Microbial protein production continues to evolve as a foundational technology in biomanufacturing, enabling the large-scale synthesis of pharmaceuticals, industrial enzymes, diagnostic antibodies and sustainable materials such as biofuels and bioplastics. Central to this capability are host organisms like Escherichia coli (E. coli), which remain popular due to their rapid growth rates, well-characterized genetics, and cost-effective cultivation in controlled fermenters. In addition, eukaryotic microbes such as the fungus Saccharomyces cerevisiae are engineered to accommodate complex proteins and post-translational modifications that bacterial systems can not natively perform.
At the cellular and genetic level, advances in synthetic and systems biology are being deployed to overcome traditional limitations in microbial hosts. For E. coli, recent work focuses on enhancing transcriptional and translational efficiency through optimized promoter architectures and ribosome binding sites, reducing metabolic burden by balancing expression rates, and engineering secretion pathways to increase soluble protein yields. Modern platforms also integrate codon optimization and chaperone co-expression to improve folding and reduce aggregation, as well as import of heterologous glycosylation systems to support post-translational modifications previously exclusive to eukaryotic hosts. To fine-tune cellular physiology for high production capacity, these strategies are often combined with omics-guided analysis such as transcriptomics, proteomics and metabolomics.
From a bioprocess engineering perspective, innovations in fermentation design and scale-up are crucial to translating optimized strains into industrial output. High-cell-density fermentation, fed-batch culture strategies, and dynamic control systems allow cultures to sustain higher biomass and prolong productive phases, significantly increasing volumetric productivity. Moreover, the adoption of continuous manufacturing approaches promises to enhance yield consistency and reduce costs by maintaining cells in productive steady states while continuously removing products from the bioreactor.
The integration of computational tools such as machine learning and artificial intelligence (AI) to predict translation-enhancing peptide sequences and inform strain designs is emerging as a powerful approach to further accelerate development cycles and unlock new levels of efficiency. These technical advancements improve the economics of microbial protein synthesis and extend its ecological sustainability by enabling efficient use of resources, reduction of energy consumption and supporting recycling of waste products.
A recent study describes a new approach to enhancing translation efficiency in E. coli. The work combines peptide engineering with AI to address a common limitation in protein expression, known as ribosome stalling.
Ribosome stalling occurs when the translation machinery halts prematurely and the synthesis of proteins of interest (POIs) can be affected by various factors, including promoter strength, the nucleotide sequence of messenger ribonucleic acid (mRNA) and transfer ribonucleic acid (tRNA) availability. These factors can limit protein production yields and compromise the functionality of synthetic circuits. This phenomenon can severely limit protein yields in E. coli, undermining its utility in producing pharmaceuticals, industrial enzymes, and bio-based materials. At the molecular level, ribosome stalling is frequently triggered by rare codons, secondary structures of mRNA, repetitive amino acid motifs, or amino acid starvation. This leads to a disruption of balance between translation elongation and cellular resource availability. Prolonged stalling can activate ribosome rescue pathways such as trans-translation mediated by transfer messenger ribonucleic acid (tmRNA) and associated factors, leading to premature termination and degradation of incomplete polypeptides
Beyond reducing yield, ribosome stalling imposes a significant metabolic burden on the host cell by sequestering ribosomes and depleting translational capacity, thereby interfering with native gene expression and overall cellular fitness. In engineered systems, this can result in unintended feedback effects, including altered growth rates, stress responses, and instability of synthetic gene circuits. As a result, translational bottlenecks caused by ribosome stalling represent a key challenge in the design of high-performance microbial cell factories.
Recent strategies to mitigate ribosome stalling include codon optimization informed by host-specific tRNA abundance, dynamic regulation of gene expression to match translational capacity, and engineering of tRNA pools to alleviate bottlenecks associated with rare codons. Additionally, advances in ribosome profiling and single-cell transcriptomics now enable high-resolution identification of stall sites across the transcriptome, providing actionable insights for rational redesign of coding sequences. These approaches highlight the importance of fine-tuning translational dynamics to increase protein yield, maintain circuit stability, and fully exploit E. coli as a scalable and reliable platform for industrial protein production.
To address this, the researchers focused on short peptide sequences, or short translational-enhancing peptides (TEPs), that could mitigate stalling. Their previous work showed that appending a tetrapeptide consisting of serine, lysine, isoleucine, and lysine (SKIK), to the N-terminus of a protein significantly improved translation efficiency.
To further investigate the mechanistic basis of ribosome stalling and identify sequence-level solutions, the research team constructed a comprehensive tetrapeptide library encompassing all 160,000 possible combinations of the 20 standard amino acids. This thourough design enabled systematic evaluation of short peptide motifs capable of modulating translational dynamics. Leveraging experimental data from approximately 250 high-throughput assays, the researchers trained an AI based prediction model to assess the translation-enhancing potential of individual tetrapeptides and their ability to alleviate ribosome stalling. Through three iterative rounds of model refinement, the framework achieved high predictive accuracy, enabling reliable extrapolation of translation efficiency across the entire combinatorial space.
Notably, the model identified specific tetrapeptide sequences that consistently enhanced translation elongation, suggesting that short nascent peptide motifs can exert a measurable influence on ribosomal progression. By embedding these translation-enhancing motifs into POIs this strategy offers a method to improve protein yield without extensive host genome modification.
By integrating rational peptide design with AI-driven predictive modeling, the team’s approach provides a promising strategy to optimize protein expression in E. coli. Rather than relying solely on extensive host strain engineering or trial-and-error sequence optimization, this method leverages short, translation-enhancing peptide motifs that can be systematically embedded into POIs. Such modular design principles are particularly well suited to industrial pipelines, where rapid prototyping, reproducibility and cross-product compatibility are essential. Furthermore, AI-guided prediction enables efficient exploration of vast sequence spaces that would be impractical to investigate experimentally, accelerating development timelines while reducing experimental cost.
Collectively, this work lays a strong foundation for more reliable, robust, and cost-effective microbial manufacturing platforms. By mitigating ribosome stalling and improving translational throughput, the strategy enhances the scalability of microbial protein production and supports broader adoption of sustainable bioprocesses. As these approaches mature, they are likely to play an increasingly important role in the transition toward circular bioeconomies, where engineered microbes serve as versatile factories for the production of high-value proteins and bio-based materials.
References:
https://pubs.rsc.org/en/content/articlehtml/2025/cb/d5cb00199d
https://phys.org/news/2025-10-ai-peptide-sequences-ribosome-stalling.html
J Biol Chem. 2023 May;299(5):104676. doi: 10.1016/j.jbc.2023.104676
ACS Synth Biol. 2024 Dec 20;13(12):3908-3916. doi: 10.1021/acssynbio.4c00221 Add to Citavi project by DOI. Epub 2024 Nov 21.
