This document relates to materials and methods for the production of protein. For example, proteins having a low flavor or low color profile and food products comprising the same.

Disclosed is a method for evaluating in vivo protein nutrition based on an LC-MS-MS technique, including the following steps: (1) collecting contents from different intestinal segments, and extracting and isolating protein ingredients; (2) determining the concentration of proteins; (3) treating before carrying out mass spectrometry: including digestion and desalting of a whole protein solution; (4) LC-MS-MS analysis; (5) database searching; and (6) data processing. Proteomic technology is used to identify proteins in the contents of different intestinal segments and digestive products thereof, and the source of the proteins in the contents of different intestinal segments and the contents thereof can be determined therefrom. Through bioinformatic analysis, the function of differential proteins in the body can be further understood, where the gene expression of enzymes related to protein digestion and metabolism may be different, thereby providing a scientific basis for further scientific evaluation of protein digestion and utilization.

Disclosed herein is a rapid genetics-free method for eliciting and detecting cryptic metabolites using an imaging mass spectrometry-based approach. An organism of choice is challenged with elicitors from a small molecule library. The molecules elicited are then imaged by mass spec, which allows for rapid identification of cryptic metabolites. These are then isolated and characterized. Employing the disclosed approach activated production of cryptic glycopeptides from an actinomycete bacterium. The molecules that result, the keratinimicins and keratinicyclins, are metabolites with important structural features. At least two of these, keratinimicins B and C, are highly bioactive against several pathogenic strains. This approach will allow for rapid activation and identification of cryptic metabolites from diverse microorganisms in the future.

A biomarker diagnostic system includes a sensor to collect a sweat sample from a biological subject; a processor operatively connected to the sensor, wherein the processor is configured to perform metabolic and proteomic profiling of biomarkers in the sweat sample. The metabolic and proteomic profile is compared to a predetermined profile of the biomarkers and to determine a physiological status of the biomarkers. The system further includes a feedback unit operatively coupled to the sensor and the processor and configured to output physiological performance data based on the physiological status.

The instant disclosure provides methods of detecting N-glycopeptides in a sample by contacting the sample with one or more exoglycosidases and detecting the N-glycopeptides by mass spectrometry. Also provided are methods of detecting the presence or progression of a liver disease and treating said liver disease.

The present systems and methods introduce deep learning to de novo peptide sequencing from tandem mass spectrometry data, and in particular mass spectrometry data obtained by data-independent acquisition. The systems and methods achieve improvements in sequencing accuracy over existing systems and methods and enables complete assembly of novel protein sequences without assisting databases. To sequence peptides from mass spectrometry data obtained by data-independent acquisition, precursor profiles representing intensities of one or more precursor ion signals associated with a precursor retention time and fragment ion spectra representing signals from fragment ions and fragment retention times are fed into a neural network.

A method for mass spectral analysis of molecules based on full mass spectral profile or raw scan mode data, comprising the steps of specifying the basic building blocks for the molecule; estimating initial values including trial numbers of building blocks, charge states, and possible modifications; calculating discrete isotope distributions based on elemental compositions; calculating a profile mode theoretical mass spectrum using a target mass spectrum peak shape function; performing regression analysis between acquired profile mode mass spectrum data and calculated theoretical mass spectrum data and reporting regression statistics; using regression statistics as feedbacks to update initially estimated values including trial numbers of building blocks, charge states, and possible modifications; and repeating selected step to optimize the regression statistics. A mass spectrometer operating in accordance with the method. A medium having computer readable program instructions for causing a mass spectrometer associated with a computer to operate in accordance with the method.

A method for characterizing or quantifying one or more proteins in visible and/or sub-visible particles formed in a sample by detecting the at least one visible or sub-visible particle in the sample, isolating and capturing the at least one visible or sub-visible particle to identify a presence of a protein, and using a mass spectrometer to characterize the protein.

The present invention generally pertains to methods of characterizing charge variants of a protein of interest. In particular, the present invention pertains to the use of desalting size exclusion chromatography-reduced peptide mapping mass spectrometry to identify charge variants separated by capillary isoelectric focusing.

An extracellular vesicle characterization and analysis device in terms of their size, phenotype, and cargo content is provided. A method performed with the device to quantify the heterogeneity of extracellular vesicle samples both in terms of size and cargo content and further quantify the purity of extracellular vesicles based on their phenotype and cargo content is further provided. The extracellular vesicle characterization and analysis device includes an atomic force microscope and confocal Raman spectrometer subsystems that will present the phenotypic characterization and cargo analysis of extracellular vesicles, respectively. By processing the topographic images obtained by atomic force microscopy with image processing methods and analyzing them, the dimensional heterogeneity of the extracellular vesicle samples can be quantified and information about their purity can be presented. The confocal Raman spectrometer applies the tip-enhanced Raman spectrum method, performs a heterogeneity quantification and provides data on the purity of the sample.