Biology subjects

Kimanius, D.

Publications and source records attributed to Kimanius, D..

15 recordsLinked to original sources

bioRxiv2026

Multiscale Physical Effects of CpG Methylation on DNA Mechanics, Nucleosome Wrapping, and Chromatin Condensates

DNA methylation at CpG dinucleotides is a central epigenetic modification linked to transcriptional repression and heterochromatin formation, yet its physical impact on chromatin organization remains incompletely understood. Here, we integrate single-molecule force spectroscopy, FRAP, and nanorheology to quantify how CpG methylation alters chromatin mechanics across scales. Optical tweezer-based measurements show that CpG methylation modestly increases DNA contour and persistence lengths, indicating a slightly extended and stiffer DNA helix. When assembled into nucleosomes, CpG methylation weakens both outer-and inner turn wrapping, reducing unwrapping forces and per-nucleosome unwrapping energy. On a higher-order assembly scale, although CpG methylated and unmethylated chromatin fibers display similar thresholds for salt-induced phase separation, methylated condensates exhibit markedly reduced internal mobility: FRAP reveals slower chromatin exchange, nanorheology shows increased viscoelasticity and longer relaxation times. Finally, CpG methylation enhances HP1-chromatin interactions, lowering the HP1 concentration required for condensation, despite no change in HP1 binding to naked DNA. Together, these results show that CpG methylation remodels chromatin mechanics by tuning DNA rigidity, nucleosome wrapping energetics, and condensate material properties, providing a multiscale physical framework for how DNA methylation contributes to heterochromatin.

biochemistry↗

bioRxiv2025

Lessons learned from a Kaggle challenge for particle picking in cryo-electron tomography

The difficulty of particle picking in cryo-electron tomography remains a barrier to routine in situ structure determination. Machine learning is well-suited to overcome this bottleneck with efficient algorithms that generalize across molecular species. To spur new algorithm development, we held a three-month Kaggle challenge that tasked contestants with annotating five molecular species across hundreds of experimental tomograms. This competition successfully engaged >1000 participants from diverse fields and delivered particle pickers that outperformed existing state-of-the-art. Systematic comparisons of the contestants submissions revealed the tolerance of subtomogram averaging to moderate but not severe over-picking and underscored the need for more robust measures of annotation quality. The winning models also highlighted the importance of data augmentation to overcome limited training data. All competition tomograms along with the ground truth and winning teams annotations have been released on the CryoET Data Portal as a resource to benchmark current and future tools for particle picking.

cell biology↗

bioRxiv2025

AreTomoLive: Automated reconstruction of comprehensively-corrected and denoised cryo-electron tomograms in real-time and at high throughput

A high throughput processing pipeline that performs comprehensive corrections is needed to realize the full potential of cryo-electron tomography and subtomogram averaging. The fields fragmented software landscape remains a significant hurdle to this end. Here we present AreTomoLive, an automated real-time pipeline composed of two GPU-accelerated packages. The first, AreTomo3, streamlines tomographic alignment and reconstruction, with new features to fully account for sample geometry, locally correct the contrast transfer function, and curate data for downstream tasks. The second package, DenoisET, is a new implementation of the machine learning algorithm Noise2Noise and runs in parallel with AreTomo3 to perform contrast enhancement. To reduce barriers to routine use, AreTomoLive prioritizes automation: AreTomo3 autonomously pauses and reactivates processing depending on the status of data collection, while DenoisET algorithmically determines when to transition from training to inference. AreTomoLive endeavors to advance cryoET for in situ structural analysis with its comprehensive corrections and full automation.

