Anti-CASP9 polyclonal antibody

Background: Although tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) can selectively induce apoptosis in some non-small cell lung cancer (NSCLC) cells, some NSCLC cells exhibit TRAIL-resistance. The underlying mechanisms that regulate TRAIL sensitivity in NSCLC cells are not well understood. The objective of this study was to investigate molecular regulators of the TRAIL pathway in NSCLC cells. Methods: The TRAIL-sensitive NSCLC cell line NCI-H358 and a TRAIL-resistant cell line A549 were treated with rmhTRAIL for 24 hours. Then cell viability were measured by MTT assay, meanwhile cell cycle and apoptosis were measured by flow cytometry. Furthermore, mass spectrometry (LC-MS/MS) was used to identify the difference in the protein expression profiles. Finally, real-time PCR was performed to detect the mRNA expression of TRAIL receptors and apoptotic related proteins. Results: These results confirmed that NCI-H358 cells were sensitive to TRAIL, whereas A549 cells were resistant. Both mRNA and protein levels of voltage-dependent anion-selective channel protein1 (VDAC1), caspase9 (CASP9), and cytochrome cl (CYC1) were upregulated in 11358 cells but downregulated in A549 cells, whereas antiapoptotic protein BAG-2 was downregulated. In addition, TRAIL also causes DR5 low expression in A549 cells. Conclusions: These results indicate that rmhTRAIL had different anti-tumor activity in different NSCLC cell lines. Downregulation of VDAC1, CYCl, CASP9, and upregulation of BAG-2 might be associated with underlying TRAIL-resistance mechanisms. These findings motivated further studies to explore new therapeutic strategy overcoming TRAIL-resistance of NSCLC cells through modulating dysregulation of the proteins above.

As the gap grows tremendously between the numbers of protein sequence and structure, in-silico protein structure prediction plays more and more critical roles in life science. Biennial experiments of Critical Assessment of protein Structure Prediction (CASP), the most authoritative in the field of protein structure prediction, shows that most prediction methods of today are successful in certain aspects, such as comparative modeling. However, incomplete models unexpectedly appear and require further refinement works. Therefore, the present study designed an automated multi-template combination algorithm to perform such refinement works. A total of 59 proteins released during CASP9 prediction season (human group) were selected as experimental targets. Four prediction methods HHpred, Pcons, Modeller, and SAM were used to generate protein models, among which 318 models were incomplete. Automated multi-template combination algorithm was used in this study to work on each incomplete model, find the missing structures from other models, combine them with the original model, and finally obtain a recombined new model. Our results indicated that the quality of 95.56% of these 318 models was improved after the combination, and the improvement was statistically significant. Therefore, this study provided an effective method to improve the protein model quality.