification result by machine learning techniques because of both the curse-of-dimensionality problem and the over-fitting problem. That is, the dimensions of the features are too large but the samples are too few. In this study, we designed an approach that attempts to avoid these two problems and then used it to select a small set of significant biomarker genes for diagnosis. Finally, we attempted to use these markers for the classification of cancer. This approach was tested the approach on a number of microarray datasets in order to demonstrate that it performs well and is both useful and reliable.

A genetic algorithm for multiple sequence alignment

Abstract.Multiple sequence alignment is an important tool in molecular sequence analysis. This paper presents genetic algorithms to solve multiple sequence alignments. Several data sets are tested and the experimental results are compared with other methods. We find our approach could obtain good performance in the data sets with high similarity and long sequences.The software can be found in http://rsdb.csie.ncu.edu.tw/tools/msa.htm.

An expert system to predict protein thermostability using decision tree

Protein thermostability information is closely linked to commercial production of many biomaterials. Recent developments have shown that amino acid composition, special sequence patterns and hydrogen bonds, disulfide bonds, salt bridges and so on are of considerable importance to thermostability. In this study, we present a system to integrate these various factors that predict protein thermostability. In this study, the features of proteins in the PGTdb are analyzed. We consider both structure and sequence features and correlation coefficients are incorporated into the feature selection algorithm. Machine learning algorithms are then used to develop identification systems and performances between the different algorithms are compared. In this research, two features, (E+F+M+R)/residue and charged/non-charged, are found to be critical to the thermostability of proteins. Although the sequence and structural models achieve a higher accuracy, sequence-only models provides sufficient accuracy for sequence-only thermostability prediction.

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.

Primer design for multiplex PCR using a genetic algorithm

Multiplex Polymerase chain reaction (PCR) is the term used when more than one pair of primers is used in a polymerase chain reaction. The goal of multiplex PCR is to amplify several segments of target DNA simultaneously and thereby to conserve template DNA, save time, and minimize expense. The success of the experiment is dependent on primer design. However, this can be a dreary task as there are many constrains such as melting temperatures, primer length, GC content and complementarity that need to be optimized to obtain a good PCR product. In our investigations, we found few primer design tools for multiplex PCR and there was no suitable tool for our partners who want to use a multiplex PCR genotypic assay. The tool draws on a genetic algorithm where stochastic approaches based on the concept of biological evolution, biological genetics and genetic operations on chromosomes are used to find an optimal solution for multiplex PCR. The presented experimental results indicate that the proposed algorithm is able to find a set of primer pairs that not only obey the design properties but also work in the same tube.

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

BackgroundMany 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.ResultsRINGdb 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/.ConclusionRINGdb is the only integrated database on the Internet to provide comprehensive RGS protein and GPCR information. This knowledgebase will be useful for clinical research, drug discovery and GPCR signaling pathway research.

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/.)

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/∼protein/NEW2/.