张学武
主要从事新型交叉学科基因组学,病毒学,分子生物学和生物化工的研究。
个性化签名
- 姓名:张学武
- 目前身份:
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学术头衔:
博士生导师
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学科领域:
生物化学
- 研究兴趣:主要从事新型交叉学科基因组学,病毒学,分子生物学和生物化工的研究。
张学武教授1983年获湖南师范大学数学学士学位,1990年获中山大学生物学硕士学位,1993年获中山大学生物学博士学位。毕业后留中山大学工作。1998-2003年先后在香港大学、加拿大University of British Columbia、University of Manitoba、美国University of California, Los Angeles做博士后研究;2003-2005年作为香港政府海外优秀人才引进计划获得者,在香港大学任研究助理教授。2005引进到华南理工大学任教授。主要从事新型交叉学科基因组学,病毒学,分子生物学和生物化工的研究。自1996年以来共发表SCI收录论文45篇。最近5年(2003-2008)发表SCI收录论文20篇,平均影响因子3.43,最高影响因子10.947。还有一篇研究论文还获得有机化学和药物化学领域国际著名杂志Bioorganic & Medicinal Chemistry最多引用论文奖 (Most Cited Paper 2003-2006 Award)。
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541
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成果数
9
张学武, Xue Wu Zhang*, , Yee Leng Yap, Dong Wei, Feng Chen, and Antoine Danchin
,-0001,():
-1年11月30日
The precise diagnosis of cancer type based on microarray data is of particular importance and is alsoa challenging task. We have devised a novel pattern recognition procedure based on independentcomponent analysis (ICA). Different from the conventional cancer classification methods, which are limitedin their clinical applicability of cancer diagnosis, our method extracts explicitly, by ICA algorithm, a set ofspecific diagnostic patterns of normal and tumor tissues corresponding to a set of biomarkers for clinicaluse. We validated our procedure with the colon and prostate cancer data sets and achieved good diagnosis(490%) on the data sets studied here. This technique is also suitable for the identification of diagnosticexpression patterns for other human cancers and demonstrates the feasibility of simple and accuratemolecular cancer diagnostics for clinical implementation.
microarray, cancer, ICA, diagnosis, pattern, biomarkers identification
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张学武, Yee Leng Yap, , *, Maria P. Wong, Xue Wu Zhang, David Hernandez, Robin Gras, David K. Smith and Antoine Danchin
Nucleic Acids Research, 2005, Vol. 33, No. 1409-421,-0001,():
-1年11月30日
Gene transcription in a set of 49 human primary lungadenocarcinomas and 9 normal lung tissue sampleswas examined using Affymetrix GeneChip technology.A total of 3442 genes, called the set MAD,were found to be either up- or down-regulated byat least 2-fold between the two phenotypes. Genesassigned to a particular gene ontology term werefound, in many cases, to be significantly unevenlydistributed between the genes in and outside MAD.Terms that were overrepresented in MAD includedfunctions directly implicated in the cancer cellmetabolism. Based on their functional roles andexpression profiles, genes in MAD were groupedinto likely co-regulated gene sets. Highly conservedsequences in the 5 kb region upstream of the genesin these sets were identified with the motif discoverytool, MoDEL. Potential oncogenic transcription factorsand their corresponding binding sites wereidentified in these conserved regions using theTRANSFAC 8.3 database. Several of the transcriptionfactors identified in this study have beenshown elsewhere to be involved in oncogenic processes.This study searched beyond phenotypicgene expression profiles in cancer cells, in orderto identify the more important regulatory transcriptionfactors that caused these aberrations in geneexpression.
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【期刊论文】Generation of predictive pharmacophore model for SARS-coronavirusmain proteinase
张学武, XueWu Zhang *, Yee LengYap, Ralf M. Altmeyer
European Journal of Medicinal Chemistry 40(2005)57-62,-0001,():
-1年11月30日
Pharmacophore-based virtual screening is an effective, inexpensive and fast approach to discovering useful starting points for drug discovery.In this study, we developed a pharmacophore model for the main proteinase of severe acute respiratory syndrome coronavirus (SARSCoV).Then we used this pharmacophore model to search NCI 3D database including 250, 251 compounds and identified 30 existing drugscontaining the pharmacophore query. Among them are six compounds that already exhibited anti-SARS-CoV activity experimentally. Thismeans that our pharmacophore model can lead to the discovery of potent anti-SARS-CoV inhibitors or promising lead compounds for furtherSARS-CoV main proteinase inhibitor development.