cell biology↗

bioRxiv2024

Annotating CryoET Volumes: A Machine Learning Challenge

Cryo-electron tomography (cryoET) has emerged as a powerful structural biology tool for understanding protein complexes in their native cellular environments. Presently, 3D volumes of cellular environments can be acquired in the thousands in a few days where each volume provides a rich and complex cellular landscape. Despite numerous innovations, localizing and identifying the vast majority of protein species in these volumes remains prohibitively difficult. Machine learning based methods provide an opportunity to automate the process of labeling and annotating cryoET volumes. Due to current bottlenecks in the annotation process, and a lack of large standardized datasets, training datasets for machine learning algorithms have been scarce. Here, we present a defined "phantom" sample, along with "ground truth" annotations, that will be the basis of a machine learning challenge to bring cryoET and ML experts together and spur creativity to address this annotation problem. We have also set up a cryoET data portal that provides additional diverse sets of annotated 3D volumes from cryoET experts across the world for the machine learning challenge.

cell biology↗

bioRxiv2024

Mechanism of DNA entrapment by the MukBEF SMC complex and its inhibition by a viral DNA mimic

Ring-like structural maintenance of chromosomes (SMC) complexes are crucial for genome organization and operate through mechanisms of DNA entrapment and loop extrusion. Here, we explore the DNA loading process of the bacterial SMC complex MukBEF. Using electron cryomicroscopy (cryo-EM), we demonstrate that ATP binding opens one of MukBEFs three potential DNA entry gates, exposing a DNA capture site that positions DNA at the open neck gate. We discover that the gp5.9 protein of bacteriophage T7 blocks this capture site by DNA mimicry, thereby preventing DNA loading and inactivating MukBEF. We propose a comprehensive and unidirectional loading mechanism in which DNA is first captured at the complexs periphery and then ingested through the DNA entry gate, powered by a single cycle of ATP hydrolysis. These findings illuminate a fundamental aspect of how ubiquitous DNA organizers are primed for genome maintenance and demonstrate how this process can be disrupted by viruses. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=193 HEIGHT=200 SRC="FIGDIR/small/616235v1_ufig1.gif" ALT="Figure 1"> View larger version (68K): org.highwire.dtl.DTLVardef@165a949org.highwire.dtl.DTLVardef@14d1302org.highwire.dtl.DTLVardef@11afb6forg.highwire.dtl.DTLVardef@71694b_HPS_FORMAT_FIGEXP M_FIG C_FIG

molecular biology↗

bioRxiv2024

An image processing pipeline for electron cryo-tomography in RELION-5

Electron tomography of frozen, hydrated samples allows structure determination of macromolecular complexes that are embedded in complex environments. Provided that the target complexes may be localised in noisy, three-dimensional tomographic reconstructions, averaging images of multiple instances of these molecules can lead to structures with sufficient resolution for de novo atomic modelling. Although many research groups have contributed image processing tools for these tasks, a lack of standardisation and inter-operability represents a barrier for newcomers to the field. Here, we present an image processing pipeline for electron tomography data in RELION-5, with functionality ranging from the import of unprocessed movies to the automated building of atomic models in the final maps. Our explicit definition of metadata items that describe the steps of our pipeline has been designed for inter-operability with other software tools and provides a framework for further standardisation.

biophysics↗

bioRxiv2023

Data-driven regularisation lowers the size barrier of cryo-EM structure determination

Macromolecular structure determination by electron cryo-microscopy (cryo-EM) is limited by the alignment of noisy images of individual particles. Because smaller particles have weaker signals, alignment errors impose size limitations on its applicability. Here, we explore how image alignment is improved by the application of deep-learning to exploit prior knowledge about biological macromolecular structures that would otherwise be difficult to express mathematically. We train a denoising convolutional neural network on pairs of half-set reconstructions from the electron microscopy data bank (EMDB) and use this denoiser as an alternative to a commonly used smoothness prior. We demonstrate that this approach, which we call Blush regularisation, yields better reconstructions than existing algorithms, in particular for data with low signal-to-noise ratios. The reconstruction of a protein-nucleic acid complex with a molecular weight of 40 kDa, which was previously intractable, illustrates that regularisation through denoising will expand the applicability of cryo-EM structure determination for a wide range of biological macromolecules.

biophysics↗

bioRxiv2023

DynaMight: estimating molecular motions with improved reconstruction from cryo-EM images

