In silico high throughput mutagenesis and screening of signal peptides to mitigate N-terminal heterogeneity of recombinant monoclonal antibodies

Enhanced production of extracellular triacylglycerol lipase for bioplastic degradation by replacing signal peptide

With the increase in plastic production, efficient and timely plastic degradation are urgently needed. In that point, biodegradable plastics have attracted attention as potential solutions for environmental pollution of plastics. However, finding of superior degrading strains and enzymes such as esterase, cutinase, and triacylglycerol lipase (TGL) of bioplastic are still needed together with the efficient secretion systems of degrading enzymes. As a result, we investigated methods to enhance protein expression and secretion of novel bioplastic degrading enzyme by using signal peptides. The genes encoding TGL from Bacillus sp. JY35 and various secretory (Sec) pathway signal peptides were cloned together by replacing the original signal sequence, and they were expressed under T7 promoters in Escherichia coli BL21 (DE3). Esterase activity with p-nitrophenol esters, a plate assay, and SDS-PAGE were performed to screen and evaluate signal peptide efficiency. As a result, the PhoA-TGL combination was the most effective against bioplastic degradation, achieving a Polycaprolactone (PCL) degradation efficiency of 77 %, which was approximately 3.3 times higher than that of TGL with the original signal peptide. Furthermore, Polybutylene succinate (PBS) degradation under similar conditions was 1.5 times higher. Overall, this study showed signal peptide engineering could enhance the extracellular secretion and degradation system of triacylglycerol lipase (TGL) and highlights the potential of PhoA signal peptides and E. coli host to enhance production and secretion of plastic-degrading enzyme and degrading system.

Unlocking the power of antimicrobial peptides: advances in production, optimization, and therapeutics

Antimicrobial peptides (AMPs) are critical effectors of innate immunity, presenting a compelling alternative to conventional antibiotics amidst escalating antimicrobial resistance. Their broad-spectrum efficacy and inherent low resistance development are countered by production challenges, including limited yields and proteolytic degradation, which restrict their clinical translation. While chemical synthesis offers precise structural control, it is often prohibitively expensive and complex for large-scale production. Heterologous expression systems provide a scalable, cost-effective platform, but necessitate optimization. This review comprehensively examines established and emerging AMP production strategies, encompassing fusion protein technologies, molecular engineering approaches, rational peptide design, and post-translational modifications, with an emphasis on maximizing yield, bioactivity, stability, and safety. Furthermore, we underscore the transformative role of artificial intelligence, particularly machine learning algorithms, in accelerating AMP discovery and optimization, thereby propelling their expanded therapeutic application and contributing to the global fight against drug-resistant infections.

Advancements in Escherichia coli secretion systems for enhanced recombinant protein production

Escherichia coli is inarguably one of the most studied microorganisms across the spectrum of microbiology. It is very widely used in recombinant protein production owing to its rapid growth, ease of genetic manipulation, and relatively high protein yields. Despite all of its advantages, its inability to efficiently secrete proteins naturally remains a drawback leading to protein aggregation as inclusion bodies in the cytoplasm and consequent low overall protein yield. Therefore, many approaches to mitigate this weakness and enhance extracellular secretion to increase protein yield have been devised. This review explores the natural and engineered secretion systems in E. coli, highlighting their potential for enhanced protein secretion for non-glycosylated proteins. Natural one-step (e.g., Type I and III Secretion Systems) and two-step systems (e.g., Sec and Tat pathways) are detailed alongside recent advancements in genetic engineering, mutagenesis, and synthetic biology approaches aimed at improving protein yield, folding, and secretion efficiency. Emerging technologies, such as the ESETEC® and BacSec® platforms, promise scalable and cost-effective solutions for higher protein production. Challenges, including limited cellular capabilities and protein aggregation, are addressed through innovative strategies like cell wall modification, co-expression of chaperones, and medium optimization. This review emphasizes E. coli's adaptability to industrial applications, and the promising future of recombinant protein technologies.

Increased glycoprotein hormone yield in stably transfected CHO cells using human serum albumin signal peptide for beta-chains

Heterologous signal peptides enable increasing titers of recombinant proteins in mammalian cell culture. Four human heterodimeric glycoprotein hormones (follicle-stimulating hormone, FSH; luteinizing hormone, LH; chorionic gonadotropin, CG; and thyroid-stimulating hormone, TSH) were expressed in stably transfected CHO cells when varying signal peptides of their β-subunits. The signal peptide of human serum albumin proved to be the most effective for the glycoprotein hormone family. The cell specific productivity was increased for LH (2.5 pg/cell, 4-fold increase), TSH (1.6 pg/cell, 13-fold increase), and CG (1.0 pg/cell, 60%-increase). According to the Western blotting and quantitative PCR data, the productivity increase is associated with an increase in the efficiency of translation and translocation of β-subunits of hormones in the endoplasmic reticulum due to their coupling with the heterologous signal peptides.