SARS-CoV, Pharmacophore, Virtual screening, Drug design
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【期刊论文】Testing the hypothesis of a recombinant origin of theSARS-associated coronavirus
张学武, X.W. Zhang, Y. L. Yap, and A. Danchin
,-0001,():
-1年11月30日
The origin of severe acute respiratory syndrome-associated coronavirus(SARS-CoV) is still a matter of speculation, although more than oneyear has passed since the onset of the SARS outbreak. In this study, we implementeda 3-step strategy to test the intriguing hypothesis that SARS-CoVmight have been derived from a recombinant virus. First, we blasted the wholeSARS-CoV genome against a virus database to search viruses of interest. Second,we employed 7 recombination detection techniques well documented in successfullydetecting recombination events to explore the presence of recombinationin SARS-CoV genome. Finally, we conducted phylogenetic analyses to furtherexplore whether recombination has indeed occurred in the course of coronaviruseshistory predating the emergence of SARS-CoV. Surprisingly, we found that 7putative recombination regions, located in Replicase 1ab and Spike protein, existbetween SARS-CoV and other 6 coronaviruses: porcine epidemic diarrheavirus (PEDV), transmissible gastroenteritis virus (TGEV), bovine coronavirus(BCoV), human coronavirus 229E (HCoV), murine hepatitis virus (MHV), andavian infectious bronchitis virus (IBV). Thus, our analyses substantiate the presenceof recombination events in history that led to the SARS-CoV genome.Like the other coronaviruses used in the analysis, SARS-CoV is also a mosaicstructure.
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【期刊论文】Classification between normal and tumor tissues based on thepair-wise gene expression ratio
张学武, YeeLeng Yap*, XueWu Zhang, MT Ling, XiangHong Wang, YC Wong, and Antoine Danchin
BMC Cancer 2004, 4: 72,-0001,():
-1年11月30日
Background: Precise classification of cancer types is critically important for early cancer diagnosis and treatment. Numerousefforts have been made to use gene expression profiles to improve precision of tumor classification. However, reliable cancerrelatedsignals are generally lacking.Method: Using recent datasets on colon and prostate cancer, a data transformation procedure from single gene expression topair-wise gene expression ratio is proposed. Making use of the internal consistency of each expression profiling dataset thistransformation improves the signal to noise ratio of the dataset and uncovers new relevant cancer-related signals (features). Theefficiency in using the transformed dataset to perform normal/tumor classification was investigated using feature partitioningwith informative features (gene annotation) as discriminating axes (single gene expression or pair-wise gene expression ratio).Classification results were compared to the original datasets for up to 10-feature model classifiers.Results: 82 and 262 genes that have high correlation to tissue phenotype were selected from the colon and prostate datasetsrespectively. Remarkably, data transformation of the highly noisy expression data successfully led to lower the coefficient ofvariation (CV) for the within-class samples as well as improved the correlation with tissue phenotypes. The transformed datasetexhibited lower CV when compared to that of single gene expression. In the colon cancer set, the minimum CV decreased from45.3% to 16.5%. In prostate cancer, comparable CV was achieved with and without transformation. This improvement in CV,coupled with the improved correlation between the pair-wise gene expression ratio and tissue phenotypes, yielded higherclassification efficiency, especially with the colon dataset-from 87.1% to 93.5%. Over 90% of the top ten discriminating axes inboth datasets showed significant improvement after data transformation. The high classification efficiency achieved suggestedthat there exist some cancer-related signals in the form of pair-wise gene expression ratio.Conclusion: The results from this study indicated that: 1) in the case when the pair-wise expression ratio transformationachieves lower CV and higher correlation to tissue phenotypes, a better classification of tissue type will follow. 2) thecomparable classification accuracy achieved after data transformation suggested that pair-wise gene expression ratio betweensome pairs of genes can identify reliable markers for cancer.
