Mapping Protein–Ligand Interactions in the Gas Phase Using a Functional Group Replacement Strategy. Comparison of CID and BIRD Activation Methods
Bibliographic record
Abstract
Intermolecular interactions in the gaseous ions of two protein-ligand complexes, a single chain antibody (scFv) and its trisaccharide ligand (α-D-Galp-(1→2)-[α-D-Abep-(1→3)]-α-Manp-OCH3, L1) and streptavidin homotetramer (S4) and biotin (B), were investigated using a collision-induced dissociation (CID)-functional group replacement (FGR) strategy. CID was performed on protonated ions of a series of structurally related complexes based on the (scFv + L1) and (S4 + 4B) complexes, at the +10 and +13 charge states, respectively. Intermolecular interactions were identified from decreases in the collision energy required to dissociate 50% of the reactant ion (Ec50) upon modification of protein residues or ligand functional groups. For the (scFv + L1)(10+) ion, it was found that deoxygenation of L1 (at Gal C3 and C6 and Man C4 and C6) or mutation of His101 (to Ala) resulted in a decrease in Ec50 values. These results suggest that the four hydroxyl groups and His101 participate in intermolecular H-bonds. These findings agree with those obtained using the blackbody infrared radiative dissociation (BIRD)-FGR method. However, the CID-FGR method failed to reveal the relative strengths of the intermolecular interactions or establish Man C4 OH and His101 as an H-bond donor/acceptor pair. The CID-FGR method correctly identified Tyr43, but not Ser27, Trp79, and Trp120, as a stabilizing contact in the (S4 + 4B)(13+) ion. In fact, mutation of Trp79 and Trp120 led to an increase in the Ec50 value. Taken together, these results suggest that the CID-FGR method, as implemented here, does not represent a reliable approach for identifying interactions in the gaseous protein-ligand complexes.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".