Biology subjects

Jonic, S.

Publications and source records attributed to Jonic, S..

6 recordsLinked to original sources

bioRxiv2026

Learning Mappings from Cryo-EM Images to Atomic Coordinates via Latent Representations

Single-particle cryo-electron microscopy (cryo-EM) aims to determine three-dimensional (3D) structures of biomolecular complexes from noisy two-dimensional (2D) projection images acquired at unknown orientations. The presence of pose uncertainty and continuous conformational heterogeneity makes high-resolution reconstruction challenging. Here, we investigate, in a controlled synthetic setting, whether supervised learning can map noisy cryo-EM single-particle images to atomic coordinates without pose recovery or 2D projection calculations. We propose a convolutional auto-encoder to compress particle images into their corresponding latent representations, followed by a regression network to predict 3D atomic coordinates from these image latents. We show the performance of this approach using synthetic datasets of pairs of particle images and conformational models of adenylate kinase and nucleosome core particles, generated using a realistic cryo-EM forward model based on Normal Mode Analysis for simulating dynamics. Inference yielded mean RMSDs of 2.11 [A] for all-atom models of adenylate kinase (1,656 atoms) and 0.80 [A] for the coarse-grain models of nucleosome (1,041 C-P atoms). These results indicate that compact image latents preserve pose and conformation related information sufficiently well to support atomic coordinate regression. This provides a quantitative proof-of-principle for coupling image and structure spaces toward fast estimation of conformational variability in cryo-EM.

bioinformatics↗

bioRxiv2026

Diffusion Probabilistic Models for Missing-Wedge Correction in Cryo-Electron Tomography

Interpretation of 3D cryo-electron tomography (cryo-ET) reconstructions (tomograms) is hampered by the so-called missing-wedge (MW) distortions, which arise because tilt image series used for the reconstructions are acquired in a limited angular range. While many deep-learning approaches address the correction of the MW artifacts on the level of tomograms (3D volumes), the correction at the level of 2D tilt images (generation of unacquired images) remains underexplored. We propose MW-RaMViD, a 2D tilt-image generation method for MW correction, based on Random-Mask Video Diffusion (RaMViD) method for prediction of frames in natural videos. To adapt RaMViD for cryo-ET, we add MRC image-format support, floating-point pixel intensity representation, and a controlled inference protocol enabling both one-run and progressive MW completion (generating a small number of missing tilts per step using a sliding window). We evaluate the method on a synthetic noisy tilt-series dataset and study the effects of MW completion step size and conditioning sequence length. Results show that smaller step sizes and larger conditioning windows reduce error accumulation at higher tilt angles and improve reconstruction fidelity, which was measured by Root Mean Square Error on the image level and by Fourier Shell Correlation on the tomogram level.

bioinformatics↗

bioRxiv2026

Towards inferring atomic scale conformation landscape of biomolecules from cryo-electron tomography data

Understanding continuous conformational variability of biomolecular complexes at atomic resolution is essential for linking structure to function, but remains challenging for cryo-electron tomography (cryo-ET) data due to high noise and missing-wedge (MW) artifacts. Physics-based methods, such as MDTOMO (based on classical molecular dynamics simulations of a given atomic structure to flexibly fit subtomograms), provide accurate estimation of atomic coordinates from subtomograms, but their computational cost limits large-scale applications. We present DeepMDTOMO, a supervised deep learning (DL) framework that uses a given set of pairs of atomic coordinates and subtomograms to learn their relationships in order to predict unknown atomic coordinates from a previously unseen set of subtomograms. The proposed regressor encoder-decoder architecture combines 3D convolutional extraction of features from subtomograms with a multilayer perceptron to predict Cartesian all-atom coordinates. Experiments on synthetic datasets show that DeepMDTOMO achieves low errors of coordinate prediction in presence of noise, MW, and large continuous conformational variability. Additionally, fine-tuning to a new motion demonstrates that learned representations capture general structure-density relationships rather than specific patterns. The results presented are encouraging and motivate future studies on speeding up subtomogram flexible-fitting methods with DL for fast atomic-scale conformational landscape determination from cryo-ET data.

bioinformatics↗

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↗

bioRxiv2023

MDTOMO: Continuous conformational variability analysis in cryo electron subtomogram data using flexible fitting based on Molecular Dynamics simulations

Cryo electron tomography (cryo-ET) allows observing macromolecular complexes in their native environment. The common routine of subtomogram averaging (STA) allows obtaining the three-dimensional (3D) structure of abundant macromolecular complexes, and can be coupled with discrete classification to reveal conformational heterogeneity of the sample. However, the number of complexes extracted from cryo-ET data is usually small, which restricts the discrete-classification results to a small number of enough populated states and, thus, results in a largely incomplete conformational landscape. Alternative approaches are currently being investigated to explore the continuity of the conformational landscapes that in situ cryo-ET studies could provide. In this article, we present MDTOMO, a method for analyzing continuous conformational variability in cryo-ET subtomograms based on Molecular Dynamics (MD) simulations. MDTOMO allows obtaining an atomic-scale model of conformational variability and the corresponding free-energy landscape, from a given set of cryo-ET subtomograms. The article presents the performance of MDTOMO on a synthetic ABC exporter dataset and an in situ SARS-CoV-2 spike dataset. MDTOMO allows analyzing dynamic properties of molecular complexes to understand their biological functions, which could also be useful for structure-based drug discovery.

bioinformatics↗

bioRxiv2021

Methods for analyzing continuous conformational variability of biomolecules in cryo electron subtomograms: HEMNMA-3D vs. traditional classification

Cryogenic electron tomography (cryo-ET) allows studying biological macromolecular complexes in cells by three-dimensional (3D) data analysis. The complexes continuously change their shapes (conformations) to achieve biological functions. The shape heterogeneity in the samples imaged in the cryo electron microscope is a bottleneck for comprehending biological mechanisms and developing drugs. Low signal-to-noise ratio and spatial anisotropy (missing wedge artefacts) make cryo-ET data particularly challenging for resolving the shape variability. Other shape variability analysis techniques simplify the problem by considering discrete rather than continuous conformational changes of complexes. Recently, HEMNMA-3D was introduced for cryo-ET continuous shape variability analysis, based on elastic and rigid-body 3D registration between simulated shapes and cryo-ET data. The simulated motions are obtained by normal mode analysis of a high- or low-resolution 3D reference model of the complex under study. The rigid-body alignment is achieved via fast rotational matching with missing wedge compensation. HEMNMA-3D provides a visual insight into molecular dynamics by grouping and averaging subtomograms of similar shapes and by animating movies of registered motions. This article reviews the method and compares it with existing literature on a simulated dataset for nucleosome shape variability.

bioinformatics↗