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【期刊论文】Putative structure and function of ORF3 in SARS coronavirus
张学武, Xue Wu Zhang*, Yee Leng Yap
Journal of Molecular Structure: THEOCHEM 715(2005)55-58,-0001,():
-1年11月30日
Based on molecular modeling techniques we constructed a rational 3D model of ORF3 in SARS coronavirus (SARS-CoV). Our studies suggest that the function of ORF3 could be involved in FAD/NAD binding according to its predicted structure and comparison with other structure neighbors. Furthermore, we identified three pairs of non-canonical N–H/p interactions in the structure of ORF3, which can make contributions to the stability of protein structure. These results provide important clues for better understanding of SARS-CoV ORF3 and trying new therapeutic strategies.
SARS-CoV, ORF3, 3D structure, Function, Non-canonical interaction
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张学武, Xue Wu Zhang* and Yee Leng Yap
Bioorganic & Medicinal Chemistry 12(2004)2517-2521,-0001,():
-1年11月30日
The SARS-associated coronavirus (SARS-CoV) main proteinase is a key enzyme in viral polyprotein processing. To allow structure-based design of drugs directed at SARS-CoV main proteinase, we predicted its binding pockets and affinities with existing HIV, psychotic and parasite drugs (lopinavir, ritonavir, niclosamide and promazine), which show signs of inhibiting the replication of SARS-CoV. Our results suggest that these drugs and another two HIV inhibitors (PNU and UC2) could be used as templates for designing SARS-CoV proteinase inhibitors.
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张学武, Xue Wu Zhang* and Yee Leng Yap
Bioorganic & Medicinal Chemistry 12(2004)2219-2223,-0001,():
-1年11月30日
The main proteinase of SARS-associated coronavirus (SARS-CoV) plays an important role in viral transcription andreplication, and is an attractive target for anti-SARS drug development. The important thing is to understand its binding mechanismwith possible ligands. In this study, we investigated possible noncanonical interactions, potential inhibitors, and bindingpockets in the main proteinase of SARS-CoV based on its recently determined crystal structure. These findings provide a wide clueto searching for anti-SARS drug. Interestingly, we found that similar structure patterns exist in SARS-CoV main proteinase withPoliovirus 3c Proteinase, Rhinovirus 3c Protease, Nsp4 Proteinase From Equine Arteritis Virus, Hepatitis C Virus Ns3 Protease,Hepatitis A Virus 3c Protease, and Dengue Virus Ns3 Protease. It suggests that the available drugs in these viruses could be used tofight SARS disease.
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张学武, Yee Leng Yap*, Xue Wu Zhang and Antoine Danchin
BMC Bioinformatics 2003, 4,-0001,():
-1年11月30日
Background: The exact origin of the cause of the Severe Acute Respiratory Syndrome (SARS) isstill an open question. The genomic sequence relationship of SARS-CoV with 30 different singlestrandedRNA (ssRNA) viruses of various families was studied using two non-standard approaches.Both approaches began with the vectorial profiling of the tetra-nucleotide usage pattern V for eachvirus. In approach one, a distance measure of a vector V, based on correlation coefficient wasdevised to construct a relationship tree by the neighbor-joining algorithm. In approach two, amultivariate factor analysis was performed to derive the embedded tetra-nucleotide usage patterns.These patterns were subsequently used to classify the selected viruses.Results: Both approaches yielded relationship outcomes that are consistent with the known virusclassification. They also indicated that the genome of RNA viruses from the same family conformto a specific pattern of word usage. Based on the correlation of the overall tetra-nucleotide usagepatterns, the Transmissible Gastroenteritis Virus (TGV) and the Feline CoronaVirus (FCoV) areclosest to SARS-CoV. Surprisingly also, the RNA viruses that do not go through a DNA stagedisplayed a remarkable discrimination against the CpG and UpA di-nucleotide (z = -77.31, -52.48respectively) and selection for UpG and CpA (z = 65.79,49.99 respectively). Potential factorsinfluencing these biases are discussed.Conclusion: The study of genomic word usage is a powerful method to classify RNA viruses. Thecongruence of the relationship outcomes with the known classification indicates that there existphylogenetic signals in the tetra-nucleotide usage patterns, that is most prominent in the replicaseopen reading frames.
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