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Y Z Chen

Publications and source records attributed to Y Z Chen.

At least 19 recordsLinked to original sources
PubMed2005

Prediction of functional class of novel viral proteins by a statistical learning method irrespective of sequence similarity.

The function of a substantial percentage of the putative protein-coding open reading frames (ORFs) in viral genomes is unknown. As their sequence is not similar to that of proteins of known function, the function of these ORFs cannot be assigned on the basis of sequence similarity. Methods complement or in combination with sequence similarity-based approaches are being explored. The web-based software SVMProt (http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi) to some extent assigns protein functional family irrespective of sequence similarity and has been found to be useful for studying distantly related proteins [Cai, C.Z., Han, L.Y., Ji, Z.L., Chen, X., Chen, Y.Z., 2003. SVM-Prot: web-based support vector machine software for functional classification of a protein from its primary sequence. Nucleic Acids Res. 31(13): 3692-3697]. Here 25 novel viral proteins are selected to test the capability of SVMProt for functional family assignment of viral proteins whose function cannot be confidently predicted on by sequence similarity methods at present. These proteins are without a sequence homolog in the Swissprot database, with its precise function provided in the literature, and not included in the training sets of SVMProt. The predicted functional classes of 72% of these proteins match the literature-described function, which is compared to the overall accuracy of 87% for SVMProt functional class assignment of 34582 proteins. This suggests that SVMProt to some extent is capable of functional class assignment irrespective of sequence similarity and it is potentially useful for facilitating functional study of novel viral proteins.

Artificial Intelligence↗
PubMed2005

Rapid non-genomic inhibitory effects of glucocorticoids on human neutrophil degranulation.

BACKGROUND: Glucocorticoids acting as anti-inflammatory or immunosuppressive drugs have been shown to exert most of their effects genomically. Recent findings suggest that non-genomic activity might be relatively more important in mediating the therapeutic effects of high-dose pulsed glucocorticoid. However, few non-genomic anti-inflammatory effects were reported, much less non-genomic mechanisms. OBJECTIVE: This study was performed to investigate the nongenomic effects of glucocorticoids on human neutrophil degranulation. METHODS: Purified human neutrophils were pretreated with 6 alpha-methylprednisolone or hydrocortisone for 5 min, and then primed with N-formyl-methionyl-leucyl-phenylalanine (fMLP) (10(-6) M) or phorbol myristate acetate (PMA) (50 ng/ml) in the presence of cytochalasin B. The release of two markers of neutrophil granules, lactoferrin and myeloperoxidase, was measured by ELISA and enzymology methods respectively. RESULTS: Both 6 alpha-methylprednisolone (10(-5)-10(-4) M) and hydrocortisone (10(-4) M) showed significant inhibitory effects on neutrophil degranulation within 5 min after fMLP administration. For PMA stimulated degranulation, 6 alpha-methylprednisolone (10(-4) M) showed significant inhibitory effects (p < 0.01), while hydrocortisone (10(-4) M) only showed an inhibitory tendency (P > 0.05). Neither RU486 (10(-5) M) nor cycloheximide (10(-4) M) could alter the inhibitory effects of glucocorticoids. CONCLUSION: Our results demonstrate that megadoses of glucocorticoids exert rapid inhibitory effects on human neutrophil degranulation at the cellular level via a new mechanism that is independent of corticosteroid type II receptor occupation or protein synthesis. We infer that these effects may be very important when glucocorticoids act as anti-inflammatory drugs during pulse therapy.

Cell Degranulation↗
PubMed2004

Predicting functional family of novel enzymes irrespective of sequence similarity: a statistical learning approach.

The function of a protein that has no sequence homolog of known function is difficult to assign on the basis of sequence similarity. The same problem may arise for homologous proteins of different functions if one is newly discovered and the other is the only known protein of similar sequence. It is desirable to explore methods that are not based on sequence similarity. One approach is to assign functional family of a protein to provide useful hint about its function. Several groups have employed a statistical learning method, support vector machines (SVMs), for predicting protein functional family directly from sequence irrespective of sequence similarity. These studies showed that SVM prediction accuracy is at a level useful for functional family assignment. But its capability for assignment of distantly related proteins and homologous proteins of different functions has not been critically and adequately assessed. Here SVM is tested for functional family assignment of two groups of enzymes. One consists of 50 enzymes that have no homolog of known function from PSI-BLAST search of protein databases. The other contains eight pairs of homologous enzymes of different families. SVM correctly assigns 72% of the enzymes in the first group and 62% of the enzyme pairs in the second group, suggesting that it is potentially useful for facilitating functional study of novel proteins. A web version of our software, SVMProt, is accessible at http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi.