Manipulating gene expression levels in mammalian cell factories: An outline of synthetic molecular toolboxes to achieve multiplexed control

Mammalian cells have developed dedicated molecular mechanisms to tightly control expression levels of their genes where the specific transcriptomic signature across all genes eventually determines the cell's phenotype. Modulating cellular phenotypes is of major interest to study their role in disease or to reprogram cells for the manufacturing of recombinant products, such as biopharmaceuticals. Cells of mammalian origin, for example Chinese hamster ovary (CHO) and Human embryonic kidney 293 (HEK293) cells, are most commonly employed to produce therapeutic proteins. Early genetic engineering approaches to alter their phenotype have often been attempted by "uncontrolled" overexpression or knock-down/-out of specific genetic factors. Many studies in the past years, however, highlight that rationally regulating and fine-tuning the strength of overexpression or knock-down to an optimum level, can adjust phenotypic traits with much more precision than such "uncontrolled" approaches. To this end, synthetic biology tools have been generated that enable (fine-)tunable and/or inducible control of gene expression. In this review, we discuss various molecular tools used in mammalian cell lines and group them by their mode of action: transcriptional, post-transcriptional, translational and post-translational regulation. We discuss the advantages and disadvantages of using these tools for each cell regulatory layer and with respect to cell line engineering approaches. This review highlights the plethora of synthetic toolboxes that could be employed, alone or in combination, to optimize cellular systems and eventually gain enhanced control over the cellular phenotype to equip mammalian cell factories with the tools required for efficient production of emerging, more difficult-to-express biologics formats.

Leveraging Artificial Intelligence to Expedite Antibody Design and Enhance Antibody-Antigen Interactions

This perspective sheds light on the transformative impact of recent computational advancements in the field of protein therapeutics, with a particular focus on the design and development of antibodies. Cutting-edge computational methods have revolutionized our understanding of protein-protein interactions (PPIs), enhancing the efficacy of protein therapeutics in preclinical and clinical settings. Central to these advancements is the application of machine learning and deep learning, which offers unprecedented insights into the intricate mechanisms of PPIs and facilitates precise control over protein functions. Despite these advancements, the complex structural nuances of antibodies pose ongoing challenges in their design and optimization. Our review provides a comprehensive exploration of the latest deep learning approaches, including language models and diffusion techniques, and their role in surmounting these challenges. We also present a critical analysis of these methods, offering insights to drive further progress in this rapidly evolving field. The paper includes practical recommendations for the application of these computational techniques, supplemented with independent benchmark studies. These studies focus on key performance metrics such as accuracy and the ease of program execution, providing a valuable resource for researchers engaged in antibody design and development. Through this detailed perspective, we aim to contribute to the advancement of antibody design, equipping researchers with the tools and knowledge to navigate the complexities of this field.

Enhancing Human Glycoprotein Hormones Production in CHO Cells Using Heterologous Beta-Chain Signal Peptides

We studied the influence of heterologous signal peptides in the β-chains of glycoprotein hormones on the biosynthesis of these hormones in a transiently transfected culture of Chinese hamster ovary cells CHO S. When the natural signal peptides of the β-chains were replaced with the heterologous signal peptide of human serum albumin, cell productivity was increased 2-2.5 times for human luteinizing hormone, human chorionic gonadotropin, and human thyroid-stimulating hormone, but not for human follicle-stimulating hormone. No significant increase in cell productivity was observed for human azurocidin signal peptide and human glycoprotein hormone α-chain signal peptide. The used approach allows quick assessing the effect of heterologous signal peptides on the biosynthesis of heterodimeric proteins of various classes.

Streamlined in vitro screening system of synthetic signal peptides in Chinese hamster ovary cells for therapeutic protein production

Increasing the screening efficiency and maintaining the N-terminal cleavage pattern are key factors in the development of an in vitro synthetic signal peptide screening system for high therapeutic protein production in Chinese hamster ovary (CHO) cells. This study improved the in vitro screening system of synthetic signal peptides in CHO cells for therapeutic protein production by modifying the expression vector. Incorporating a leaky stop codon with IgG transmembrane and cytoplasmic domains into the expression vector improved the proportion of high producers in establishing stable CHO cell pools. The selected signal peptides from stable CHO cell pools that were generated using degenerate codon-based oligonucleotides with a conserved polar carboxy-terminal domain in the native signal peptide showed similar N-terminal cleavage patterns to the native one. In addition, replacing native signal peptide with selected synthetic signal peptides did not influence the sialylated N-linked glycan formation and biological activity of therapeutic Fc-fusion glycoprotein in CHO cells. Thus, an in vitro synthetic signal peptide screening system can be used for therapeutic Fc-fusion glycoprotein production in CHO cells with an enhanced specific protein productivity while maintaining the N-terminal cleavage pattern similar to the native one.

Peptide-drug conjugates (PDCs): a novel trend of research and development on targeted therapy, hype or hope?

Peptide-drug conjugates (PDCs) are the next generation of targeted therapeutics drug after antibody-drug conjugates (ADCs), with the core benefits of enhanced cellular permeability and improved drug selectivity. Two drugs are now approved for market by US Food and Drug Administration (FDA), and in the last two years, the pharmaceutical companies have been developing PDCs as targeted therapeutic candidates for cancer, coronavirus disease 2019 (COVID-19), metabolic diseases, and so on. The therapeutic benefits of PDCs are significant, but poor stability, low bioactivity, long research and development time, and slow clinical development process as therapeutic agents of PDC, how can we design PDCs more effectively and what is the future direction of PDCs? This review summarises the components and functions of PDCs for therapeutic, from drug target screening and PDC design improvement strategies to clinical applications to improve the permeability, targeting, and stability of the various components of PDCs. This holds great promise for the future of PDCs, such as bicyclic peptide‒toxin coupling or supramolecular nanostructures for peptide-conjugated drugs. The mode of drug delivery is determined according to the PDC design and current clinical trials are summarised. The way is shown for future PDC development.