DiffUSE January 2026 Retreat: From Coast to Coast, Diffuse Scattering Reproduces

Why This Retreat Mattered

What Have We Accomplished Since October?

Data Collection

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  9 beamtimes since project inception across two synchrotrons (CHESS and ALS)
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  18 participants contributed to data collection
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  7 protein systems: Mac1, NrdE, Lysozyme, DNA fibers, ATCase, Insulin, and Huwe1
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  129 "good" datasets collected (no data collection errors)

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  Temperature: Data collected at 100K (cryo), 220-275K (intermediate), and 310-315K (elevated), though sample handling for intermediate temperatures requires further optimization
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  Ligands: Mac1 + ADPr (11 datasets from CHESS and ALS combined) and Mac1 + small molecule "opener" (6 datasets from ALS)
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  Radiation damage mitigation: Vector scans implemented at CHESS to spread dose across radiation-sensitive samples like NrdE and ATCase

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  ALS: Explored dose dependence, wavelength effects, and exposure time optimization; addressed collimator ring scatter issues at 14 keV
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  CHESS: Continued X-ray aperture optimization for background reduction

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  Containerized deployment via conda install -c conda-forge mdx2
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  Jupyter Lab environment integration
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  Live processing capability on Voltage Park during beam times

Molecular Dynamics Simulations

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  Total correlation coefficient: CC = 0.96
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  Anisotropic correlation coefficient: CC = 0.56
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  Simulation: 2×2×2 supercell with OPC3 waters (279,004 atoms), neutron crystal structure 7TX3, 1100 ns unrestrained trajectory

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  Enrichment: Selectively removing MD frames to increase diffuse correlation
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  Reweighting: Using JAX to optimize frame weights via differentiable Pearson CC maximization. Initial test on experimental diffuse data achieved CC = 0.97 with 47,150 reflections to 3.5 Å resolution (work by Karson Chrispens, documented in a DiffUSE blog post)

Machine Learning Modeling

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  Model wrappers implemented for RF3, Protenix, Boltz-1, and Boltz-2 (MD and X-ray modes)
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  Initial test set of ~50 structures from PDB prepared with altlocs; electron density maps being generated
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  Evaluation metrics: RSCC, LDDT, clash scores, backbone and sidechain geometry

Infrastructure and Publishing

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  H100 SXM5 GPUs available via bare metal (8× GPU configurations)
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  Two usage patterns supported:

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      Workspaces: Interactive environments for experimental work, debugging, and visualization
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      Workflows: Hardened, scalable pipelines for production analysis
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  The DiffUSE web app now provides resource checkout, visibility into running resources, and SSH/Jupyter access
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  Custom container management enables workspace pausing and environment customization
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  Workflow orchestration via Prefect and Docker

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  Storage: Core Backblaze storage (S3-compatible) with OSN bucket integration for beamline data
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  Access: Automatic mounting to Voltage Park resources, plus web app download, CLI, Python SDK, and API
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  Metadata: Experiments have artifacts, optional markdown content (like logbook entries), relationships to other experiments, and tags
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  Automation: Beam-trip data automatically triggers experiment registration; dataset files populate metadata fields
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  Governance: Standards compliance checking, staging-to-public workflows, DOI attachment decisions

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  When to stage data privately vs. make everything open immediately
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  When to attach DOIs (content should be largely immutable)
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  External database destinations: SBGrid Databank, PDB, Zenodo

Open Science

Reflections on Our Distributed Model

What's Next?

Data Collection (3-month goals)

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  Collect data on additional systems; collect lysozyme at ALS
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  Optimize sample handling for intermediate temperatures (oil-based approaches)
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  Explore serial crystallography approaches (chip types, small wedges, crystal size variation)
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  Continue investigating cryo options (traditional, NANUQ, high-pressure cryocooling)

Data Processing (2026 goals)

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  Improve mdx2 performance (~2× speedup)
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  Implement GOODVIBES and DISCOBALL in Python (JAX)
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  Fully explore serial crystallography processing
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  Deploy on Ando lab Galaxy server; add mdx2 tools
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  Develop "Cryosparc for diffuse" project roadmap

MD Simulations

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  Continue model/data comparisons and refine MD models (protonation states, parameterization)
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  Expand to new systems and additional ligand/mutation perturbations
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  Explore how MD optimizations can support other DiffUSE activities (ML modeling, diffraction image simulation, data processing validation)

ML Modeling

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  Scale up Sampleworks evaluation across initial test set
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  Improve water prediction models (retrain SuperWater with better data, explore flow matching vs. diffusion)
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  Quantify water model precision requirements by systematically perturbing well-supported waters
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  Progress toward reciprocal space/Bragg peak guidance, ultimately targeting diffuse data guidance

Infrastructure

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  Finalize containerized workspace management with pause/resume capability
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  Expand workflow orchestration options
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  Refine data governance workflows for staging → public → external database publication

Open Science

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  Address identified barriers to sharing
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  Establish timelines for DOI attachment and external database deposition
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  Continue documentation through blog posts and logbooks

Glossary