RESUMO
Poly (ß-amino ester) (PBAE) is an exceptional non-viral vector that is widely used in gene delivery, owing to its exceptional biocompatibility, easy synthesis, and cost-effectiveness. However, it carries a high surface positive charge that may cause cytotoxicity. Therefore, hydrophilic d-α-tocopherol polyethylene glycol succinate (TPGS) was copolymerised with PBAE to increase the biocompatibility and to decrease the potential cytotoxicity of the cationic polymer-DNA plasmid polyplex nanoparticles (NPs) formed through electrostatic forces between the polymer and DNA. TPGS-b-PBAE (TBP) copolymers with varying feeding molar ratios were synthesised to obtain products of different molecular weights. Their gene transfection efficiency was subsequently evaluated in HEK 293T cells using green fluorescent protein plasmid (GFP) as the model because free GFP is unable to easily pass through the cell membrane and then express as a protein. The particle size, ζ-potential, and morphology of the TBP2-GFP polyplex NPs were characterised, and plasmid incorporation was confirmed through gel retardation assays. The TBP2-GFP polyplex NPs effectively transfected multiple cells with low cytotoxicity, including HEK 293T, HeLa, Me180, SiHa, SCC-7 and C666-1 cells. We constructed a MUC2 (Mucin2)-targeting CRISPR/cas9 gene editing system in HEK 293T cells, with gene disruption supported by oligodeoxynucleotide (ODN) insertion in vitro. Additionally, we developed an LMP1 (latent membrane protein 1)-targeting CRISPR/cas9 gene editing system in LMP1-overexpressing SCC7 cells, which was designed to cleave fragments expressing the LMP1 protein (related to Epstein-Barr virus infection) and thus to inhibit the growth of the cells in vivo. As evidenced by in vitro and in vivo experiments, this system has great potential for gene therapy applications.
RESUMO
BACKGROUND: Prediction of protein-ligand binding sites is an important issue for protein function annotation and structure-based drug design. Nowadays, although many computational methods for ligand-binding prediction have been developed, there is still a demanding to improve the prediction accuracy and efficiency. In addition, most of these methods are purely geometry-based, if the prediction methods improvement could be succeeded by integrating physicochemical or sequence properties of protein-ligand binding, it may also be more helpful to address the biological question in such studies. RESULTS: In our study, in order to investigate the contribution of sequence conservation in binding sites prediction and to make up the insufficiencies in purely geometry based methods, a simple yet efficient protein-binding sites prediction algorithm is presented, based on the geometry-based cavity identification integrated with sequence conservation information. Our method was compared with the other three classical tools: PocketPicker, SURFNET, and PASS, and evaluated on an existing comprehensive dataset of 210 non-redundant protein-ligand complexes. The results demonstrate that our approach correctly predicted the binding sites in 59% and 75% of cases among the TOP1 candidates and TOP3 candidates in the ranking list, respectively, which performs better than those of SURFNET and PASS, and achieves generally a slight better performance with PocketPicker. CONCLUSIONS: Our work has successfully indicated the importance of the sequence conservation information in binding sites prediction as well as provided a more accurate way for binding sites identification.
