Investigation of intact protein complexes by mass spectrometry
Bibliographic record
Abstract
Abstract I. Introduction 00 II. Electrospray Ionization of Biomacromolecules 00 III. Mass Analyzers for Biomacromolecular Mass Spectrometry 00 A. Collisional Cooling and/or Focusing 00 B. Alternative Mass Analyzers 00 IV. Solution‐Phase Properties of Proteins Complexes 00 A. Protein–Protein Interactions 00 B. Cooperativity and Cofactors 00 C. Dynamics of Assembly 00 V. Gas‐Phase Properties of Protein Complexes 00 A. Tandem Mass Spectrometry 00 B. Charge Separation in Protein Dissociation 00 VI. Future Outlook 00 Acknowledgments 00 References 00 Mass spectrometry has grown in recent years to a well‐accepted and increasingly important complementary technique in structural biology. Especially electrospray ionization mass spectrometry is well suited for the detection of non‐covalent protein complexes and their interactions with DNA, RNA, ligands, and cofactors. Over the last decade, significant advances have been made in the ionization and mass analysis techniques, which makes the investigation of even larger and more heterogeneous intact assemblies feasible. These technological developments have paved the way to study intact non‐covalent protein–protein interactions, assembly and disassembly in real time, subunit exchange, cooperativity effects, and effects of cofactors, allowing us a better understanding of proteins in cellular processes. In this review, we describe some of the latest developments and several highlights. © 2004 Wiley Periodicals, Inc., Mass Spec Rev
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".