Production of sialic acid rich glycopeptide from bovine κ-casein glycomacropeptide by hydrolyzing with papain
Bibliographic record
Abstract
Bovine κ-casein glycomacropeptide (GMP) is a sialic acid containing glycopeptide having many biological activities. The study described in this research communication was undertaken to determine whether sialic acid rich glycopeptide can be produced from GMP by proteinase treatment. A sample of GMP was hydrolyzed with papain, and the obtained hydrolysate was chromatographed on a column of diethylaminoethyl-Sephacel to obtain a glycopeptide fraction (GPF). This product accounted for average 48.1% dry weight of GMP or 81.1% total recovered sialic acid from GMP. The content of sialic acid (expressed as % dry weight) was 1.7 times higher in GPF (22.6) than in unhydrolyzed GMP (13.4). Major differences in amino acid composition between GPF and GMP were found in the contents (mol%) of: lysine (<1 and 4.5, respectively), serine (20.3 and 10.3, approximately twice higher in GPF), asparagine/aspartic acid and isoleucine. The contents of the last two amino acids were approximately twice lower in GPF. On gel filtration chromatography with Sephacryl S-100, GMP was eluted as a single peak with elution volume similar to that of dimeric β-lactoglobulin (36.6 kDa) whereas GPF was eluted in two peaks both with elution volumes greater than that of α-lactalbumin (14.2 kDa). These peak fractions containing high (fraction I) and low (fraction II) molecular size glycopeptides gave different sialic acid to peptide ratio, which was 1.7 times higher in fraction I than in fraction II. Results of size exclusion HPLC on Superdex-75 were consistent with those of gel filtlation chromatography. On cellulose acetate electrophoresis, the mobility of GPF relative to that of GMP as 1.0 was found to average 1.2, suggesting a higher negative charge density in GPF than in GMP. It was concluded that papain digestion of GMP is an efficient method to produce glycopeptide with high sialic acid content.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".