Influence of hydrophilic amino acids and GC-content on expression of recombinant proteins used in vaccines against foot-and-mouth disease virus in Escherichia coli

PERISCOPE-Opt: Machine learning-based prediction of optimal fermentation conditions and yields of recombinant periplasmic protein expressed in Escherichia coli

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The ensemble model considered both fermentation conditions and protein properties.

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Optimal fermentation conditions and periplasmic recombinant protein yield can be predicted.

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Predictor’s accuracy and Pearson correlation coefficient are 75% and 0.91, respectively.

Optimization of the fermentation process for recombinant protein production (RPP) is often resource-intensive. Machine learning (ML) approaches are helpful in minimizing the experimentations and find vast applications in RPP. However, these ML-based tools primarily focus on features with respect to amino-acid-sequence, ruling out the influence of fermentation process conditions. The present study combines the features derived from fermentation process conditions with that from amino acid-sequence to construct an ML-based model that predicts the maximal protein yields and the corresponding fermentation conditions for the expression of target recombinant protein in the Escherichia coli periplasm. Two sets of XGBoost classifiers were employed in the first stage to classify the expression levels of the target protein as high (>50 mg/L), medium (between 0.5 and 50 mg/L), or low (<0.5 mg/L). The second-stage framework consisted of three regression models involving support vector machines and random forest to predict the expression yields corresponding to each expression-level-class. Independent tests showed that the predictor achieved an overall average accuracy of 75% and a Pearson coefficient correlation of 0.91 for the correctly classified instances. Therefore, our model offers a reliable substitution of numerous trial-and-error experiments to identify the optimal fermentation conditions and yield for RPP. It is also implemented as an open-access webserver, PERISCOPE-Opt (http://periscope-opt.erc.monash.edu).

Salt-Dependent Aggregation and Assembly of E coli-Expressed Ferritin

Ferritin, with the primary function of iron storage, is a nearly ubiquitous protein found in most living organisms. Our recent investigations suggest that ferritin can assemble nanoparticles. So we use ferritin as a novel type of delivery vehicle for recombinant epitope vaccines. And, we found that ferritin form nonnative aggregates depended sensitively on NaCl concentrations. Here, we report that ferritin is an ion-sensitive protein and has the attribute of salt-dependent aggregation. Our results indicate that recombinant ferritin can be released as a soluble form from Escherichia coli at low NaCl concentrations (≤50 mmol/L). Moreover, this result affords us to confirm a proper self-assembling solution for soluble ferritin or other ferritin-based fusion proteins to assemble nanoparticles.