A Year (plus) of DiffUSE

A Year (plus-ish) of DiffUSE

The DiffUSE Project began in July 2025 as an experiment in democratizing the methods of dynamic structural biology. While static models and sequence data have provided incredible insights into biological function and applications, in part due to AI, this data is reaching the limit of what it can explain. The most interesting biological problems cannot be addressed with a single structure. Elucidating the dynamics of transient biological events, understanding allostery, and investigating how binding events reshape the conformational landscape to achieve specificity all require the ensemble. But we are data-poor in this area. To reach the scale and diversity of data needed to be able to leverage, predict, and act upon protein dynamics,

Reaching the scale and diversity of data required to understand and ultimately predict protein dynamics demands a rethink of the entire pipeline, from data collection to interpretation, without the constraints of conventional static structure modeling.

The conventional approach is sequential: collect the data, process it, model it, encode it, then interpret it. But to actually get the data and information needed to tackle the ambitious biological problems of our day, we need to address the full pipeline at once.

But we are not improving one step in an existing workflow. We are changing an entire ecosystem. This work could not be done inside a conventional institutional structure. The bottleneck in dynamic structural biology is the absence of data at scale and the absence of methods that can absorb and collect it. Neither could be solved by an individual group, nor by focusing on individual biological problems. Both are solved by treating methods development as primary scientific work and staffing it accordingly. Our scientific organization is made possible by being a Radial project. Methods developers, platform engineers, and data infrastructure teams work alongside the scientists asking the biological questions. Loose teams form around data collection and processing, modeling, encoding, and interpretation. Each team works within the scope of the problem in front of it, and each output is usable by the others and by the field.

Over the past year, we have made significant progress in every area of the project. We demonstrated reproducibility with diffuse scattering across two beamlines (pub coming soon!); collected diffuse data on 8 different proteins; and advanced theory to better model this data. We also built a robust modeling platform that integrates structure predictors with experimental data and used it to stress-test how much memorization exists in structure predictors. We also recovered latent heterogeneity from deposited X-ray crystallography data in over 60k X-ray structures. We have developed algorithms and methods for improving our ability to model solvent in X-ray crystallography protein structures, a crux for correctly modeling experimental structural biology data. Finally, we have developed new ways to encode and visualize these dynamics. We have done all of this work completely openly, engaging with external groups and the greater structural biology community along the way.

A year in, the different areas of the project are now beginning to converge. We are beginning to integrate diffuse scattering data into our modeling software, and consider how to encode different types of heterogeneity in mmCIF files. As the different areas of the project converge, we are just starting to show the power of tackling the entire pipeline at once: the whole can be greater than the sum of the individual parts.

By the Numbers

4 Scholarly Pubs with 8 different contributors

• sampleworks: A Modular Platform for Experimentally Guided Biomolecular Ensemble Generation
• Supplying a User-Defined Bulk Solvent Map for Refinement
• Recovering Conformational Heterogeneity from the Protein Data Bank at Scale
• WaterFlow: Prediction of Ordered Water Molecule Positions on Protein Structures

16 Logbooks with 18 different contributors

5 Key Software Methods with 13 different contributors

• Mdx2: an open-source toolkit for diffuse data processing written in Python
• sampleworks: a Python framework for integrating generative biomolecular structure models with experimental data
• WaterFlow: a Deep Learning model that predicts the positions of ordered water molecules conditioned on a protein structure
• Goodvibes (General Optimization Of Diffuse halos from VIBrational Elastic network Simulations): a software package that produces a full forward model of the phononic contribution to the diffuse signal
• A Python library and command-line tool for reading, writing, and modifying PDBx/mmCIF protein structure files with support for hierarchical heterogeneity extensions

38 Blog Posts written by 14 different authors!

Making diffuse scattering reproducible

Diffuse scattering was the first experimental data type tackled within the project. In a typical macromolecular X-ray crystallography experiment, only about half of the X-rays scattered by the sample are analyzed. Those photons fall in the Bragg peaks, and they encode the average structure of the molecules in the lattice. Surrounding and between those peaks is a weaker signal, known as diffuse scattering, that captures deviations from the average structure. This signal not only provides one of the few direct measurements of correlated protein motion, which is essential for understanding catalysis, allostery, and ligand recognition.

