LeGenD: determining N-glycoprofiles using an explainable AI-leveraged model with lectin profiling.

Glycosylation affects many vital functions of organisms. Therefore, its surveillance is critical from basic science to biotechnology, including biopharmaceutical development and clinical diagnostics. However, conventional glycan structure analysis faces challenges with throughput and cost.

Preparing glycomics data for robust statistical analysis with GlyCompareCT.

GlyCompareCT is a portable command-line tool to facilitate downstream glycomic data analyses, by addressing data inherent sparsity and non-independence. Inputting glycan abundances, users can run GlyCompareCT with one line of code to obtain the abundances of a minimal substructure set, named glycomotif, thereby quantifying hidden biosynthetic relationships between measured glycans. Optional parameters tuning and annotation are supported for personal preference.

Correction: StanDep: Capturing transcriptomic variability improves context-specific metabolic models.

[This corrects the article DOI: 10.1371/journal.pcbi.

Systematic evaluation of parameters for genome-scale metabolic models of cultured mammalian cells.

Genome-scale metabolic models describe cellular metabolism with mechanistic detail. Given their high complexity, such models need to be parameterized correctly to yield accurate predictions and avoid overfitting. Effective parameterization has been well-studied for microbial models, but it remains unclear for higher eukaryotes, including mammalian cells.

A genome-scale metabolic network model and machine learning predict amino acid concentrations in Chinese Hamster Ovary cell cultures.

The control of nutrient availability is critical to large-scale manufacturing of biotherapeutics. However, the quantification of proteinogenic amino acids is time-consuming and thus is difficult to implement for real-time in situ bioprocess control. Genome-scale metabolic models describe the metabolic conversion from media nutrients to proliferation and recombinant protein production, and therefore are a promising platform for in silico monitoring and prediction of amino acid concentrations.

In situ detection of protein interactions for recombinant therapeutic enzymes.

Despite their therapeutic potential, many protein drugs remain inaccessible to patients since they are difficult to secrete. Each recombinant protein has unique physicochemical properties and requires different machinery for proper folding, assembly, and posttranslational modifications (PTMs). Here we aimed to identify the machinery supporting recombinant protein secretion by measuring the protein-protein interaction (PPI) networks of four different recombinant proteins (SERPINA1, SERPINC1, SERPING1, and SeAP) with various PTMs and structural motifs using the proximity-dependent biotin identification (BioID) method.

StanDep: Capturing transcriptomic variability improves context-specific metabolic models.

Diverse algorithms can integrate transcriptomics with genome-scale metabolic models (GEMs) to build context-specific metabolic models. These algorithms require identification of a list of high confidence (core) reactions from transcriptomics, but parameters related to identification of core reactions, such as thresholding of expression profiles, can significantly change model content. Importantly, current thresholding approaches are burdened with setting singular arbitrary thresholds for all genes; thus, resulting in removal of enzymes needed in small amounts and even many housekeeping genes.

The emerging impact of cell-free chemical biosynthesis.

Biomanufacturing has emerged as a promising alternative to chemocatalysis for green, renewable, complex synthesis of biofuels, medicines, and fine chemicals. Cell-free chemical biosynthesis offers additional advantages over in vivo production, enabling plug-and-play assembly of separately produced enzymes into an optimal cascade, versatile reaction conditions, and direct access to the reaction environment. In order for these advantages to be realized on the larger scale of industry, strategies are needed to reduce costs of biocatalyst generation, improve biocatalyst stability, and enable economically sustainable continuous cascade operation.

Rapid, portable detection of endocrine disrupting chemicals through ligand-nuclear hormone receptor interactions.

Endocrine disrupting chemicals (EDC) are structurally diverse compounds that can interact with nuclear hormone receptors, posing significant risk to human and ecological health. Unfortunately, many conventional biosensors have been too structure-specific, labor-intensive or laboratory-oriented to detect broad ranges of EDC effectively. Recently, several technological advances are providing more rapid, portable, and affordable detection of endocrine-disrupting activity through ligand-nuclear hormone receptor interactions.

Efficient tRNA degradation and quantification in Escherichia coli cell extract using RNase-coated magnetic beads: A key step toward codon emancipation.

Emancipating sense codons toward a minimized genetic code is of significant interest to science and engineering. A key approach toward sense codon emancipation is the targeted in vitro removal of native tRNA. However, challenges remain such as the insufficient depletion of tRNA in lysate-based in vitro systems and the high cost of the purified components system (PURE).

Protein synthesis directly from PCR: progress and applications of cell-free protein synthesis with linear DNA.

A rapid, versatile method of protein expression and screening can greatly facilitate the future development of therapeutic biologics, proteomic drug targets and biocatalysts. An attractive candidate is cell-free protein synthesis (CFPS), a cell-lysate-based in vitro expression system, which can utilize linear DNA as expression templates, bypassing time-consuming cloning steps of plasmid-based methods. Traditionally, such linear DNA expression templates (LET) have been vulnerable to degradation by nucleases present in the cell lysate, leading to lower yields.