Biology subjects

Zhong, E. D.

Publications and source records attributed to Zhong, E. D..

9 recordsLinked to original sources

bioRxiv2026

Agentic campaign control for high-throughput de novo binder design

Progress in artificial intelligence has produced a rapidly growing ecosystem of methods for de novo protein design. With access to many specialized and often complementary tools, how does one use them effectively, especially with a finite compute budget? Here, we introduce Target-adaptive Rescue-Explore-eXploit (T-REX), an agentic protein binder design framework that orchestrates multiple state-of-the-art protein generative models and structure evaluators. Over the course of a design campaign, T-REX leverages large language model (LLM) agents to reason over accumulating outcomes and decide whether to rescue promising candidates, explore alternative settings, or exploit productive routes, while a deterministic controller validates and schedules proposed actions.

bioengineering↗

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↗

bioRxiv2024

Cryo-ET reveals the in situ architecture of the polar tube invasion apparatus from microsporidian parasites

Microsporidia are divergent fungal pathogens that employ a harpoon-like apparatus called the polar tube (PT) to invade host cells. The PT architecture and its association with neighboring organelles remain poorly understood. Here, we use cryo-electron tomography to investigate the structural cell biology of the PT in dormant spores from the human-infecting microsporidian species, Encephalitozoon intestinalis. Segmentation and subtomogram averaging of the PT reveal at least four layers: two protein-based layers surrounded by a membrane, and filled with a dense core. Regularly spaced protein filaments form the structural skeleton of the PT. Combining cryo-electron tomography with cellular modeling, we propose a model for the 3-dimensional organization of the polaroplast, an organelle that is continuous with the membrane layer that envelops the PT. Our results reveal the ultrastructure of the microsporidian invasion apparatus in situ, laying the foundation for understanding infection mechanisms.

microbiology↗

bioRxiv2024

Revealing biomolecular structure and motion with neural ab initio cryo-EM reconstruction

Proteins and other biomolecules form dynamic macromolecular machines that are tightly orchestrated to move, bind, and perform chemistry. Cryo-electron microscopy (cryo-EM) and cryo-electron tomography (cryo-ET) can access the intrinsic heterogeneity of these complexes and are therefore key tools for understanding their function. However, 3D reconstruction of the collected imaging data presents a challenging computational problem, especially without any starting information, a setting termed ab initio reconstruction. Here, we introduce cryoDRGN-AI, a method leveraging an expressive neural representation and combining an exhaustive search strategy with gradient-based optimization to process challenging heterogeneous datasets. Using cryoDRGN-AI, we reveal new conformational states in large datasets, reconstruct previously unresolved motions from unfiltered datasets, and demonstrate ab initio reconstruction of biomolecular complexes from in situ data. With this expressive and scalable model for structure determination, we hope to unlock the full potential of cryo-EM and cryo-ET as a high-throughput tool for structural biology and discovery.

biophysics↗

bioRxiv2023

Conformational states of the microtubule nucleator, the γ-tubulin ring complex

Microtubules (MTs) perform essential functions in the cell, and it is critical that they are made at the correct cellular location and cell cycle stage. This nucleation process is catalyzed by the {gamma}-tubulin ring complex ({gamma}-TuRC), a cone-shaped protein complex composed of over 30 subunits. Despite recent insight into the structure of vertebrate {gamma}-TuRC, which shows that its diameter is wider than that of a MT, and that it exhibits little of the symmetry expected for an ideal MT template, the question of how {gamma}-TuRC achieves MT nucleation remains open. Here, we utilized single particle cryo-EM to identify two conformations of {gamma}-TuRC. The helix composed of 14 {gamma}-tubulins at the top of the {gamma}-TuRC cone undergoes substantial deformation, which is predominantly driven by bending of the hinge between the GRIP1 and GRIP2 domains of the {gamma}-tubulin complex proteins. However, surprisingly, this deformation does not remove the inherent asymmetry of {gamma}-TuRC. To further investigate the role of {gamma}-TuRC conformational change, we used cryo electron-tomography (cryo-ET) to obtain a 3D reconstruction of {gamma}-TuRC bound to a nucleated MT, providing insight into the post-nucleation state. Rigid-body fitting of our cryo-EM structures into this reconstruction suggests that the MT lattice is nucleated by spokes 2 through 14 of the {gamma}-tubulin helix, which entails spokes 13 and 14 becoming more structured than what is observed in apo {gamma}-TuRC. Together, our results allow us to propose a model for conformational changes in {gamma}-TuRC and how these may facilitate MT formation in a cell.

