ArticleScientific reports2022
Machine learning modeling for solubility prediction of recombinant antibody fragment in four different E. coli strains.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 13 citations in OpenAlex.
- Improved Extracellular Level of L-Asparaginase in Shaken Cultures of Escherichia coli ATCC 11,303 Using a Statistical Design Approach.Applied biochemistry and biotechnology · 2026Article
- The response surface method enables efficient optimization of induction parameters for the production of bioactive peptides in fed-batch bioreactors using Escherichia coli.Folia microbiologica · 2026Article
- The landscape of antibody production systems: recombinant expression for research, diagnostics and therapy.Frontiers in bioengineering and biotechnology · 2026Review
- Prediction and optimization of electrochemical oxidation efficiency using machine learning: Insights from carbon-based anodes for water treatment.Journal of environmental management · 2025Article
- Estimation and validation of solubility of recombinant protein in E. coli strains via various advanced machine learning models.Scientific reports · 2025Article
- Review
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Authors and funding
3 authors at 2 institutions in 1 country.
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Abstract
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.
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