Artificial Intelligence↗
PubMed2004

MoViES: molecular vibrations evaluation server for analysis of fluctuational dynamics of proteins and nucleic acids.

Analysis of vibrational motions and thermal fluctuational dynamics is a widely used approach for studying structural, dynamic and functional properties of proteins and nucleic acids. Development of a freely accessible web server for computation of vibrational and thermal fluctuational dynamics of biomolecules is thus useful for facilitating the relevant studies. We have developed a computer program for computing vibrational normal modes and thermal fluctuational properties of proteins and nucleic acids and applied it in several studies. In our program, vibrational normal modes are computed by using modified AMBER molecular mechanics force fields, and thermal fluctuational properties are computed by means of a self-consistent harmonic approximation method. A web version of our program, MoViES (Molecular Vibrations Evaluation Server), was set up to facilitate the use of our program to study vibrational dynamics of proteins and nucleic acids. This software was tested on selected proteins, which show that the computed normal modes and thermal fluctuational bond disruption probabilities are consistent with experimental findings and other normal mode computations. MoViES can be accessed at http://ang.cz3.nus.edu.sg/cgi-bin/prog/norm.pl.

Computational Biology↗
PubMed2004

Enzyme family classification by support vector machines.

One approach for facilitating protein function prediction is to classify proteins into functional families. Recent studies on the classification of G-protein coupled receptors and other proteins suggest that a statistical learning method, Support vector machines (SVM), may be potentially useful for protein classification into functional families. In this work, SVM is applied and tested on the classification of enzymes into functional families defined by the Enzyme Nomenclature Committee of IUBMB. SVM classification system for each family is trained from representative enzymes of that family and seed proteins of Pfam curated protein families. The classification accuracy for enzymes from 46 families and for non-enzymes is in the range of 50.0% to 95.7% and 79.0% to 100% respectively. The corresponding Matthews correlation coefficient is in the range of 54.1% to 96.1%. Moreover, 80.3% of the 8,291 correctly classified enzymes are uniquely classified into a specific enzyme family by using a scoring function, indicating that SVM may have certain level of unique prediction capability. Testing results also suggest that SVM in some cases is capable of classification of distantly related enzymes and homologous enzymes of different functions. Effort is being made to use a more comprehensive set of enzymes as training sets and to incorporate multi-class SVM classification systems to further enhance the unique prediction accuracy. Our results suggest the potential of SVM for enzyme family classification and for facilitating protein function prediction. Our software is accessible at http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi.

Amino Acid Sequence↗
PubMed2004

TRMP: a database of therapeutically relevant multiple pathways.

UNLABELLED: Disease processes often involve crosstalks between proteins in different pathways. Different proteins have been used as separate therapeutic targets for the same disease. Synergetic targeting of multiple targets has been explored in combination therapy of a number of diseases. Potential harmful interactions of multiple targeting have also been closely studied. To facilitate mechanistic study of drug actions and a more comprehensive understanding the relationship between different targets of the same disease, it is useful to develop a database of known therapeutically relevant multiple pathways (TRMPs). Information about non-target proteins and natural small molecules involved in these pathways also provides useful hint for searching new therapeutic targets and facilitate the understanding of how therapeutic targets interact with other molecules in performing specific tasks. The TRMPs database is designed to provide information about such multiple pathways along with related therapeutic targets, corresponding drugs/ligands, targeted disease conditions, constituent individual pathways, structural and functional information about each protein in the pathways. Cross links to other databases are also introduced to facilitate the access of information about individual pathways and proteins. AVAILABILITY: This database can be accessed at http://bidd.nus.edu.sg/group/trmp/trmp.asp and it currently contains 11 entries of multiple pathways, 97 entries of individual pathways, 120 targets covering 72 disease conditions together with 120 sets of drugs directed at each of these targets. Each entry can be retrieved through multiple methods including multiple pathway name, individual pathway name and disease name. SUPPLEMENTARY INFORMATION: http://bidd.nus.edu.sg/group/trmp/sm.pdf

Biomarkers↗
PubMed2004

Prediction of torsade-causing potential of drugs by support vector machine approach.

