Biology subjects

Woollard, G.

Publications and source records attributed to Woollard, G..

6 recordsLinked to original sources

bioRxiv2026

The CAHRA Challenge: A Community-Wide Assessment of Cryo-EM Heterogeneous Reconstruction Algorithms

The ability of cryo-electron microscopy (cryo-EM) to interrogate the atomic structure and motion of biomolecules has motivated the development of a wide range of algorithms for heterogeneity analysis. However, evaluating and comparing these methods remains challenging because ground-truth structures are generally unknown for experimental samples. Here, we introduce the 2026 Community-Wide Assessment of Heterogeneous Reconstruction Algorithms (CAHRA), a community-wide challenge centered on three benchmark datasets that probe distinct tasks in heterogeneity analysis. These include (1) compositional heterogeneity arising from mixtures of distinct protein complexes, (2) continuous conformational variability and atomic modeling, and (3) entanglement between molecular conformation and particle pose. We describe the design, construction, and validation of these datasets, as well as their use in the recently completed CAHRA Challenge.

biophysics↗

bioRxiv2025

Improving Cryo-EM Optimization Robustness with an Optimal Transport Loss Function for Noisy Images

Many tasks in single-particle cryo-electron microscopy (cryo-EM), such as 2D/3D classification and homo/heterogeneous reconstruction, require optimizing model parameters to minimize the discrepancy between observed data and a forward model. The standard Mean Squared Error (MSE) loss function is computationally efficient but suffers from a non-convex rugged loss landscape, particularly for high-resolution heterogeneity inference. In this work, we investigate the practical utility of Sliced Wasserstein (SW) distances. We implement exact W2 estimators (inverse-CDF and greedy matching) of projections alongside a computationally efficient proxy based on the L2 norm of CDFs, a formulation akin to the sliced Cramer-von Mises distance. We establish the latter as a robust, fully differentiable workhorse for the cryo-EM forward model. We evaluate its performance against the MSE in joint inference tasks recovering pose, CTF parameters, and conformational heterogeneity. Our results demonstrate that SW significantly broadens the basin of attraction, enabling robust gradient-based optimization from distant initializations where MSE fails. Using a helical spiral toy model, we highlight how SW losses are sensitive to per-particle contrast, where background noise level miscalibration can induce geometric bias in the inferred structure. We show that this bias is manageable through a joint optimization strategy that treats background contrast as a learnable parameter. Finally, we validate the approach on a synthetic dataset using the Zernike3D framework, showing that the SW loss works and yields an accurate landscape representations, comparable with MSE. These findings establish SW as a powerful tool for navigating the rugged landscapes of cryo-EM forward model parameters. SynopsisThe Sliced Wasserstein loss provides a smoother optimization landscapes than mean squared error for single particle cryo-EM joint inference of pose, CTF defocus and conformational heterogeneity. Estimating background contrast is essential to avoid biasing other parameters.

molecular biology↗

bioRxiv2025

CryoJAX - A Cryo-Electron Microscopy Image Simulation Library in JAX

While cryo-electron microscopy (cryo-EM) has come to prominence in the last decade due to its ability to resolve biomolecular complexes at atomic resolution, advancements in experimental and computational methods have made cryo-EM promising for investigating intracellular organization and heterogeneous molecular states. A primary challenge for these alternative applications is the development of techniques for cryo-EM data analysis, which are very computationally demanding. To this end, it is advantageous to leverage advanced scientific computing frameworks for statistical analysis. One such framework is JAX, an emerging array-oriented Python numerical computing package for automatic differentiation and vectorization with a growing ecosystem for statistical inference and machine learning. We have developed cryoJAX, a cryo-EM image simulation library for building computational data analysis applications in JAX. CryoJAX is a flexible modeling language for cryo-EM image formation and therefore can support a wide range of data analysis downstream. By integrating with the JAX ecosystem, cryoJAX enables the development and deployment of algorithms for the growing breadth of scientific applications for cryo-EM. SynopsisThe authors have developed cryoJAX, a cryo-EM image simulation library for developing data analysis techniques across cryo-EM modalities. CryoJAX is built on JAX, an emerging scientific computing framework in Python well suited for cryo-EM data analysis.

