Machine Learning-Assisted Protein Redesign and Codon Optimization for Soluble Expression of Human Lactoferrin in Escherichia coli

Recombinant lactoferrin (LF) production in Escherichia coli (E. coli) is limited by poor solubility and inclusion body formation. To address this, a computational-experimental workflow was established, combining protein redesign and structural screening with a deep learning-based codon optimization strategy. Candidate mutants were screened using SurfSol and structurally assessed using AlphaFold 3. Subsequently, a bidirectional gated recurrent unit (BiGRU) model was benchmarked against five algorithms. In the test-subset comparison, the BiGRU model achieved the highest area under the receiver operating characteristic curve (ROC-AUC) and average precision (AP) values, recording a ROC-AUC of 0.777 and an AP of 0.720. Qualitative Western blot screening detected soluble-fraction anti-His-reactive bands for ELF2 among the tested constructs in E. coli. In lipopolysaccharide (LPS)-stimulated NCM460 cells, treatment with the ELF2-derived Ni-NTA-enriched preparation was associated with lower interleukin-6 (IL-6) and tumor necrosis factor-α (TNF-α) levels and higher interleukin-10 (IL-10) levels. Attribution of these cytokine changes to the redesigned LF requires protein identification and purity assessment. Nevertheless, the qualitative expression screen supports ELF2 as a promising candidate for further characterization.