An endogenous ‘non-specific’ protein detected by a His-tag antibody is human transcription regulator YY1
Bibliographic record
Abstract
Histidine-tags have been used for a wide variety of experiments including protein purification, Western blots, immunoprecipitation and immunohistochemistry. In our previous studies, we have repeatedly detected a 'non-specific' endogenous protein of about 60 kD in Western blots of protein lysates from HEK293T or HeLa cells using the anti-His-tag antibody (His-probe (H3), catalogue #, SC-8036, Santa Cruz Biotech. Co.) (Yu et al., J. Biol. Chem. 284 (2009) 1505-1513). Here we have immunoprecipitated the protein from HeLa nuclear extracts using the anti-His-tag antibody, excised the 60 kD band and subjected it to LC-MS/MS (Fig. 1). The deduced sequences of two peptides of the protein match the human transcriptional regulator YY1 (Yin and Yang 1, UniProt ID, P25490, Fig. 2), which contains 11 histidine residues in a stretch (from amino acid 70 to 80) at its NH2-terminal region without known functions (Lee et al., Nucleic Acids Res. 23 (1995) 925-931; Bushmeyer et al., J. Biol. Chem. 270 (1995) 30213-30220). Since genes encoding other Histidine-repeat proteins also exist in the genome (Salichs et al., PLoS Genet. 5 (2009) e1000397), it is possible that YY1 might not be the only endogenous protein that could be expressed and recognized by the antibody in different sources of samples in future experiments. The presence of various endogenous histidine-repeat proteins suggests that data from experiments particularly immunostaining using His-tag antibodies need to be interpreted with caution. This might also be useful to the broader scientific community by providing an example for the interpretation of 'non-specific' bands in Western blots.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".