Python workflow for the selection and identification of marker peptides-proof-of-principle study with heated milk.

The analysis of almost holistic food profiles has developed considerably over the last years. This has also led to larger amounts of data and the ability to obtain more information about health-beneficial and adverse constituents in food than ever before. Especially in the field of proteomics, software is used for evaluation, and these do not provide specific approaches for unique monitoring questions. An additional and more comprehensive way of evaluation can be done with the programming language Python. It offers broad possibilities by a large ecosystem for mass spectrometric data analysis, but needs to be tailored for specific sets of features, the research questions behind. It also offers the applicability of various machine-learning approaches. The aim of the present study was to develop an algorithm for selecting and identifying potential marker peptides from mass spectrometric data. The workflow is divided into three steps: (I) feature engineering, (II) chemometric data analysis, and (III) feature identification. The first step is the transformation of the mass spectrometric data into a structure, which enables the application of existing data analysis packages in Python. The second step is the data analysis for selecting single features. These features are further processed in the third step, which is the feature identification. The data used exemplarily in this proof-of-principle approach was from a study on the influence of a heat treatment on the milk proteome/peptidome.

Repetitive Synthesis of High-Molecular-Weight Hyaluronic Acid with Immobilized Enzyme Cascades.

Industrial hyaluronic acid (HA) production comprises either fermentation with Streptococcus strains or extraction from rooster combs. The hard-to-control product quality is an obstacle to these processes. Enzymatic syntheses of HA were developed to produce high-molecular-weight HA with low dispersity. To facilitate enzyme recovery and biocatalyst re-use, here the immobilization of cascade enzymes onto magnetic beads was used for the synthesis of uridine-5'-diphosphate-α-d-N-acetyl-glucosamine (UDP-GlcNAc), UDP-glucuronic acid (UDP-GlcA), and HA. The combination of six enzymes in the UDP-sugar cascades with integrated adenosine-5'-triphosphate-regeneration reached yields between 60 and 100 % for 5 repetitive batches, proving the productivity. Immobilized HA synthase from Pasteurella multocida produced HA in repetitive batches for three days. Combining all seven immobilized enzymes in a one-pot synthesis, HA production was demonstrated for three days with a HA concentration of up to 0.37 g L-1 , an average MW of 2.7-3.6 MDa, and a dispersity of 1.02-1.03.