Deciphering Pathways and Thermodynamics of Protein Assembly Using Native Mass Spectrometry
Bibliographic record
Abstract
Protein oligomerization regulates many critical physiological processes, and its dysregulation can contribute to dysfunction and diseases. Elucidating the assembly pathways and quantifying their underlying thermodynamic and kinetic parameters are crucial for a comprehensive understanding of biological processes and for advancing therapeutics targeting abnormal protein oligomerization. Established binding assays, with limited mass precision, often rely on simplified models for data interpretation. In contrast, high-resolution native mass spectrometry (nMS) can directly determine the stoichiometry of biomolecular complexes in vitro. However, quantification is hindered by the fact that the relative abundances of gas-phase ions generally do not reflect solution concentrations due to nonuniform response factors. Recently, slow mixing mode (SLOMO)-nMS, which can quantify the relative response factors of interacting species, has been demonstrated to reliably measure the affinity ( K d ) of binary biomolecular complexes. Here, we introduce an extended form of SLOMO-nMS that enables simultaneous quantification of the thermodynamics in multistep association reactions. Application of this method to homo-oligomerization of concanavalin A and insulin confirmed the reliability of the assay and uncovered details about the assembly processes that had previously resisted elucidation. Results acquired using SLOMO-nMS implemented with charge detection shed new light on the binding of recombinant human angiotensin-converting enzyme 2 and the SARS-CoV-2 spike protein. Importantly, new assembly pathways were uncovered, and the affinities of these interactions, which regulate host cell infection, were quantified. Together, these findings highlight the tremendous potential of SLOMO-nMS to accelerate the characterization of protein assembly pathways and thermodynamics and, in so doing, enhance fundamental biological understanding and facilitate therapeutic development. https://orcid.org/0000-0002-3389-7112
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".