Mapping substrate interactions of the human membrane-associated neuraminidase, NEU3, using STD NMR
Bibliographic record
Abstract
Saturation transfer difference (STD) nuclear magnetic resonance (NMR) is a powerful technique which can be used to investigate interactions between proteins and their substrates. The method identifies specific sites of interaction found on a small molecule ligand when in complex with a protein. The ability of STD NMR to provide specific insight into binding interactions in the absence of other structural data is an attractive feature for its use with membrane proteins. We chose to employ STD NMR in our ongoing investigations of the human membrane-associated neuraminidase NEU3 and its interaction with glycolipid substrates (e.g., GM3). In order to identify critical substrate-enzyme interactions, we performed STD NMR with a catalytically inactive form of the enzyme, NEU3(Y370F), containing an N-terminal maltose-binding protein (MBP)-affinity tag. In the absence of crystallographic data on the enzyme, these data represent a critical experimental test of proposed homology models, as well as valuable new structural data. To aid interpretation of the STD NMR data, we compared the results with molecular dynamics (MD) simulations of the enzyme-substrate complexes. We find that the homology model is able to predict essential features of the experimental data, including close contact of the hydrophobic aglycone and the Neu5Ac residue with the enzyme. Additionally, the model and STD NMR data agree on the facial recognition of the galactose and glucose residues of the GM3-analog studied. We conclude that the homology model of NEU3 can be used to predict substrate recognition, but our data indicate that unstructured portions of the NEU3 model may require further refinement.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".