molecular biology↗

bioRxiv2023

Deep reconstructing generative networks for visualizing dynamic biomolecules inside cells

Advances in cryo-electron tomography (cryo-ET) have produced new opportunities to visualize the structures of dynamic macromolecular machinery in native cellular environments. Here, we describe a machine learning approach that can reconstruct the structural landscape and dynamics of biomolecular complexes present in cryo-ET subtomograms. This method, cryoDRGN-ET, learns a deep generative model of 3D density maps directly from subtomogram tilt series images and can capture states diverse in both composition and conformation. We use this approach to reconstruct the in situ translation dynamics of prokaryotic ribosomes, and we reveal the distribution of functional states during translation elongation populated by S. cerevisiae ribosomes inside cells.

biophysics↗

bioRxiv2022

Uncovering structural ensembles from single particle cryo-EM data using cryoDRGN

CryoDRGN is a machine learning system for heterogeneous cryo-EM reconstruction of proteins and protein complexes from single particle cryo-EM data. Central to this approach is a deep generative model for heterogeneous cryo-EM density maps, which we empirically find effectively models both discrete and continuous forms of structural variability. Once trained, cryoDRGN is capable of generating an arbitrary number of 3D density maps, and thus interpreting the resulting ensemble is a challenge. Here, we showcase interactive and automated processing approaches for analyzing cryoDRGN results. Specifically, we detail a step-by-step protocol for analysis of the assembling 50S ribosome dataset (Davis et al., EMPIAR-10076), including preparation of inputs, network training, and visualization of the resulting ensemble of density maps. Additionally, we describe and implement methods to comprehensively analyze and interpret the distribution of volumes with the assistance of an associated atomic model. This protocol is appropriate for structural biologists familiar with processing single particle cryo-EM datasets and with moderate experience programming in Python and navigating Jupyter notebooks. It requires 3-4 days to complete.

biochemistry↗

bioRxiv2020

CryoDRGN: Reconstruction of heterogeneous structures from cryo-electron micrographs using neural networks

Cryo-EM single-particle analysis has proven powerful in determining the structures of rigid macromolecules. However, many protein complexes are flexible and can change conformation and composition as a result of functionally-associated dynamics. Such dynamics are poorly captured by current analysis methods. Here, we present cryoDRGN, an algorithm that for the first time leverages the representation power of deep neural networks to efficiently reconstruct highly heterogeneous complexes and continuous trajectories of protein motion. We apply this tool to two synthetic and three publicly available cryo-EM datasets, and we show that cryoDRGN provides an interpretable representation of structural heterogeneity that can be used to identify discrete states as well as continuous conformational changes. This ability enables cryoDRGN to discover previously overlooked structural states and to visualize molecules in motion.

molecular biology↗

bioRxiv2020

Recording the age of RNA with deamination

Transcriptional programs implemented by cells often consist of complex temporal features, but current approaches to single-cell RNA sequencing only provide limited information about the dynamics of gene expression. Here, we present RNA timestamps, a method for inferring the age of individual RNAs in a sequencing-based readout by leveraging RNA editing. Timestamped RNAs include a RNA reporter motif that accumulates A to I edits over time, allowing the age of the RNA to be inferred with hour-scale accuracy. By combining observations of multiple timestamped RNAs driven by the same promoter, we are able to infer when in the past the promoter was active. We demonstrate that the system can infer the presence and timing of multiple past transcriptional events, with no prior knowledge. Finally, we apply this method to cluster single cells according to the times at which a particular transcriptional program was activated. RNA timestamps thus suggest a new approach for incorporating temporal information into RNA sequencing workflows.

synthetic biology↗