Utilization of Oriented Peptide Libraries to Identify Substrate Motifs Selected by ATM
Bibliographic record
Abstract
The ataxia telangiectasia mutated (ATM) gene encodes a serine/threonine protein kinase that plays a critical role in genomic surveillance and development. Here, we use a peptide library approach to define the in vitro substrate specificity of ATM kinase activity. The peptide library analysis identified an optimal sequence with a central core motif of LSQE that is preferentially phosphorylated by ATM. The contributions of the amino acids surrounding serine in the LSQE motif were assessed by utilizing specific peptide libraries or individual peptide substrates. All amino acids comprising the LSQE sequence were critical for maximum peptide substrate suitability for ATM. The DNA-dependent protein kinase (DNA-PK), a Ser/Thr kinase related to ATM and important in DNA repair, was compared with ATM in terms of peptide substrate selectivity. DNA-PK was found to be unique in its preference of neighboring amino acids to the phosphorylated serine. Peptide library analyses defined a preferred amino acid motif for ATM that permits clear distinctions between ATM and DNA-PK kinase activity. Data base searches using the library-derived ATM sequence identified previously characterized substrates of ATM, as well as novel candidate substrate targets that may function downstream in ATM-directed signaling pathways.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".