In an effort to facilitate drug discovery, computational methods for facilitating the prediction of various adverse drug reactions (ADRs) have been developed. So far, attention has not been sufficiently paid to the development of methods for the prediction of serious ADRs that occur less frequently. Some of these ADRs, such as torsade de pointes (TdP), are important issues in the approval of drugs for certain diseases. Thus there is a need to develop tools for facilitating the prediction of these ADRs. This work explores the use of a statistical learning method, support vector machine (SVM), for TdP prediction. TdP involves multiple mechanisms and SVM is a method suitable for such a problem. Our SVM classification system used a set of linear solvation energy relationship (LSER) descriptors and was optimized by leave-one-out cross validation procedure. Its prediction accuracy was evaluated by using an independent set of agents and by comparison with results obtained from other commonly used classification methods using the same dataset and optimization procedure. The accuracies for the SVM prediction of TdP-causing agents and non-TdP-causing agents are 97.4 and 84.6% respectively; one is substantially improved against and the other is comparable to the results obtained by other classification methods useful for multiple-mechanism prediction problems. This indicates the potential of SVM in facilitating the prediction of TdP-causing risk of small molecules and perhaps other ADRs that involve multiple mechanisms.

Algorithms↗
PubMed2004

APP induces neuronal apoptosis through APP-BP1-mediated downregulation of beta-catenin.

Alzheimer's disease (AD) is a neurodegenerative disease associated with progressive dementia. This mini-review focuses on how the amyloid precursor protein (APP) plays a central role in AD and Down syndrome as the regulator of the APP-BP1/hUba3 activated neddylation pathway. It is argued that the physiological function of APP is to downregulate the level of beta-catenin. However, this APP function is abnormally amplified in patients with familial AD (FAD) mutations in APP and presenilins, resulting in the hyperactivation of neddylation and the decrease of beta-catenin below a threshold level. Evidence in the literature is summarized to show that dysfunction of APP in downregulating beta-catenin may underlie the mechanism of neuronal death in AD and Down syndrome.

Alzheimer Disease↗
PubMed2003

SVM-Prot: Web-based support vector machine software for functional classification of a protein from its primary sequence.

Prediction of protein function is of significance in studying biological processes. One approach for function prediction is to classify a protein into functional family. Support vector machine (SVM) is a useful method for such classification, which may involve proteins with diverse sequence distribution. We have developed a web-based software, SVMProt, for SVM classification of a protein into functional family from its primary sequence. SVMProt classification system is trained from representative proteins of a number of functional families and seed proteins of Pfam curated protein families. It currently covers 54 functional families and additional families will be added in the near future. The computed accuracy for protein family classification is found to be in the range of 69.1-99.6%. SVMProt shows a certain degree of capability for the classification of distantly related proteins and homologous proteins of different function and thus may be used as a protein function prediction tool that complements sequence alignment methods. SVMProt can be accessed at http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi.

Amino Acid Sequence↗
PubMed2003

KDBI: Kinetic Data of Bio-molecular Interactions database.

