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Record W4415590417 · doi:10.1063/4.0000943

Analytical Differentiable Finite-Resolution Density Map Calculation in CCTBX/Phenix

2025· article· en· W4415590417 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueStructural Dynamics · 2025
Typearticle
Languageen
FieldMaterials Science
TopicElectron and X-Ray Spectroscopy Techniques
Canadian institutionsNickel Institute
Fundersnot available
KeywordsDifferentiable functionFocus (optics)Fourier transformReciprocal latticeSpace (punctuation)SimplicitySoftwareReciprocal

Abstract

fetched live from OpenAlex

Beyond validation, publication, and other analyses, the final stage of structure determination using cryo-EM typically involves atomic model refinement against experimental data. This refinement is most naturally performed in real space - bypassing Fourier space - because all objects at this stage, including models and maps, exist in real space. However, many tools currently used in cryo-EM structure determination originate from and remain anchored to crystallography, which primarily operates in reciprocal (Fourier) space. While Phenix tools specifically designed for cryo-EM during the resolution revolution were tailored to operate in real space - such as phenix.real_space_refine for atomic model refinement against maps, which accounts for 95% of structures deposited in the PDB using cryo-EM - they are still suboptimal in at least two aspects. First, the refinement target for coordinate refinement in phenix.real_space_refine is perhaps the simplest and fastest to compute, as it focuses on fitting atoms to the nearest density peaks without considering the overall shape of the density map. The advantage of this approach is that it enables refinement of very large molecules on relatively modest computing resources (e.g., laptops). However, the drawback is the need for excessive geometric restraints to compensate for the simplicity of this refinement target, which does not account for the shape of the map. The second limitation is that ADP (B-factor) and occupancy refinements still require a bypass through Fourier space, as they rely on spatial integration of density peaks for fitting. Here, we will focus our discussion on implementing resolution-truncated density map calculations in CCTBX and Phenix using accurate, differentiable analytic approximations of density maps. This implementation will enable more accurate real-space refinement in Phenix for all atomic model parameters (coordinates, ADPs, occupancies, etc.), eliminating the need for Fourier space entirely in this process. Additionally, making this approach available in the freely accessible CCTBX framework will provide the broader community with a uniform method for computing finite-resolution density maps and their derivatives with respect to atomic model parameters. This is particularly valuable for machine learning-based model building and refinement approaches, where algorithms for computing differentiable finite-resolution density maps are essential (e.g., qFit, ROCKET).

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.719
Threshold uncertainty score0.564

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.006
GPT teacher head0.276
Teacher spread0.270 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it