Generation of an affinity matrix useful in the purification of natural inhibitors of plasmepsin II, an antimalarial‐drug target
Bibliographic record
Abstract
Various approaches for removal of high-abundance components in body fluids are currently available. While most methods are constructed for plasma depletion, there is a need for body-fluid-specific strategies. The aim of the present study was to design an affinity matrix suitable for the depletion of high-abundance proteins in CSF (cerebrospinal fluid). Hence, molecules with specific affinity towards proteins present at high concentration in CSF were desired. Affibody molecules are specific binders of small size that have shown high stability under various conditions and are therefore good candidates for such a matrix. The protein composition in CSF resembles that in plasma. However, 20% of the proteins are brain-derived and are therefore present in higher proportions in CSF than in plasma, whereas larger plasma-derived proteins are less abundant in CSF. Therefore five high-abundance CSF proteins were chosen for the design of a CSF-specific depletion setup. Affibody molecules with specificity towards HSA (human serum albumin), IgG, transferrin and transthyretin were combined in an affinity column. In addition, polyclonal antibodies against cystatin C were coupled to chromatographic beads and packed in a separate column. Highly reproducible and efficient removal of the five target proteins was observed. The proportion of depleted proteins were estimated to be 99, 95, 74, 92 and 83% for HSA, IgG, transferrin, transthyretin and cystatin C respectively. SDS/PAGE analysis was used for monitoring and identifying proteins in native CSF, depleted CSF samples and the captured fractions. Moreover, shotgun proteomics was used for protein identification in native as well as depleted CSF and the achieved data were compared. Enhanced identification of lower-abundance components was observed in the depleted fraction, in terms of more detected peptides per protein.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".