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publications
Structure and Intrinsic Disorder in Protein Autoinhibition
Published in Structure, 2013
Autoinhibition plays a significant role in the regulation of many proteins. By analyzing autoinhibited proteins, we demonstrate that these proteins are enriched in intrinsic disorder because of the properties of their inhibitory modules (IMs). A comparison of autoinhibited proteins with structured and intrinsically disordered IMs revealed that in the latter group (1) multiple phosphorylation sites are highly abundant; (2) splice variants occur in greater number than in their structured cousins; and (3) activation is often associated with changes in secondary structure in the IM. Analyses of families of autoinhibited proteins revealed that the levels of disorder in IMs can vary significantly throughout homologous proteins, whereas residues located at the interfaces between the IMs and inhibited domains are conserved. Our findings suggest that intrinsically disordered IMs provide advantages over structured ones that are likely to be exploited in the fine-tuning of the equilibrium between active and inactive states of autoinhibited proteins.
Recommended citation: Travis Trudeau, Roy Nassar, Alexander Cumberworth, Eric T.C. Wong, Geoffrey Woollard, Jörg Gsponer. (2013). "Structure and Intrinsic Disorder in Protein Autoinhibition." Structure. 21(3),332-341.
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Computational proteome-wide screening predicts neurotoxic drug-protein interactome for the investigational analgesic BIA 10-2474
Published in Biochemical and Biophysical Research Communications, 2017
The investigational compound BIA 10-2474, designed as a long-acting and reversible inhibitor of fatty acid amide hydrolase for the treatment of neuropathic pain, led to the death of one participant and hospitalization of five others due to intracranial hemorrhage in a Phase I clinical trial. Putative off-target activities of BIA 10-2474 have been suggested to be major contributing factors to the observed neurotoxicity in humans, motivating our study’s proteome-wide screening approach to investigate its polypharmacology. Accordingly, we performed an in silico screen against 80,923 protein structures reported in the Protein Data Bank. The resulting list of 284 unique human interactors was further refined using target-disease association analyses to a subset of proteins previously linked to neurological, intracranial, inflammatory, hemorrhagic or clotting processes and/or diseases. Eleven proteins were identified as potential targets of BIA 10-2474, and the two highest-scoring proteins, Factor VII and thrombin, both essential blood-clotting factors, were predicted to be inhibited by BIA 10-2474 and suggest a plausible mechanism of toxicity. Once this small molecule becomes commercially available, future studies will be conducted to evaluate the predicted inhibitory effect of BIA 10-2474 on blood clot formation specifically in the brain.
Recommended citation: Steven V. Molinski, Vijay M. Shahani, Stephen S. MacKinnon, Leonard D. Morayniss, Marcon Laforet, Geoffrey Woollard, Naheed Kurji, Cecilia G. Sanchez, Shoshana J. Wodak, Andreas Windemuth. (2017). "Computational proteome-wide screening predicts neurotoxic drug-protein interactome for the investigational analgesic BIA 10-2474." Biochemical and Biophysical Research Communications. 483(1), 502-508.
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Comprehensive mapping of cystic fibrosis mutations to CFTR protein identifies mutation clusters and molecular docking predicts corrector binding site
Published in Proteins, 2018
Cystic 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, and C18) is not well understood. In this study, exonic CFTR mutations and mutant allele frequencies described in 3 curated databases (ABCMdb, CFTR1, and CFTR2, comprising >130 000 data points) were mapped to 2 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. Accordingly, residue positions of 6 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. In addition, immunoblotting confirmed the approximate binding site of Class I correctors, demonstrating that these small molecules act via a similar mechanism in vitro, and in silico molecular docking generated binding poses for their complex with the cryo-electron microscopy structure to suggest the putative corrector binding site is a multi-domain pocket near residues F374-L375. These 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.
Recommended citation: Steven V. Molinski, Vijay M. Shahani, Adithya S. Subramanian, Stephen S. MacKinnon, Geoffrey Woollard, Marcon Laforet, Onofrio Laselva, Leonard D. Morayniss, Christine E. Bear, Andreas Windemuth. (2018). "Comprehensive mapping of cystic fibrosis mutations to CFTR protein identifies mutation clusters and molecular docking predicts corrector binding site." Proteins. 86(8):833-843.
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Structural Characterization of Endogenous Tuberous Sclerosis Protein Complex Revealed Potential Polymeric Assembly
Published in Biochemistry, 2021
Tuberous sclerosis protein complex (pTSC) nucleates a proteinaceous signaling hub that integrates information about the internal and external energy status of the cell in the regulation of growth and energy consumption. Biochemical and cryo-electron microscopy studies of recombinant pTSC have revealed its structure and stoichiometry and hinted at the possibility that the complex may 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 a ≤1 μm length in linear and weblike organizations prepared from pig brain. Immunogold labeling experiments demonstrate linear aggregates of pTSC purified from mammalian brains. These observations suggest polymerization of endogenous pTSC into filamentous superstructures.
