Characterization of tissue transglutaminase in human osteoblast-like cells

Tissue transglutaminase (tTG) is a calcium-dependent and guanosine 5'-triphosphate (GTP) binding enzyme, which catalyzes the post-translational modification of proteins by forming intermolecular ε(ϒ-glutamyl)lysine cross-links. In this study, human osteoblasts (HOBs) isolated from femoral head trabecular bone and two osteosarcoma cell lines (HOS and MG-63) were studied for their expression and localization of tTG. Quantitative evaluation of transglutaminase (TG) activity determined using the [1,414C]-putrescine incorporation assay showed that the enzyme was active in all cell types. However, there was a significantly higher activity in the cell homogenates of MG-63 cells as compared with HOB and HOS cells (p <0.001). There was no significant difference between the activity of the enzyme in HOB and HOS cells. All three cell types also have a small amount of active TG on their surface as determined by the incorporation of biotinylated cadaverine into fibronectin. Cell surface-related tTG was further shown by preincubation of cells with tTG antibody, which led to inhibition of cell attachment. Western blot analysis clearly indicated that the active TG was tTG and immunocytochemistry showed it be situated in the cytosol of the cells. In situ extracellular enzyme activity also was shown by the cell-mediated incorporation of fluorescein cadaverine into extracellular matrix (ECM) proteins. These results clearly showed that MG-63 cells have high extracellular activity, which colocalized with the ECM protein fibronectin and could be inhibited by the competitive primary amine substrate putrescine. The contribution of tTG to cell surface/matrix interactions and to the stabilization of the ECM of osteoblast cells therefore could by an important factor in the cascade of events leading to bone differentiation and mineralization.

EnDNA-Prot:identification of DNA-binding proteins by applying ensemble learning

DNA-binding proteins are crucial for various cellular processes, such as recognition of specific nucleotide, regulation of transcription, and regulation of gene expression. Developing an effective model for identifying DNA-binding proteins is an urgent research problem. Up to now, many methods have been proposed, but most of them focus on only one classifier and cannot make full use of the large number of negative samples to improve predicting performance. This study proposed a predictor called enDNA-Prot for DNA-binding protein identification by employing the ensemble learning technique. Experiential results showed that enDNA-Prot was comparable with DNA-Prot and outperformed DNAbinder and iDNA-Prot with performance improvement in the range of 3.97-9.52% in ACC and 0.08-0.19 in MCC. Furthermore, when the benchmark dataset was expanded with negative samples, the performance of enDNA-Prot outperformed the three existing methods by 2.83-16.63% in terms of ACC and 0.02-0.16 in terms of MCC. It indicated that enDNA-Prot is an effective method for DNA-binding protein identification and expanding training dataset with negative samples can improve its performance. For the convenience of the vast majority of experimental scientists, we developed a user-friendly web-server for enDNA-Prot which is freely accessible to the public. © 2014 Ruifeng Xu et al.

Identifying DNA-binding proteins by combining support vector machine and PSSM distance transformation

Background: DNA-binding proteins play a pivotal role in various intra- and extra-cellular activities ranging from DNA replication to gene expression control. Identification of DNA-binding proteins is one of the major challenges in the field of genome annotation. There have been several computational methods proposed in the literature to deal with the DNA-binding protein identification. However, most of them can't provide an invaluable knowledge base for our understanding of DNA-protein interactions. Results: We firstly presented a new protein sequence encoding method called PSSM Distance Transformation, and then constructed a DNA-binding protein identification method (SVM-PSSM-DT) by combining PSSM Distance Transformation with support vector machine (SVM). First, the PSSM profiles are generated by using the PSI-BLAST program to search the non-redundant (NR) database. Next, the PSSM profiles are transformed into uniform numeric representations appropriately by distance transformation scheme. Lastly, the resulting uniform numeric representations are inputted into a SVM classifier for prediction. Thus whether a sequence can bind to DNA or not can be determined. In benchmark test on 525 DNA-binding and 550 non DNA-binding proteins using jackknife validation, the present model achieved an ACC of 79.96%, MCC of 0.622 and AUC of 86.50%. This performance is considerably better than most of the existing state-of-the-art predictive methods. When tested on a recently constructed independent dataset PDB186, SVM-PSSM-DT also achieved the best performance with ACC of 80.00%, MCC of 0.647 and AUC of 87.40%, and outperformed some existing state-of-the-art methods. Conclusions: The experiment results demonstrate that PSSM Distance Transformation is an available protein sequence encoding method and SVM-PSSM-DT is a useful tool for identifying the DNA-binding proteins. A user-friendly web-server of SVM-PSSM-DT was constructed, which is freely accessible to the public at the web-site on http://bioinformatics.hitsz.edu.cn/PSSM-DT/.