High-throughput Identification of DNA-Encoded IgG Ligands that Distinguish Active and Latent<i>Mycobacterium tuberculosis</i>Infections
Bibliographic record
Abstract
The circulating antibody repertoire encodes a patient’s health status and pathogen exposure history, but identifying antibodies with diagnostic potential usually requires knowledge of the antigen(s). We previously circumvented this problem by screening libraries of bead-displayed small molecules against case and control serum samples to discover “epitope surrogates” (ligands of IgGs enriched in the case sample). Here, we describe an improved version of this technology that employs DNA-encoded libraries and high-throughput FACS-based screening to discover epitope surrogates that differentiate noninfectious/latent (LTB) patients from infectious/active TB (ATB) patients, which is imperative for proper treatment selection and antibiotic stewardship. Normal control/LTB (10 patients each, NCL) and ATB (10 patients) serum pools were screened against a library (5 × 10 6 beads, 448 000 unique compounds) using fluorescent antihuman IgG to label hit compound beads for FACS. Deep sequencing decoded all hit structures and each hit’s occurrence frequencies. ATB hits were pruned of NCL hits and prioritized for resynthesis based on occurrence and homology. Several structurally homologous families were identified and 16/21 resynthesized representative hits validated as selective ligands of ATB serum IgGs ( p < 0.005). The native secreted TB protein Ag85B (though not the E. coli recombinant form) competed with one of the validated ligands for binding to antibodies, suggesting that it mimics a native Ag85B epitope. The use of DNA-encoded libraries and FACS-based screening in epitope surrogate discovery reveals thousands of potential hit structures. Distilling this list down to several consensus chemical structures yielded a diagnostic panel for ATB composed of thermally stable and economically produced small molecule ligands in place of protein antigens.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".