Sequential Peptide Affinity Purification System for the Systematic Isolation and Identification of Protein Complexes from Escherichia coli
Bibliographic record
Abstract
Biochemical purification of affinity-tagged proteins in combination with mass spectrometry methods is increasingly seen as a cornerstone of systems biology, as it allows for the systematic genome-scale characterization of macromolecular protein complexes, representing demarcated sets of stably interacting protein partners. Accurate and sensitive identification of both the specific and shared polypeptide components of distinct complexes requires purification to near homogeneity. To this end, a sequential peptide affinity (SPA) purification system was developed to enable the rapid and efficient isolation of native Escherichia coli protein complexes (J Proteome Res 3:463-468, 2004). SPA purification makes use of a dual-affinity tag, consisting of three modified FLAG sequences (3X FLAG) and a calmodulin binding peptide (CBP), spaced by a cleavage site for tobacco etch virus (TEV) protease (J Proteome Res 3:463-468, 2004). Using the lambda-phage Red homologous recombination system (PNAS 97:5978-5983, 2000), a DNA cassette, encoding the SPA-tag and a selectable marker flanked by gene-specific targeting sequences, is introduced into a selected locus in the E. coli chromosome so as to create a C-terminal fusion with the protein of interest. This procedure aims for near-endogenous levels of tagged protein production in the recombinant bacteria to avoid spurious, non-specific protein associations (J Proteome Res 3:463-468, 2004). In this chapter, we describe a detailed, optimized protocol for the tagging, purification, and subsequent mass spectrometry-based identification of the subunits of even low-abundance bacterial protein complexes isolated as part of an ongoing large-scale proteomic study in E. coli (Nature 433:531-537, 2005).
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.006 |
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".