Improved Structure-Based Histidine p<i>K</i><sub>a</sub> Prediction for pH-Responsive Protein Design
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide The near neutral p K a of histidine is commonly exploited to engineer pH-sensitive biomolecules. For example, histidine mutations introduced in the complementarity-determining region (CDR) of therapeutic antibodies can enhance selectivity for antigens in the acidic microenvironment of solid tumors or increase dissociation rates in the acidic early endosomes of cells. While solvent-exposed histidines typically have a p K a near 6.5, interacting histidines can experience p K a shifts of up to 4 pH units in either direction, making histidine one of the most variable titratable residues. To assist in selecting potential histidine mutation sites, p K a prediction software should achieve an accuracy significantly better than the current standard of around 1.0 pH unit. However, the limited availability of experimental histidine p K a measurements hinders the use of AI-based methods. This study evaluates histidine p K a predictions using Amber force field electrostatics combined with a continuum solvent model, previously calibrated in the solvated interaction energy (SIE) function for binding affinity predictions. By incorporating limited rotameric sampling, proton optimization, and an empirical correction for buried side-chains, the method achieves a mean unsigned error of 0.4 pH units across a diverse set of 91 histidines from 38 distinct protein structures obtained from the PKAD database. This approach should improve the in-silico design of pH-responsive mutations. The method is implemented in the software program JustHISpKa ( https://mm.nrc-cnrc.gc.ca/software/JustHISpKa ).
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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 | 0.000 |
| 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 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".