Understanding of cellular processes and underlying molecular events requires knowledge about different aspects of molecular interactions, networks of molecules and pathways in addition to the sequence, structure and function of individual molecules involved. Databases of interacting molecules, pathways and related chemical reaction equations have been developed. The kinetic data for these interactions, which is important for mechanistic investigation, quantitative study and simulation of cellular processes and events, is not provided in the existing databases. We introduce a new database of Kinetic Data of Bio-molecular Interactions (KDBI) aimed at providing experimentally determined kinetic data of protein-protein, protein-RNA, protein-DNA, protein-ligand, RNA-ligand, DNA-ligand binding or reaction events described in the literature. KDBI contains information about binding or reaction event, participating molecules (name, synonyms, molecular formula, classification, SWISS-PROT AC or CAS number), binding or reaction equation, kinetic data and related references. The kinetic data is in terms of one or a combination of the following quantities as given in the literature of a particular event: association/dissociation or on/off rate constant, first/second/third/. order rate constant, equilibrium rate constant, catalytic rate constant, equilibrium association/dissociation constant, inhibition constant and binding affinity constant. Each entry can be retrieved through protein or nucleic acid or ligand name, SWISS-PROT AC number, ligand CAS number and full-text search of a binding or reaction event. KDBI currently contains 8273 entries of biomolecular binding or reaction events involving 1380 proteins, 143 nucleic acids and 1395 small molecules. Hyperlinks are provided for accessing references in Medline and available 3D structures in PDB and NDB. This database can be accessed at http://xin.cz3.nus.edu.sg/group/kdbi/kdbi.asp.

DNA↗
PubMed2003

Protein function classification via support vector machine approach.

Support vector machine (SVM) is introduced as a method for the classification of proteins into functionally distinguished classes. Studies are conducted on a number of protein classes including RNA-binding proteins; protein homodimers, proteins responsible for drug absorption, proteins involved in drug distribution and excretion, and drug metabolizing enzymes. Testing accuracy for the classification of these protein classes is found to be in the range of 84-96%. This suggests the usefulness of SVM in the classification of protein functional classes and its potential application in protein function prediction.

Amino Acid Sequence↗
PubMed2003

Correlation between normal modes in the 20-200 cm-1 frequency range and localized torsion motions related to certain collective motions in proteins.

In certain biologically relevant collective motions, such as protein domain motions and sub-domain motions, large amplitude movements are localized in one or a few flexible regions consisting of a small number of residues. This paper explores the possible use of normal mode analysis in probing localized vibrational torsion motions in these flexible regions that may be related to certain collective motions. The normal modes of 10 structures of five proteins in different conformation (TRP repressor, calmodulin, calbindin D(9k), HIV-1 protease and troponin C), known to have shear or hinge domain or sub-domain motion, respectively, are analyzed. Our study identifies, for each structure, unique normal modes in the 20-200 cm-1 frequency range, whose corresponding motions are primarily concentrated in the region where large amplitude torsion movements of a known domain or sub-domain motion occur. This suggests possible correlation between normal modes at 20-200 cm-1 frequency range and initial fluctuational motions leading to localized collective motions in proteins, and thus the potential application of normal mode analysis in facilitating the study of biologically important localized motions in biomolecules.

Calbindins↗
PubMed2002

TTD: Therapeutic Target Database.

A number of proteins and nucleic acids have been explored as therapeutic targets. These targets are subjects of interest in different areas of biomedical and pharmaceutical research and in the development and evaluation of bioinformatics, molecular modeling, computer-aided drug design and analytical tools. A publicly accessible database that provides comprehensive information about these targets is therefore helpful to the relevant communities. The Therapeutic Target Database (TTD) is designed to provide information about the known therapeutic protein and nucleic acid targets described in the literature, the targeted disease conditions, the pathway information and the corresponding drugs/ligands directed at each of these targets. Cross-links to other databases are also introduced to facilitate the access of information about the sequence, 3D structure, function, nomenclature, drug/ligand binding properties, drug usage and effects, and related literature for each target. This database can be accessed at http://xin.cz3.nus.edu.sg/group/ttd/ttd.asp and it currently contains entries for 433 targets covering 125 disease conditions along with 809 drugs/ligands directed at each of these targets. Each entry can be retrieved through multiple methods including target name, disease name, drug/ligand name, drug/ligand function and drug therapeutic classification.

Databases, Nucleic Acid↗
PubMed2002

CLiBE: a database of computed ligand binding energy for ligand-receptor complexes.

