Remeasuring HEWL pK <sub>a</sub> values by NMR spectroscopy: Methods, analysis, accuracy, and implications for theoretical pK <sub>a</sub> calculations
Bibliographic record
Abstract
Site-specific pK(a) values measured by NMR spectroscopy provide essential information on protein electrostatics, the pH-dependence of protein structure, dynamics and function, and constitute an important benchmark for protein pK(a) calculation algorithms. Titration curves can be measured by tracking the NMR chemical shifts of several reporter nuclei versus sample pH. However, careful analysis of these curves is needed to extract residue-specific pK(a) values since pH-dependent chemical shift changes can arise from many sources, including through-bond inductive effects, through-space electric field effects, and conformational changes. We have re-measured titration curves for all carboxylates and His 15 in Hen Egg White Lysozyme (HEWL) by recording the pH-dependent chemical shifts of all backbone amide nitrogens and protons, Asp/Glu side chain protons and carboxyl carbons, and imidazole protonated carbons and protons in this protein. We extracted pK(a) values from the resulting titration curves using standard fitting methods, and compared these values to each other, and with those measured previously by ¹H NMR (Bartik et al., Biophys J 1994;66:1180–1184). This analysis gives insights into the true accuracy associated with experimentally measured pK(a) values. We find that apparent pK(a) values frequently differ by 0.5–1.0 units depending upon the nuclei monitored, and that larger differences occasionally can be observed. The variation in measured pK(a) values, which reflects the difficulty in fitting and assigning pH-dependent chemical shifts to specific ionization equilibria, has significant implications for the experimental procedures used for measuring protein pK(a) values, for the benchmarking of protein pK(a) calculation algorithms, and for the understanding of protein electrostatics in general.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".