Binding mode–guided development of high-performance antibodies targeting site-specific posttranslational modifications
Bibliographic record
Abstract
Posttranslational modifications (PTMs) of proteins play critical roles in regulating many cellular events. Antibodies targeting site-specific PTMs are essential tools for detecting and enriching PTMs at sites of interest. However, fundamental difficulties in molecular recognition of both PTM and surrounding peptide sequence have hindered the efficient generation of highly sequence-specific anti-PTM antibodies. Furthermore, the widespread use of potentially inconsistent, nonrenewable, and molecularly undefined antibodies presents experimental challenges thought to contribute to the reproducibility problem in biomedical research. In this study, we describe the binding mode-guided development of a platform that efficiently generates potent and selective recombinant antibodies to PTMs that are molecularly defined and renewable. Our platform is built on our previous discovery of an unconventional binding mode of anti-PTM antibodies, antigen clasping, where two antigen binding sites cooperatively sandwich a single antigen, creating extensive interactions with the antigen and leading to high selectivity and potency. We designed the platform that generates clasping antibodies with two distinct binding units, resulting in efficient generation of antibodies to a set of trimethylated histone H3 with high levels of specificity and affinity. Performance comparison in chromatin immunoprecipitation, a common application in epigenomics, revealed that a clasping antibody to trimethylated histone H3 at lysine 27 exhibited superior specificity to a widely used conventional antibody and captured symmetric and asymmetric nucleosomes in a less biased manner. We further generated clasping antibodies to phosphotyrosine antigens by using the same principle. These results suggest the broad applicability of our platform to generating high-performance clasping antibodies to diverse PTMs.
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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".