Consideration of binding competitiveness of a drug candidate against natural ligands and other drugs that bind to the same receptor site may facilitate the rational development of a candidate into a potent drug. A strategy that can be applied to computer-aided drug design is to evaluate ligand-receptor interaction energy or other scoring functions of a designed drug with that of the relevant ligands known to bind to the same binding site. As a tool to facilitate such a strategy, a database of ligand-receptor interaction energy is developed from known ligand-receptor 3D structural entries in the Protein Databank (PDB). The Energy is computed based on a molecular mechanics force field that has been used in the prediction of therapeutic and toxicity targets of drugs. This database also contains information about ligand function and other properties and it can be accessed at http://xin.cz3.nus.edu.sg/group/CLiBE.asp. The computed energy components may facilitate the probing of the mode of action and other profiles of binding. A number of computed energies of some PDB ligand-receptor complexes in this database are studied and compared to experimental binding affinity. A certain degree of correlation between the computed energy and experimental binding affinity is found, which suggests that the computed energy may be useful in facilitating a qualitative analysis of drug binding competitiveness.

Binding, Competitive↗
PubMed2002

ADME-AP: a database of ADME associated proteins.

Drug absorption, distribution, metabolism and excretion (ADME) often involve interaction of a drug with specific proteins. Knowledge about these ADME-associated proteins is important in facilitating the study of the molecular mechanism of disposition and individual response as well as therapeutic action of drugs. It is also useful in the development and testing of pharmacokinetics prediction tools. Several databases describing specific classes of ADME-associated proteins have appeared. A new database, ADME-associated proteins (ADME-AP), is introduced to provide comprehensive information about all classes of ADME-associated proteins described in the literature including physiological function of each protein, pharmacokinetic effect, ADME classification, direction and driving force of disposition, location and tissue distribution, substrates, synonyms, gene name and protein availability in other species. Cross-links to other databases are also provided to facilitate the access of information about the sequence, 3D structure, function, polymorphisms, genetic disorders, nomenclature, ligand binding properties and related literatures of each protein. ADME-AP currently contains entries for 321 proteins and 964 substrates.

Databases, Protein↗
PubMed2002

Computer automated prediction of potential therapeutic and toxicity protein targets of bioactive compounds from Chinese medicinal plants.

Understanding the molecular mechanism and pharmacology of bioactive compounds from Chinese medicinal plants (CMP) is important in facilitating scientific evaluation of novel therapeutic approaches in traditional Chinese medicine. It is also of significance in new drug development based on the mechanism of Chinese medicine. A key step towards this task is the determination of the therapeutic and toxicity protein targets of CMP compounds. In this work, newly developed computer software INVDOCK is used for automated identification of potential therapeutic and toxicity targets of several bioactive compounds isolated from Chinese medicinal plants. This software searches a protein database to find proteins to which a CMP compound can bind or weakly bind. INVDOCK results on three CMP compounds (allicin, catechin and camptotecin) show that 60% of computer-identified potential therapeutic protein targets and 27% of computer-identified potential toxicity targets have been implicated or confirmed by experiments. This software may potentially be used as a relatively fast-speed and low-cost tool for facilitating the study of molecular mechanism and pharmacology of bioactive compounds from Chinese medicinal plants and natural products from other sources.

Animals↗
PubMed2002

Construction of a dense comparative map between human chromosome 1p36-->p35 and swine chromosome 6 by using human sequence-tagged sites.

Construction of a comprehensive comparative map between swine and human chromosomes is a prerequisite, in order to select candidate swine genes for traits from the human genome database as well as to understand the evolutionary process of the two species. The present study attempted to use 910 sequence-tagged sites (STSs) localized in human chromosome (HSA) 1p36-->p35 (35 Mbp) for radiation hybrid (RH) mapping to swine chromosomes (SSCs). Out of the 910 STSs subjected to amplification of swine orthologues, primer pairs for 13 STSs were found to amplify the respective orthologues and the STSs were assigned to SSCs. Eleven STSs were assigned to SSC6 in the same order as that in HSA1: SSC6cen-(SHGC-150)-(A006H31)-(X82877)-(A007E03)-(IB404)-(stGDB:371372)-(stSG31658)-(A009Q18)-(stSG14201/A009C01)-(H08335)-qter. One of the remaining two STSs, WI-20819, was assigned to SSCX, and the other, R91D18R, was not linked to any first-generation markers of the IMpRH map with a lod score greater than 3.

Animals↗