Synthetic antibodies and peptides recognizing progressive multifocal leukoencephalopathy-specific point mutations in polyomavirus JC capsid viral protein 1
Bibliographic record
Abstract
Polyomavirus JC (JCV) is the causative agent of progressive multifocal leukoencephalopathy (PML), a rare and frequently fatal brain disease that afflicts a small fraction of the immune-compromised population, including those affected by AIDS and transplantation recipients on immunosuppressive drug therapy. Currently there is no specific therapy for PML. The major capsid viral protein 1 (VP1) involved in binding to sialic acid cell receptors is believed to be a key player in pathogenesis. PML-specific mutations in JCV VP1 sequences present at the binding pocket of sialic acid cell receptors, such as L55F and S269F, abolish sialic acid recognition and might favor PML onset. Early diagnosis of these PML-specific mutations may help identify patients at high risk of PML, thus reducing the risks associated with immunosuppressive therapy. As a first step in the development of such early diagnostic tools, we report identification and characterization of affinity reagents that specifically recognize PML-specific mutations in VP1 variants using phage display technology. We first identified 2 peptides targeting wild type VP1 with moderate specificity. Fine-tuning via selection of biased libraries designed based on 2 parental peptides yielded peptides with different, yet still moderate, bindinspecificities. In contrast, we had great success in identifying synthetic antibodies that recognize one of the PML-specific mutations (L55F) with high specificity from the phage-displayed libraries. These peptides and synthetic antibodies represent potential candidates for developing tailored immune-based assays for PML risk stratification in addition to complementing affinity reagents currently available for the study of PML and JCV.
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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) | 0.001 | 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 | 0.000 |
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 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".