biophysics↗

bioRxiv2025

The Inaugural Flatiron Institute Cryo-EM Conformational Heterogeneity Challenge

Despite the rise of single particle cryo-electron microscopy (cryo-EM) as a premier method for resolving macromolecular structures at atomic resolution, methods to address molecular heterogeneity in vitrified samples have yet to reach maturity. With an increasing number of new methods to analyze the multitude of heterogeneous states captured in single particle images, a systematic approach to validation in this field is needed. With this motivation, we issued a challenge to the community to analyze two cryo-EM particle image sets of thyroglobulin that exhibit continuous conformational heterogeneity. The first dataset was experimental and the second was generated with a simulator, allowing control over the distribution of molecular structures and enabled direct comparison between participants submissions and the ground truth molecular structures and distributions. Participants were asked to submit 80 volumes representing the heterogeneous ensemble and estimate their respective populations in the image sets provided. Participation of the research community in the challenge was strong, with submissions from nearly all developers of heterogeneity methods, resulting in 41 submissions across both datasets. Submissions qualitatively exceeded expectations, with the molecular motions identified by methods resembling both each other and the ground truth motion. However, quantitatively assessing these similarities was a challenge in and of itself. In the process of assessing the submissions, we developed several validation metrics, most of which require reference to the underlying ground truth volumes. However, we have also explored the use of metrics that do not necessarily reference ground truth. This is particularly apt for experimental datasets where ground truth is inaccessible. These approaches allowed us to assess the similarity and accuracy in volume quality, molecular motions, and conformational distribution of di!erent submissions. These metrics and the e!orts of all participants help chart a path forward for the improvements of heterogeneity methods for cryo-EM and for future challenges to validate these new methods as they continue to be developed by the community.

biophysics↗

bioRxiv2021

Polymeric assembly of endogenous Tuberous Sclerosis Protein Complex

Tuberous Sclerosis protein complex (pTSC) nucleates a proteinaceous signaling hub that integrates information about the internal and external energy status of the cell in regulation of growth and energy consumption. Biochemical and electron cryomicroscopy (cryoEM) studies of recombinant pTSC have revealed the structure and stoichiometry of the pTSC and have hinted at the possibility that the complex form large oligomers. Here, we have partially purified endogenous pTSC from fasted mammalian brains of rat and pig by leveraging a recombinant antigen binding fragment (Fab) specific for the TSC2 subunit of pTSC. We demonstrate Fab dependent purification of pTSC from membrane solubilized fractions of the brain homogenates. Negative stain electron microscopy of the samples purified from pig brain demonstrates rod-shaped protein particles with a width of 10 nm, a variable length as small as 40 nm and a high degree of conformational flexibility. Larger filaments are evident with a similar 10 nm width and up to 1 m in length in linear and web-like organizations prepared from pig brain. These observations suggest polymerization of endogenous pTSC into filamentous super-structures.

biochemistry↗

bioRxiv2018

Comprehensive mapping of Cystic Fibrosis mutations to CFTR protein identifies mutation clusters and molecular docking predicts corrector binding site

BackgroundCystic Fibrosis (CF) is caused by mutations in the CFTR gene, of which over 2000 have been reported to date. Mutations have yet to be analyzed in aggregate to assess their distribution across the tertiary structure of the CFTR protein, an approach that could provide valuable insights into the structure-function relationship of CFTR. In addition, the binding site of Class I correctors (VX-809, VX-661, C18) is not well understood.\n\nMethodsExonic CFTR mutations and mutant allele frequencies described in three curated databases (ABCMdb, CFTR1 and CFTR2, comprising >130,000 data points) were mapped to two different structural models: a homology model of full-length CFTR protein in the open-channel state, and a cryo-electron microscopy core-structure of CFTR in the closed-channel state. Immunoblotting confirmed the approximate binding site of Class I correctors, and molecular docking generated binding poses for their complex with the cryo-electron microscopy structure.\n\nResultsResidue positions of six high-frequency mutant CFTR alleles were found to spatially co-localize in CFTR protein, and a significant cluster was identified at the NBD1:ICL4 interdomain interface. Further, Class I correctors VX-809, VX-661 and C18 were shown to act via a similar mechanism in vitro, and a putative multi-domain corrector binding site near residues F374-L375 was predicted in silico.\n\nConclusionsOur results confirm the significance of interdomain interfaces as susceptible to disruptive mutation, and identify a putative corrector binding site. The structural pharmacogenomics approach of mapping mutation databases to protein models shows promise for facilitating drug discovery and personalized medicine for monogenetic diseases.

biochemistry↗