Proper modelling of ligand binding requires an ensemble of bound and unbound states
Bibliographic record
Abstract
Synopsis We emphasise and demonstrate the importance of modelling the superpositions of ligand-bound and unbound states that commonly occur in crystallographic datasets. Generation of an ensemble that describes not only the dominant state in the crystal is important for the high-quality refinement of low-occupancy ligands, as well as to present a model that explains all of the observed density. Abstract Small molecules bind to only a fraction of the proteins in the crystal lattice, but occupancy refinement of ligands is often avoided by convention; occupancies are set to unity, assuming that the error will be adequately modelled by the B-factors, and weak ligand density is generally ignored or attributed to disorder. Where occupancy refinement is performed, the superposed atomic state is rarely modelled. We show here that these modelling approaches lead to a degradation of the quality of the ligand model, and potentially affect the interpretation of the interactions between the bound ligand and the protein. Instead, superior accuracy is achieved by modelling the ligand as partially occupied and superposed on a ligand-free “ground-state” solvent model. Explicit modelling of the superposed unbound fraction of the crystal using a reference dataset allows constrained refinement of the occupancy of the ligand with minimal fear of over-fitting. Better representation of the crystal also leads to more meaningful refined atomic parameters such as the B-factor, allowing more insight into dynamics in the crystal. We present a simple approach and simple guidelines for generating the ensemble of bound and unbound states, assuming that datasets representing the unbound states (the ground state) are available. Judged by various electron density metrics, ensemble models are consistently better than corresponding single-state models. Furthermore, local modelling of the superposed ground state is found to be generally more important for the quality of the ligand model than convergence of the overall phases.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".