Assay Development and High-Throughput Screening for Inhibitors of Kaposi’s Sarcoma–Associated Herpesvirus N-Terminal Latency-Associated Nuclear Antigen Binding to Nucleosomes
Bibliographic record
Abstract
Kaposi's sarcoma-associated herpesvirus (KSHV) has a causative role in several human malignancies, especially in immunocompromised hosts. KSHV latently infects tumor cells and persists as an extrachromosomal episome (plasmid). KSHV latency-associated nuclear antigen (LANA) mediates KSHV episome persistence. LANA binds specific KSHV sequence to replicate viral DNA. In addition, LANA tethers KSHV genomes to mitotic chromosomes to efficiently segregate episomes to daughter nuclei after mitosis. N-terminal LANA (N-LANA) binds histones H2A and H2B to attach to chromosomes. Currently, there are no specific inhibitors of KSHV latent infection. To enable high-throughput screening (HTS) of inhibitors of N-LANA binding to nucleosomes, here we develop, miniaturize, and validate a fluorescence polarization (FP) assay that detects fluorophore-labeled N-LANA peptide binding to nucleosomes. We also miniaturize a counterscreen to identify DNA intercalators that nonspecifically inhibit N-LANA binding to nucleosomes, and also develop an enzyme-linked immunosorbent assay to assess N-LANA binding to nucleosomes in the absence of fluorescence. HTS of libraries containing more than 350,000 compounds identified multiple compounds that inhibited N-LANA binding to nucleosomes. No compounds survived all counterscreens, however. More complex small-molecule libraries will likely be necessary to identify specific inhibitors of N-LANA binding to histones H2A and H2B; these assays should prove useful for future screens.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".