XueWu Zhang | PubMed Health

PubMed2005

Structural analysis of inhibition mechanisms of aurintricarboxylic acid on SARS-CoV polymerase and other proteins.

We recently published experimental results that indicated Aurintricarboxylic Acid (ATA) could selectively inhibit SARS-CoV replication inside host cells by greater than 1000 times. This inhibition suggested that ATA could be developed as potent anti-viral drug. Here, to extend our experimental observation, we have incorporated protein structural studies (with positive/negative controls) to investigate the potential binding modes/sites of ATA onto RNA-dependent RNA polymerase (RdRp) from SARS-CoV and other pathogenic positive-strand RNA-viruses, as well as other proteins in SARS-CoV based on the fact that ATA binds to Ca2+-activated neutral protease (m-calpain), the protein tyrosine phosphatase (PTP) and HIV integrase which have existing crystal structures. Eight regions with homologous 3D-conformation were derived for 10 proteins of interest. One of the region, Rbinding (754-766 in SARS-CoV's RdRp), located in the palm sub-domain mainly constituted of anti-parallel beta-strand-turn-beta-strand hairpin structures that covers two of the three RdRp catalytic sites (Asp 760, Asp761), was also predicted by molecular docking method (based on free energy of binding DeltaG) to be important binding motif recognized by ATA. The existence of this strictly conserved region that incorporated catalytic residues, coupled with the homologous ATA binding pockets and their consistent DeltaG values, suggested strongly ATA may be involved in an analogous inhibition mechanism of SARS-COV's RdRp in concomitant to the case in m-calpain, PTP and HIV integrase.

Amino Acid Sequence↗

PubMed2004

Classification between normal and tumor tissues based on the pair-wise gene expression ratio.

BACKGROUND: Precise classification of cancer types is critically important for early cancer diagnosis and treatment. Numerous efforts have been made to use gene expression profiles to improve precision of tumor classification. However, reliable cancer-related signals are generally lacking. METHOD: Using recent datasets on colon and prostate cancer, a data transformation procedure from single gene expression to pair-wise gene expression ratio is proposed. Making use of the internal consistency of each expression profiling dataset this transformation improves the signal to noise ratio of the dataset and uncovers new relevant cancer-related signals (features). The efficiency in using the transformed dataset to perform normal/tumor classification was investigated using feature partitioning with 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 datasets respectively. Remarkably, data transformation of the highly noisy expression data successfully led to lower the coefficient of variation (CV) for the within-class samples as well as improved the correlation with tissue phenotypes. The transformed dataset exhibited lower CV when compared to that of single gene expression. In the colon cancer set, the minimum CV decreased from 45.3% 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 higher classification efficiency, especially with the colon dataset - from 87.1% to 93.5%. Over 90% of the top ten discriminating axes in both datasets showed significant improvement after data transformation. The high classification efficiency achieved suggested that 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 transformation achieves lower CV and higher correlation to tissue phenotypes, a better classification of tissue type will follow. 2) the comparable classification accuracy achieved after data transformation suggested that pair-wise gene expression ratio between some pairs of genes can identify reliable markers for cancer.

Colon↗