Characterizing the Termini of Recombinant Proteins
Bibliographic record
Abstract
Protein diversity is generated throughout a variety of biological mechanisms such as splicing, phosphorylation, acetylation among many other posttranslational modifications (PTMs) that add a chemical group to amino acids on proteins. These modifications have the effect of modifying protein functions, half-life, or cellular localization. Proteolytic processing is another yet irreversible modification that affects the vast majority of proteins, often with great functional consequences. Proteolytic processing has profound effects on the functionality of proteins and can either abrogate or antagonize function, modify half-life, and also determine cellular localization. Hence, determination of the true start of isolated or recombinant proteins is critical to ensure the product will have the desired functionality. However, since processing does not add a chemical group that can be utilized for affinity purification methodologies necessitating different strategies are required to determine the protein starts and ends. Due to the lack of a chemical group that otherwise facilitates affinity purification, N-terminal sequence assignation has unique challenges that have been addressed by biochemical and more recently mass spectrometric technologies. This chapter discusses the classic biochemical method of N-terminal protein determination by Edman sequencing—a classic technique for sequentially sequencing the amino acid residues from the N-terminus of a protein, the limitations of Edman analysis for moieties with blocked N-termini—as well as two separate mass spectrometry (MS)-based approaches. With the increasing availability of whole genome sequences, increasing computational power of modern data deconvolution algorithms and higher-performance liquid chromatography–tandem mass spectrometry (LC-MS/MS) instrumentation, determination of the true starts of proteins is becoming more routine. We discuss in particular a bottom-up approach, Amino Terminal Orientated Mass Spectrometry (ATOMS), as well as provide a brief discussion on the utilization of top-down MS for identification of intact protein molecules.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.005 |
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".