How to deal with continuously flexing molecules is one of the biggest outstanding challenges in single-particle analysis of proteins from cryo-EM images. Here, we present DynaMight, a new software tool that estimates a continuous space of conformations in a cryo-EM data set by learning 3D deformations of a Gaussian pseudo-atomic model of a consensus structure for every particle image. Inversion of the learnt deformations is then used to obtain an improved reconstruction of the consensus structure. We illustrate the performance of DynaMight for several experimental cryo-EM data sets. We also show how error estimates on the deformations may be obtained by independently training two variational autoencoders (VAEs) on half sets of the cryo-EM data, and how regularisation of the 3D deformations through the use of atomic models may lead to important artefacts due to model bias. DynaMight is distributed as free, open-source software, as part of RELION-5.

biophysics↗

bioRxiv2023

Disease-specific tau filaments assemble via polymorphic intermediates

Intermediate species in the assembly of amyloid filaments are believed to play a central role in neurodegenerative diseases and may constitute important targets for therapeutic intervention. However, structural information about intermediate species has been scarce and the molecular mechanisms by which amyloids assemble remain largely unknown. Here, we use time-resolved electron cryo-microscopy (cryo-EM) to study the in vitro assembly of recombinant truncated tau (amino acids 297-391) into paired helical filaments of Alzheimers disease or into filaments of chronic traumatic encephalopathy. We report the formation of a shared first intermediate amyloid (FIA), with an ordered core comprising amino acids 302-316. Nuclear magnetic resonance indicates that the same amino acids adopt rigid, {beta}-strand-like conformations in monomeric tau. At later time points, the FIAs disappear and we observe many different intermediate amyloid filaments, with structures that depend on the reaction conditions. At the end of both reactions, most intermediate amyloids disappear and filaments with the same ordered cores as those from human brains remain. Our results provide structural insights into the processes of primary and secondary nucleation of amyloid assembly, with implications for the design of novel therapies.

biophysics↗

bioRxiv2023

Automated model building and protein identification in cryo-EM maps

Interpreting electron cryo-microscopy (cryo-EM) maps with atomic models requires high levels of expertise and labour-intensive manual intervention. We present ModelAngelo, a machine-learning approach for automated atomic model building in cryo-EM maps. By combining information from the cryo-EM map with information from protein sequence and structure in a single graph neural network, ModelAngelo builds atomic models for proteins that are of similar quality as those generated by human experts. For nucleotides, ModelAngelo builds backbones with similar accuracy as humans. By using its predicted amino acid probabilities for each residue in hidden Markov model sequence searches, ModelAngelo outperforms human experts in the identification of proteins with unknown sequences. ModelAngelo will thus remove bottlenecks and increase objectivity in cryo-EM structure determination.

molecular biology↗

bioRxiv2021

New tools for automated cryo-EM single-particle analysis in RELION-4.0

1We describe new tools for the processing of electron cryo-microscopy (cryo-EM) images in the fourth major release of the RELION software. In particular, we introduce VDAM, a Variable-metric gradient Descent algorithm with Adaptive Moments estimation, for image refinement; a convolutional neural network for unsupervised selection of 2D classes; and a flexible framework for the design and execution of multiple jobs in pre-defined workflows. In addition, we present a stand-alone utility called MDCatch that links the execution of jobs within this framework with metadata gathering during microscope data acquisition. The new tools are aimed at providing fast and robust procedures for unsupervised cryo-EM structure determination, with potential applications for on-the-fly processing and the development of flexible, high-throughput structure determination pipelines. We illustrate their potential on twelve publicly available cryo-EM data sets.

biophysics↗

bioRxiv2020

Structural basis for the inhibition of the SARS-CoV-2 RNA-dependent RNA polymerase by favipiravir-RTP