For decades, diffuse scattering has been treated as background and discarded. This is primarily because the signal is difficult to measure, model, and interpret. Those difficulties have kept the method in the hands of a small number of specialist laboratories. Over the past several years, macromolecular diffuse scattering methodology has advanced substantially, and the measurement and interpretation problems that once defined the field are now considered tractable.

Building the tools and methods to democratize the method would allow rapid scaling by using existing synchrotron infrastructure. This would allow us to potentially reach a throughput of about 10,000 ensembles produced from diffuse scattering per year, providing the scale of data needed to learn about ensemble behavior, including for the training data necessary to predict and act upon protein dynamics. The joint analysis of the Bragg and diffuse components therefore returns two results from a single experiment: a high-resolution atomic model, and a description of how atomic displacements within that model are coupled.

Overall, our goal was to develop the methods and tools to democratize diffuse scattering. We started by aiming to obtain reproducible data from two different beamlines. Working across the Advanced Light Source at Berkeley (led by Kara Zielinski) and the Cornell High Energy Synchrotron Source (led by Steve Meisburger), we collected diffuse data on the same systems at both facilities and compared the resulting maps. Agreement between independently collected maps is the minimum standard for treating diffuse scattering as a quantitative measurement, enabling us to collect and analyze this data more widely. While there were no big surprises in samples and data collection, the lessons learned provide more background to help guide others in collecting data. With samples, we learned that humidity sleeves are essential to prevent dehydration over the course of the experiment, that the dose must be kept low, and that beamline background must also be low. Finally, crystal size matters; the larger the crystal, the better the data collected. We also found that multiple crystals are required for completeness, mostly because dose limits the rotation range available from any single crystal. We also observed that background subtraction is the most sensitive step in the pipeline. These insights have driven improvements in mdx2, our open-source package for processing diffuse scattering. The development of mdx2 is making the software more robust for other datasets. Finally, we can see that good diffuse data requires good Bragg data, with mosaicity and resolution being good proxies for diffuse map quality. Both are available early, which allows samples to be triaged before a full dataset is collected.

Outside of mac1, we also collected data on six other protein systems: NrdE, lysozyme, DNA fibers, ATCase, insulin, and Huwe1. Eighteen contributors collected this data, all logged in DiffUSE Logbook. This work helps democratize the collection, processing, and quality control of high-quality datasets, with the goal of allowing more groups (outside the DiffUSE project) to collect and process their own data. Be on the lookout for our upcoming publication detailing this analysis in much more detail! To model and interpret that data, we have mainly been using molecular dynamics as our primary route to modeling the signal. While this can provide valuable insights into the molecular motion we capture in the diffuse signal, a large gap remains between what the simulations produce and what the experimental data show. Closing that gap for improved modeling, including with machine learning (see below), requires pushing these physical models closer together. Therefore, in parallel, we are pushing further toward a complete forward model of diffuse scattering. Because diffuse scattering comes from multiple sources of deviations from the average structure, including deviations within the unit cell (lattice vibrations) and deviations or movement of macromolecules within the unit cell, we want to separate these two contributions to isolate the biologically interesting motions.

Our first approach is to continue developing GOODVIBES (led by Stephen Thornton), the software package that produces a full forward model of the phononic contribution. We are also working on generating accurate total scattering from crystalline molecular dynamics simulations and comparing it to experimental data (led by Taylor Womack and Mike Wall). Both are critical to determining how we model this signal. By simultaneously collecting and processing high-quality data today, combined with improving the theory of diffuse scattering, we will improve models of the data and biological motion down the road, similar to what has been done with Bragg peak data and cryo-EM data processing.

