Structural characterization of immunoglobulin G using time-dependent disulfide bond reduction

Protein-Specific Signal Peptides for Mammalian Vector Engineering

Expression of recombinant proteins in mammalian cell factories relies on synthetic assemblies of genetic parts to optimally control flux through the product biosynthetic pathway. In comparison to other genetic part-types, there is a relative paucity of characterized signal peptide components, particularly for mammalian cell contexts. In this study, we describe a toolkit of signal peptide elements, created using bioinformatics-led and synthetic design approaches, that can be utilized to enhance production of biopharmaceutical proteins in Chinese hamster ovary cell factories. We demonstrate, for the first time in a mammalian cell context, that machine learning can be used to predict how discrete signal peptide elements will perform when utilized to drive endoplasmic reticulum (ER) translocation of specific single chain protein products. For more complex molecular formats, such as multichain monoclonal antibodies, we describe how a combination of in silico and targeted design rule-based in vitro testing can be employed to rapidly identify product-specific signal peptide solutions from minimal screening spaces. The utility of this technology is validated by deriving vector designs that increase product titers ≥1.8×, compared to standard industry systems, for a range of products, including a difficult-to-express monoclonal antibody. The availability of a vastly expanded toolbox of characterized signal peptide parts, combined with streamlined in silico/in vitro testing processes, will permit efficient expression vector re-design to maximize titers of both simple and complex protein products.

Detection of multiply charged protein ions using matrix-assisted laser desorption/ionization mass spectrometry and a force-dried droplet method with a 2-nitrophloroglucinol matrix

MALDI-MS of myoglobin using 2-NPG with HCl additive.

Label-free sensitive luminescence biosensor for immunoglobulin G based on Ag6Au6 ethisterone cluster-estrogen receptor α aggregation and graphene

A specific and label-free "on-off-on" luminescence biosensor based on a novel heterometallic cluster [Ag₆Au₆(ethisterone)₁₂]-estrogen receptor α (Ag₆Au₆Eth-ERα) aggregation utilizing graphene oxide (GO) as a quencher to lead a small background signal was firstly constructed to detect immunoglobulin G (IgG) with a simple process and high selectivity. The efficient photoluminescent (PL) Ag₆Au₆Eth-ERα aggregation is strongly quenched by GO. In the presence of IgG, the PL of this system will be restored, and perceivable by human eyes under UV lamp excitation (365 nm). The quenching mechanism of GO on Ag₆Au₆Eth-ERα and enhancement mechanism of IgG on Ag₆Au₆Eth-ERα-GO were investigated in detail. Under the optimum conditions, the biosensor for high sensitive IgG detection expressed a wider linear range of 0.0078-10 ng/mL and a lower detection limit of 0.65 pg/mL with good stability and repeatability, which provided a new approach for label-free IgG detection.