Statistical optimization of culture conditions for expression of recombinant humanized anti-EpCAM single-chain antibody using response surface methodology

Optimization of a recombinant BlaR-CTD protein formulation using the response surface methodology

The sequence of a carboxy-terminal of the β-lactam sensor-transducer protein (BlaR-CTD) from Bacillus licheniformis ATCC14580 was extracted from US7745193B2 patent and expressed in E. coli using pColdI vector as a soluble His-tag recombinant protein. In this study, several excipients were used to improve the stability of recombinant BlaR-CTD and obtain the optimal formulation for this protein using response surface methodology (RSM)/ Central Composite Design (CCD). Total protein concentration was measured by UV spectroscopy and the Bradford test. A total of 7 various factors were designed using four different excipients including Glycerol, Sucrose, Triton x-100, and Tween-20, and three different buffers like Tris, Borate, and PBS. By obtaining suitable excipients and buffer i.e. glycerol and sucrose, pH ranging from 7 to 9 were evaluated. The pH 7.62, glycerol 15.35%, and sucrose 152.52 mM were determined as the most suitable for improving the thermal stability of recombinant BlaR-CTD.

Genome-scale metabolic model-based engineering of Escherichia coli enhances recombinant single-chain antibody fragment production

PURPOSE

Escherichia coli is an attractive and cost-effective cell factory for producing recombinant proteins such as single-chain variable fragments (scFvs). AntiEpEX-scFv is a small antibody fragment that has received considerable attention for its ability to target the epithelial cell adhesion molecule (EpCAM), a cancer-associated biomarker of solid tumors. Due to its metabolic burden, scFv recombinant expression causes a remarkable decrease in the maximum specific growth rate of the scFv-producing strain. In the present study, a genome-scale metabolic model (GEM)-guided engineering strategy is proposed to identify gene targets for improved antiEpEX-scFv production in E. coli.

METHODS

In this study, a genome-scale metabolic model of E. coli (iJO1366) and a metabolic modeling tool (FVSEOF) were employed to find appropriate genes to be amplified in order to improve the strain for incresed production of antiEpEX-scFv. To validate the model predictions, one target gene was overexpressed in the parent strain Escherichia coli BW25113 (DE3).

RESULTS

For improving scFv production, we applied the FVSEOF method to identify a number of potential genetic engineering targets. These targets were found to be localized in the glucose uptake system and pentose phosphate pathway. From the predicted targets, the glk gene encoding glucokinase was chosen to be overexpressed in the parent strain Escherichia coli BW25113 (DE3). By overexpressing glk, the growth capacity of the recombinant E. coli strain was recovered. Moreover, the engineered strain with glk overexpression successfully led to increased scFv production.

CONCLUSION

The genome-scale metabolic modeling can be considered for the improvement of the production of other recombinant proteins.

Machine learning modeling for solubility prediction of recombinant antibody fragment in four different E. coli strains

The solubility of proteins is usually a necessity for their functioning. Recently an emergence of machine learning approaches as trained alternatives to statistical models has been evidenced for empirical modeling and optimization. Here, soluble production of anti-EpCAM extracellular domain (EpEx) single chain variable fragment (scFv) antibody was modeled and optimized as a function of four literature based numerical factors (post-induction temperature, post-induction time, cell density of induction time, and inducer concentration) and one categorical variable using artificial neural network (ANN) and response surface methodology (RSM). Models were established by the CCD experimental data derived from 232 separate experiments. The concentration of soluble scFv reached 112.4 mg/L at the optimum condition and strain (induction at cell density 0.6 with 0.4 mM IPTG for 24 h at 23 °C in Origami). The predicted value obtained by ANN for the response (106.1 mg/L) was closer to the experimental result than that obtained by RSM (97.9 mg/L), which again confirmed a higher accuracy of ANN model. To the author's knowledge this is the first report on comparison of ANN and RSM in statistical optimization of fermentation conditions of E.coli for the soluble production of recombinant scFv.