Recommended citation: David L. Dai, M. Naimul Hasan, Geoffrey WoollardY, azan M. Abbas, Stephanie A. Bueler, Jean-Philippe Julien, John L. Rubinstein, Mohammad T. Mazhab-Jafari. (2021). "Structural Characterization of Endogenous Tuberous Sclerosis Protein Complex Revealed Potential Polymeric Assembly." Biochemistry. 60(23), 1808-1821.
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Application of transport-based metric for continuous interpolation between cryo-EM density maps
Published in AIMS Mathematics, 2021
Cryogenic electron microscopy (cryo-EM) has become widely used for the past few years in structural biology, to collect single images of macromolecules “frozen in time”. As this technique facilitates the identification of multiple conformational states adopted by the same molecule, a direct product of it is a set of 3D volumes, also called EM maps. To gain more insights on the possible mechanisms that govern transitions between different states, and hence the mode of action of a molecule, we recently introduced a bioinformatic tool that interpolates and generates morphing trajectories joining two given EM maps. This tool is based on recent advances made in optimal transport, that allow efficient evaluation of Wasserstein barycenters of 3D shapes. As the overall performance of the method depends on various key parameters, including the sensitivity of the regularization parameter, we performed various numerical experiments to demonstrate how MorphOT can be applied in different contexts and settings. Finally, we discuss current limitations and further potential connections between other optimal transport theories and the conformational heterogeneity problem inherent with cryo-EM data.
Recommended citation: Arthur Ecoffet, Geoffrey Woollard, Artem Kushner, Frédéric Poitevin, Khanh Dao Duc. (2021). "Application of transport-based metric for continuous interpolation between cryo-EM density maps." AIMS Mathematics. 7(1), 986–999.
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Physics aware inference for the cryo-EM inverse problem: anisotropic network model heterogeneity, global pose and microscope defocus
Published in NeurIPS 2022 Workshop. Machine Learning for Structural Biolog, 2022
We propose a parametric forward model for single particle cryo-electron microscopy (cryo-EM), and employ stochastic variational inference to infer posterior distributions of the physically interpretable latent variables. Our cryo-EM forward model accounts for the biomolecular configuration (via spatial coordinates of pseudo-atoms, in contrast with traditional voxelized representations) the global pose, the effect of the microscope (contrast transfer function’s defocus parameter). To account for conformational heterogeneity, we use the anisotropic network model (ANM). We perform experiments on synthetic data and show that the posterior of the scalar component along the lowest ANM mode and the angle of 2D in-plane pose can be jointly inferred with deep neural networks. We also perform Fourier frequency marching in the simulation and likelihood during training of the neural networks, as an annealing step.
Recommended citation: Geoffrey Woollard, Shayan Shekarforoush, Frank Wood, Marcus Brubaker, Khanh Dao Duc. (2022). "Exploring Simulators for Particle Picking in Cryo-Electron Tomography." 36th Conference on Neural Information Processing Systems (NeurIPS 2022) Workshop: Machine Learning for Structural Biology Workshop.
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AlignOT: An Optimal Transport Based Algorithm for Fast 3D Alignment With Applications to Cryogenic Electron Microscopy Density Maps
Published in IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2023
Aligning electron density maps from Cryogenic electron microscopy (cryo-EM) is a first key step for studying multiple conformations of a biomolecule. As this step remains costly and challenging, with standard alignment tools being potentially stuck in local minima, we propose here a new procedure, called AlignOT, which relies on the use of computational optimal transport (OT) to align EM maps in 3D space. By embedding a fast estimation of OT maps within a stochastic gradient descent algorithm, our method searches for a rotation that minimizes the Wasserstein distance between two maps, represented as point clouds. We quantify the impact of various parameters on the precision and accuracy of the alignment, and show that AlignOTcan outperform the standard local alignment methods, with an increased range of rotation angles leading to proper alignment. We further benchmark AlignOT on various pairs of experimental maps, which account for different types of conformational heterogeneities and geometric properties. As our experiments show good performance, we anticipate that our method can be broadly applied to align 3D EM maps.
Recommended citation: Aryan Tajmir Riahi, Geoffrey Woollard, Frédéric Poitevin, Anne Condon, Khanh Dao Duc. (2023). "AlignOT: An optimal transport based algorithm for fast 3D alignment with applications to cryogenic electron microscopy density maps." IEEE/ACM Trans. Comput. Biol. Bioinform. 20(6), 3842–3850.