The RNA polymerase inhibitor, favipiravir, is currently in clinical trials as a treatment for infection with SARS-CoV-2, despite limited information about the molecular basis for its activity. Here we report the structure of favipiravir ribonucleoside triphosphate (favipiravir-RTP) in complex with the SARS-CoV-2 RNA-dependent RNA polymerase (RdRp) bound to a template:primer RNA duplex, determined by electron cryomicroscopy (cryoEM) to a resolution of 2.5 [A]. The structure shows clear evidence for the inhibitor at the catalytic site of the enzyme, and resolves the conformation of key side chains and ions surrounding the binding pocket. Polymerase activity assays indicate that the inhibitor is weakly incorporated into the RNA primer strand, and suppresses RNA replication in the presence of natural nucleotides. The structure reveals an unusual, non-productive binding mode of favipiravir-RTP at the catalytic site of SARS-CoV-2 RdRp which explains its low rate of incorporation into the RNA primer strand. Together, these findings inform current and future efforts to develop polymerase inhibitors for SARS coronaviruses.

molecular biology↗

bioRxiv2020

Exploiting prior knowledge about biological macromolecules in cryo-EM structure determination

Three-dimensional reconstruction of the electron scattering potential of biological macromolecules from electron cryo-microscopy (cryo-EM) projection images is an ill-posed problem. The most popular cryo-EM software solutions to date rely on a regularisation approach that is based on the prior assumption that the scattering potential varies smoothly over three-dimensional space. Although this approach has been hugely successful in recent years, the amount of prior knowledge it exploits compares unfavourably to the knowledge about biological structures that has been accumulated over decades of research in structural biology. Here, we present a regularisation framework for cryo-EM structure determination that exploits prior knowledge about biological structures through a convolutional neural network that is trained on known macromolecular structures. We insert this neural network into the iterative cryo-EM structure determination process through an approach that is inspired by Regularisation by Denoising. We show that the new regularisation approach yields better reconstructions than the current state-of-the-art for simulated data and discuss options to extend this work for application to experimental cryo-EM data.

molecular biology↗

bioRxiv2018

RELION-3: new tools for automated high-resolution cryo-EM structure determination

Here, we describe the third major release of relion. CPU-based vector acceleration has been added in addition to GPU support, which provides flexibility in use of resources and avoids memory limitations. Reference-free autopicking with Laplacian-of-Gaussian filtering and execution of jobs from python allows non-interactive processing during acquisition, including 2D-classification, de novo model generation and 3D-classification. Perparticle refinement of CTF parameters and correction of estimated beam tilt provides higher-resolution reconstructions when particles are at different heights in the ice, and/or coma-free alignment has not been optimal. Ewald sphere curvature correction improves resolution for large particles. We illustrate these developments with publicly available data sets: together with a Bayesian approach to beam-induced motion correction it leads to resolution improvements of 0.2-0.7 [A] compared to previous relion versions.

biophysics↗

bioRxiv2018

Characterisation of molecular motions in cryo-EM single-particle data by multi-body refinement in RELION

Macromolecular complexes that exhibit continuous forms of structural flexibility pose a challenge for many existing tools in cryo-EM single-particle analysis. We describe a new tool, called multi-body refinement, which models flexible complexes as a user-defined number of rigid bodies that move independently from each other. Using separate focused refinements with iteratively improved partial signal subtraction, the new tool generates improved reconstructions for each of the defined bodies in a fully automated manner. Moreover, using principal component analysis on the relative orientations of the bodies over all particles in the data set, we generate movies that describe the most important motions in the data. Our results on two test cases, a cytoplasmic ribosome from Plasmodium falciparum, and the spliceosomal B-complex from yeast, illustrate how multi-body refinement can be useful to gain unique insights into the structure and dynamics of large and flexible macromolecular complexes.\n\nPlease note that this bioRxiv submission is ahead of the availability of the multi-body software in relion-3.0. We take great care in distributing stable software, but this does take time. We will announce the (beta-)release of relion-3.0 through the ccp-em mailing list (https://www.jiscmail.ac.uk/CCPEM) and on twitter (@SjorsScheres).

biophysics↗