Machine Learning Modeling of Experimental Data

Machine learning structure predictors are now central to structural biology, in part because they can be used as a search mechanism over conformational space. Predicted models now routinely serve as templates for atomic model building and refinement, and placing predictors in an iterative loop with experimental data accelerates model building while improving prediction quality. One path to advance this idea is to exploit modern predictors’ ability to sample from their learned conformational distribution. Treated as conformational samplers, predictors can be combined with inference-time guidance, also described as scaling or steering, to improve agreement with experimental data. Such methods have been applied to single structures and, more recently, to ensembles. However, comparing results across these approaches or building on them was difficult. This limited our ability to iterate fast to improve modeling.

Developing sampleworks

To enable rapid development and iteration, we developed sampleworks, a platform for modeling experimental structural biology using machine learning. Our initial conditions for sampleworks were that it needed to take in multiple types of experimental data and be robust enough for us and others to build on in the future, as machine learning modeling approaches change. sampleworks provides abstractions for modular combinations of structure predictors, inference-time steering methods, and loss functions, so that new predictors and guidance methods can be implemented and tested against multiple forms of experimental data. It also includes evaluation infrastructure for parallel comparison of predictors and guidance methods, using metrics for ensemble quality and for agreement with experimental data.

We used sampleworks to test whether ensemble predictors have internalized physics or memorized their training distributions. We used alternative conformations deposited as altlocs within a single PDB as the test. They are physically real and experimentally supported, but are stripped from the training data of every public predictor. A predictor that has learned anything resembling physics should recover them, particularly when guided by experimental density. Without guidance, predictions collapse onto whichever conformation dominates the training set. Guidance substantially improves fit to density, raising the fraction of segments above an RSCC of 0.8 from 45.4 to 96.0 percent. However, we observed that the predictors tend to track their training distribution. While guidance reduces this bias, it did not remove it. But we can already see some machine learning tricks or changes to ensemble predictors that point to potential ways forward. sampleworks lets us integrate new experimental data types, such as diffuse scattering, to test new methods and release those capabilities for others to use in modeling structural data. Critically, this lets us integrate the project from data collection through machine-learning-based modeling.

Modeling existing heterogeneity from the PDB

One thing from our analysis with sampleworks is how limited conformational ensemble prediction is by training data. However, one of the largest untapped sources is the experimental data already deposited. Crystallography and cryo-EM measure data from millions of copies of macromolecules and average them. Conventional refinement collapses that average into one set of coordinates. However, the signal on these multiple states exists in the structure factors but needs to be modeled. Therefore, we set out to generate better ensemble data from the latent data in the structure factors.

We ran qFit multiconformer modeling across the high-resolution PDB to extract some of this latent heterogeneity. Starting from 80,876 PDB-REDO entries at 2 Å or better, the qFit multiconformer models fit the data better, with 85.7 percent having a lower R-free than the re-refined deposited comparison. They also contain substantially more conformational heterogeneity, with 9.4 percent of residues gaining at least one alternate conformation, and root-mean-square fluctuation increased by a mean of 0.13 Å. This is only the starting point. We can extract significantly more information from existing data. By combining sampleworks, qFit, and other machine learning approaches, we are extracting even more heterogeneity latent in structure factors. This is a key piece in getting us the ensemble data needed to accelerate generating the diversity we need to predict ensembles.

Solvent Modeling

Solvent occupies about 50% of a macromolecular crystal. X-ray scattering arises from every atom in the crystal, so solvent must be correctly accounted for to model the collected data. Beyond this, water molecules mediate protein stability, ligand binding, and catalysis, making them essential to understanding macromolecular biology. We model ordered waters in the first hydration shells as discrete atoms. However, predicting water molecule positions has lagged far behind protein and ligand structure prediction. Everything beyond them is approximated by a flat, binary mask.

We have tackled this with two approaches, first predicting ordered water molecules with WaterFlow using a deep learning model. The model sets a new state of the art, especially at sub-angstrom localization, where the gains concentrate, and sub-angstrom accuracy is what binding and catalysis applications require. WaterFlow is a two-stage model: a flow-matching generator learns a velocity field that transports prior samples to crystallographic water molecule positions, combined with a confidence model that scores each candidate; the user then sets a threshold that controls how many waters are returned. Both stages share a geometric vector perceptron backbone built on ESM3 residue embeddings. The largest single design gain came from crystallographic symmetry mates.