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CryoBench: Diverse and challenging datasets for the heterogeneity problem in cryo-EM
Published in Advances in Neural Information Processing Systems, 2024
Cryo-electron microscopy (cryo-EM) is a powerful technique for determining high-resolution 3D biomolecular structures from imaging data. As this technique can capture dynamic biomolecular complexes, 3D reconstruction methods are increasingly being developed to resolve this intrinsic structural heterogeneity. However, the absence of standardized benchmarks with ground truth structures and validation metrics limits the advancement of the field. Here, we propose CryoBench, a suite of datasets, metrics, and performance benchmarks for heterogeneous reconstruction in cryo-EM. We propose five datasets representing different sources of heterogeneity and degrees of difficulty. These include conformational heterogeneity generated from simple motions and random configurations of antibody complexes and from tens of thousands of structures sampled from a molecular dynamics simulation. We also design datasets containing compositional heterogeneity from mixtures of ribosome assembly states and 100 common complexes present in cells. We then perform a comprehensive analysis of state-of-the-art heterogeneous reconstruction tools including neural and non-neural methods and their sensitivity to noise, and propose new metrics for quantitative comparison of methods. We hope that this benchmark will be a foundational resource for analyzing existing methods and new algorithmic development in both the cryo-EM and machine learning communities.
Recommended citation: Minkyu Jeon, Rishwanth Raghu, Miro Astore, Geoffrey Woollard, Ryan Feathers, Alkin Kaz, Sonya M. Hanson, Pilar Cossio, Ellen D. Zhong. (2024). "CryoBench: Diverse and challenging datasets for the heterogeneity problem in cryo-EM." Advances in Neural Information Processing Systems. 37, 89468--89512.
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Recent Advances, Applications and Open Challenges in Machine Learning for Health: Reflections from Research Roundtables at ML4H 2024 Symposium
Published in arXiv, 2025
The fourth Machine Learning for Health (ML4H) symposium was held in person on December 15th and 16th, 2024, in the traditional, ancestral, and unceded territories of the Musqueam, Squamish, and Tsleil-Waututh Nations in Vancouver, British Columbia, Canada. The symposium included research roundtable sessions to foster discussions between participants and senior researchers on timely and relevant topics for the ML4H community. The organization of the research roundtables at the conference involved 13 senior and 27 junior chairs across 13 tables. Each roundtable session included an invited senior chair (with substantial experience in the field), junior chairs (responsible for facilitating the discussion), and attendees from diverse backgrounds with an interest in the session’s topic.
Recommended citation: Amin Adibi, Xu Cao, Zongliang Ji, Jivat Neet Kaur, Winston Chen, Elizabeth Healey, Brighton Nuwagira, Wenqian Ye, Geoffrey Woollard, Maxwell A Xu, Hejie Cui, Johnny Xi, Trenton Chang, Vasiliki Bikia, Nicole Zhang, Ayush Noori, Yuan Xia, Md. Belal Hossain, Hanna A. Frank, Alina Peluso, Yuan Pu, Shannon Zejiang Shen, John Wu, Adibvafa Fallahpour, Sazan Mahbub, Ross Duncan, Yuwei Zhang, Yurui Cao, Zuheng Xu, Michael Craig, Rahul G. Krishnan, Rahmatollah Beheshti, James M. Rehg, Mohammad Ehsanul Karim, Megan Coffee, Leo Anthony Celi, Jason Alan Fries, Mohsen Sadatsafavi, Dennis Shung, Shannon McWeeney, Jessica Dafflon, Sarah Jabbour. (2025). "Recent Advances, Applications and Open Challenges in Machine Learning for Health: Reflections from Research Roundtables at ML4H 2024 Symposium. " arXiv.
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InstaMap: instant-NGP for cryo-EM density maps
Published in Acta Crystallographica Section D, 2025
Despite the parallels between problems in computer vision and cryo-electron microscopy (cryo-EM), many state-of-the-art approaches from computer vision have yet to be adapted for cryo-EM. Within the computer-vision research community, implicits such as neural radiance fields (NeRFs) have enabled the detailed reconstruction of 3D objects from few images at different camera-viewing angles. While other neural implicits, specifically density fields, have been used to map conformational heterogeneity from noisy cryo-EM projection images, most approaches represent volume with an implicit function in Fourier space, which has disadvantages compared with solving the problem in real space, complicating, for instance, masking, constraining physics or geometry, and assessing local resolution. In this work, we build on a recent development in neural implicits, a multi-resolution hash-encoding framework called instant-NGP, that we use to represent the scalar volume directly in real space and apply it to the cryo-EM density-map reconstruction problem (InstaMap). We demonstrate that for both synthetic and real data, InstaMap for homogeneous reconstruction achieves higher resolution at shorter training stages than five other real-spaced representations. We propose a solution to noise overfitting, demonstrate that InstaMap is both lightweight and fast to train, implement masking from a user-provided input mask and extend it to molecular-shape heterogeneity via bending space using a per-image vector field.
