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H-X Zhou

Publications and source records attributed to H-X Zhou.

2 recordsLinked to original sources

Suppression of EGFR expression by antisense or small interference RNA inhibits U251 glioma cell growth in vitro and in vivo.

Epidermal growth factor receptor (EGFR) had been reported as one of the major responsible genes for malignant progression and phenotype reversion of gliomas, and has been used as one of the most important therapeutic targets. In the present study, small interference RNA (siRNA) and antisense EGFR expression constructs, which target sequences of human EGFR catalytic domain (2400-2420) and the 3'-coding region, respectively, were used to examine the growth inhibition effects on U251 glioma cells. Cell growth was significantly inhibited and G2/M arrest was observed in antisense- and siRNA-treated groups. Matrigel matrix demonstrated spotted cell clustering pattern in antisense- and siRNA-transfected U251 cells, indicating poor cell growth activities. In addition, the tumor volumes in U251 subcutaneous mice model treated with antisense and siRNA were significantly smaller than those treated with control siRNA and phosphate-buffered saline. Also, glial fibrillary acidic protein expression was upregulated in antisense- and siRNA-treated groups than the control groups. Our results demonstrated that antisense- or siRNA-targeting intracellular region of EGFR can inhibit EGFR expression, exerted growth inhibition effect on U251 glioma cells in vitro and in vivo. Consequently, siRNA expression plasmid-mediated gene therapy would be a new strategy in treatment of gliomas.

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Data-driven docking: HADDOCK's adventures in CAPRI.

We have shown previously that given high-resolution structures of the unbound molecules, structure determination of protein complexes is possible by including biochemical and/or biophysical data as highly ambiguous distance restraints in a docking approach. We applied this method, implemented in the HADDOCK (High Ambiguity Driven DOCKing) package (Dominguez et al., J Am Chem Soc 2003;125:1731-1737), to the targets in the fourth and fifth rounds of CAPRI. Here we describe our results and analyze them in detail. Special attention is given to the role of flexibility in our docking method and the way in which this improves the docking results. We describe extensions to our approach that were developed as a direct result of our participation in CAPRI. In addition to experimental information, we also included interface residue predictions from PPISP (Protein-Protein Interaction Site Predictor; Zhou and Shan, Proteins 2001;44:336-343), a neural network method. Using HADDOCK we were able to generate acceptable structures for 6 of the 8 targets, and to submit at least 1 acceptable structure for 5 of them. Of these 5 submissions, 3 were of medium quality (Targets 10, 11, and 15) and 2 of high quality (Targets 13 and 14). In all cases, predictions were obtained containing at least 40% of the correct epitope at the interface for both ligand and receptor simultaneously.

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