We demonstrated that we can predict bridging water molecules between proteins and ligands, and that our predicted water molecules fit experimental data better, including novel predictions with no deposited counterpart, sitting on positive difference density far more often than displaced decoys.

We also worked on integrating bulk solvent parameters into refinement software. While bulk solvent is often treated as flat and discrete, this is not what the data is actually capturing. The poor modeling of this solvent is a major contributor to the persistent gap between refinement R values and data quality. Richer descriptions exist, from molecular dynamics, 3D-RISM, and holographic reconstruction; however, getting them into refinement software to test them was a barrier. We developed a patch to integrate these.

Encoding Data for AI and Humans

Modeling heterogeneity is useless if the result cannot be communicated to AI and humans. PDBx/mmCIF represents alternate conformations through altlocs, which mark mutually exclusive atomic positions. It provides no way to state how those states relate. Beyond dynamics, this encoding is critical to communicate protein-ligand interactions. Fragment screening and time-resolved experiments generate exactly this structure of heterogeneity, and both are growing ways for us to understand new biology.

Over the past year, we have worked in the wwPDB and refinement programs to implement a new mmCIF category encoding hierarchical relationships between heterogeneity states, paired with a coexistence table specifying which states may co-occur. To support these changes, we have been developing visualization software and building off of existing modeling software, Bandicoot, to enable model building, visualization, and computing on these heterogeneous structures.

Functional evaluation

Collecting, modeling, and encoding structural data is valuable, but it has limited utility if it does not provide insight into function. However, how to even measure ‘function’ is an open question. A single protein can perform an extraordinary range of biological functions. It can catalyze reactions, bind ligands, and transduce signals allosterically. The impact of each function can change with small perturbations, such as a shift in pH, a macromolecular interaction, or a post-translational modification. Together, these functions are essential for biological processes. Predicting these functions from sequence data and how they change under such perturbations is a central goal of biology. This would enable interpretation of the millions of cataloged protein sequences, understanding the impact of mutations, and designing for function. One challenge of full functional characterization is that protein function is vast and multifaceted, spanning many levels of granularity. Untangling these effects is required to predict mutational consequences, design better therapeutics, and engineer proteins with novel properties.

High-throughput experimental methods now enable the collection of data on more specific, mechanistic functional landscapes at scale. However, each assay often measures a single molecular phenotype under a single engineered selection such as stability, binding, or enzymatic turnover. A variant can be neutral in the assay’s readout yet functionally disruptive through a mechanism the assay never probes. These assays can produce the scale machine learning needs; however, each captures only a subset of functional axes. To characterize models predicting protein function, we developed PLUG, an extensible framework for constructing leakage-free hold-out sets that enables evaluation of prediction models across multiple functional viewpoints. The benchmark compiler is organized so that any new functional benchmark can be added and a rebuilt leakage-free training set can be created against it, giving the community a moving target that resists the gradual contamination that has eroded the value of previous static benchmarks. We propose adopting PLUG as a framework for continual evaluation of protein function predictors, with careful homology detection to assess generalization and performance as a function of training similarity.

We are using PLUG as the baseline for our functional datasets, with our next endeavor looking at how dynamics impact function, focusing on cases where sequence data does not provide the necessary information.

Where are we now?

Beyond progressing on each of these fronts, we are beginning to integrate the pipeline. Diffuse scattering data is being brought directly into Sampleworks. In parallel, we are determining how best to encode Sampleworks ensemble outputs, and we are using conformational heterogeneity already present in the PDB to train better models to input into Sampleworks. We are also building functional outputs to establish where sequence or static structure provides sufficient functional information and where dynamic data is genuinely required, stress-testing our data collection and modeling.

Recurring lessons across the project:

• You can tackle ambitious science projects with the right people
• New organizational structures are needed to accelerate science
• Democratizing tools and methods requires returning to the basics
• Building robust tools that others can readily build upon is essential
• Working openly enables community engagement

We are building on these lessons to expand our experiment into new experimental modalities, new biological questions, and new AI-native approaches to solve these problems.