Recommended citation: Geoffrey Woollard, Wenda Zhou, Erik H. Thiede, Chen Lin, Nikolaus Grigorieff, Pilar Cossio, Khanh Dao Duc, Sonya M. Hanson. (2025). "InstaMap: instant-NGP for cryo-EM density maps." Acta Crystallographica Section D Structural Biology. 81(4), 147–169.
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The Inaugural Flatiron Institute Cryo-EM Conformational Heterogeneity Challenge.
Published in bioRxiv, 2025
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.
Recommended citation: Miro A. Astore, Geoffrey Woollard, David Silva-Sanchez, Wenda Zhou, Mykhailo Kopylov, Khanh Dao Duc, Roy R. Lederman, Yilai Li, Yi Zhou, Jing Yuan, Fei Ye, Quanquan Gu, Remi Vuillemot, Slavica Jonic, Lan Dang, Steven J. Ludtke, Hannah Bridges, Serena Liu, Michael McLean, Valentin Peretroukhin, Johannes Schwab, Eduardo R. Cruz-Chu, Peter Schwander, Marc A. Gilles, Amit Singer, David Herreros, Jose Maria Carazo, Carlos Oscar S. Sorzano, J. Ryan Feathers, Ellen D. Zhong, Nikolaus Grigorieff, Pilar Cossio, Sonya M. Hanson. (2025). "The Inaugural Flatiron Institute Cryo-EM Conformational Heterogeneity Challenge." bioRxiv.
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The 2025 Community-Wide Assessment of Cryo-EM Heterogeneous Reconstruction Algorithms (CAHRA) Challenge
Published in , 2025
A community-wide data processing challenge for cryo-electron microscopy.
Recommended citation: Tom Burnley, Pilar Cossio, Ryan Feathers, Joel Greer, Sonya Hanson, Robert Heeter, Geoffrey Woollard, Ellen Zhong. (2025). "The 2025 Community-Wide Assessment of Cryo-EM Heterogeneous Reconstruction Algorithms (CAHRA) Challenge." https://heterogeneity.notion.site/challenge.
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Exploring Simulators for Particle Picking in Cryo-Electron Tomography
Published in NeurIPS 2025 Workshop. Imageomics: Discovering Biological Knowledge from Images Using AI, 2025
To understand how proteins function, we need to know the conformations that they adopt and with what they interact in their native cellular environment. Cryo-electron tomography (cryo-ET) offers a powerful tool by enabling in situ imaging of proteins. But high noise levels and the need for expertise in particle identification limit its scalability. In this study, we present a machine learning framework for automated recognition and localization of particles in cryo-ET data. We treat particle picking as an object recognition task and employ a U-Net-based architecture for multi-class segmentation. To overcome the scarcity of annotated data, we train our model on synthetic tomograms generated by a simulator that incorporates empirical noise from publicly available cryo-ET datasets. Our results show that training on a mixed dataset containing both synthetic and empirical backgrounds provides the most effective particle-picking performance, enhancing the model’s robustness to different background types. Furthermore, we demonstrate that training exclusively on simulated particles enables the model to reliably distinguish particles from background in real tomograms, highlighting the potential of simulation-based training strategies in cryo-ET.
Recommended citation: Serena M. Arghittu, Lars Dingeldein, Geoffrey Woollard, LingLi Kong, Magnus Petersen, Sonya Hanson, Roberto Covino, Pilar Cossio. (2025). "Exploring Simulators for Particle Picking in Cryo-Electron Tomography." 39th Conference on Neural Information Processing Systems (NeurIPS 2025) Workshop: The 3rd Workshop on Imageomics: Discovering Biological Knowledge from Images Using AI.
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cryoJAX: A Cryo-electron Microscopy Image Simulation Library In JAX
Published in bioRxiv, 2025
The 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.
Recommended citation: Michael J. O'Brien, David Silva-Sanchez, Geoffrey Woollard, Kwanghwi Je, Sonya M. Hanson, Daniel J. Needleman, Pilar Cossio, Erik Henning Thiede, Miro A. Astore. (2025). "cryoJAX: A Cryo-electron Microscopy Image Simulation Library In JAX." bioRxiv.
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Improving Cryo-EM Optimization Robustness with an Optimal Transport Loss Function for Noisy Images
Published in bioRxiv, 2025
The 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.
Recommended citation: Geoffrey Woollard, David Herreros, Minhuan Li, Pilar Cossio, Khanh Dao Duc. (2025). "Improving Cryo-EM Optimization Robustness with an Optimal Transport Loss Function for Noisy Images." bioRxiv.
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talks
Talk 1 on Relevant Topic in Your Field
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teaching
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Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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