A Bioinformatics Approach to Identify Natural Autoantibodies from Healthy Blood Donors' Sera Reactive with the HCV NS5A-Derived Peptide by Immunoassay
Bibliographic record
Abstract
Natural autoantibodies (NAbs) are continually produced throughout life and have an ability to recognize self and altered self, as well as foreign antigens, by recognizing cellular pattern recognition receptors. Sometimes NAb specificity demonstrates overlap between human and pathologic proteomes. This information can be useful in selecting target sequences for screening purposes. In this study we undertook a multi-step bioinformatics search to predict a virus-derived peptide that can be recognized by NAbs in sera of uninfected individuals. We selected protein hepatitis C virus (HCV) NS5A as a target sequence, motivated by the fact that the HCV proteome is characterized by extensive sequence similarities to the human proteome, and because screening for anti-HCV antibodies, including anti-NS5A, is important clinically, particularly in screening of potential blood donors. The virus-specific peptide P1, and the homologous human peptide derived from enzyme-inducible nitric oxide synthase (iNOS), P2, exhibiting not only simple homology, but also complementarities of physicochemical patterns, were synthesized and 80 HCV-negative and 50 HCV-positive blood donor sera were tested by ELISA. These peptides reacted similarly (p<0.001) with HCV-negative sera, and in several cases the measured reactivity was significantly above the cut-off value of commercial anti-HCV screening assays. In HCV-positive sera, the titers of antibodies reactive with analyzed HCV NS5A peptide were not significantly increased (p<0.001) compared to host peptide, the implications of which are unclear, but may be consistent with these antibodies being "naturally produced." Finally, we extended our bioinformatics analyses to the dataset of human self-binding sequences, and propose a general approach for the selection of specific diagnostic and screening antigens for use in immunoassays.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".