Biology subjects

Stabrin, M.

Publications and source records attributed to Stabrin, M..

6 recordsLinked to original sources

bioRxiv2023

In situ structures from relaxed cardiac myofibrils reveal the organization of the muscle thick filament

The thick filament is a key component of sarcomeres, the basic force-generating and load-bearing unit of striated muscle1. Mutations in thick filament proteins are associated with familial hypertrophic cardiomyopathy and other heart and muscle diseases2, 3. Despite this central importance for sarcomere force generation, it remains unclear how thick filaments are structurally organized and how its components interact with each other and with thin filaments to enable highly regulated muscle contraction. Here, we present the molecular architecture of native cardiac sarcomeres in the relaxed state, determined by electron cryo-tomography. Our reconstruction of the thick filament reveals the three-dimensional organization of myosin heads and tails, myosin-binding protein C (MyBP-C) and titin, elucidating the structural basis for their interaction during muscle contraction. The arrangement of myosin heads is variable depending on their position along the filament, suggesting that they have different capacities in terms of strain susceptibility and activation. Myosin tails exhibit a distinct arrangement and pattern of interactions. These are likely orchestrated by three alpha and three beta titin chains that are arranged like a spring, suggesting the existence of specialized roles of thick filament segments in length-dependent activation and contraction. Surprisingly, while the three titin alpha chains run along the entire length of the thick filament, titin beta does not. The structure also demonstrates that the C-terminal region of MyBP-C binds myosin tails and unexpectedly also directly interacts with the myosin heads, suggesting a previously undescribed direct role in the preservation of the myosin OFF state. Furthermore, we visualize how MyBP-C forms links between thin and thick filaments. These findings establish a robust groundwork for forthcoming research endeavors aiming to explore muscle disorders that involve sarcomeric structural components.

biochemistry↗

bioRxiv2022

TomoTwin: Generalized 3D Localization of Macromolecules in Cryo-electron Tomograms with Structural Data Mining

Cryoelectron tomography enables the visualization of cellular environments in extreme detail through the lens of a benign observer; what remains lacking however are tools to analyze the full amount of information contained within these densely packed volumes. Detailed analysis of macromolecules through subtomogram averaging requires particles to first be localized within the tomogram volume, a task complicated by several factors including a low signal to noise ratio and crowding of the cellular space. Available methods for this task suffer either from being error prone or requiring manual annotation of training data. To assist in this crucial particle picking step, we present TomoTwin: a robust, first in class general picking model for cryo-electron tomograms based on deep metric learning. By embedding tomograms in an information-rich, high-dimensional space which separates macromolecules according to their 3-dimensional structure, TomoTwin allows users to identify proteins in tomograms de novo without manually creating training data or retraining the network each time a new protein is to be located. TomoTwin is open source and available at https://github.com/MPI-Dortmund/tomotwin-cryoet.

biophysics↗

bioRxiv2020

Structural basis of TRPC4 regulation by calmodulin and pharmacological agents

ABSTRACTCanonical transient receptor potential channels (TRPC) are involved in receptor-operated and/or store-operated Ca2+ signaling. Inhibition of TRPCs by small molecules was shown to be promising in treating renal diseases. In cells, the channels are regulated by calmodulin. Molecular details of both calmodulin and drug binding have remained elusive so far. Here we report structures of TRPC4 in complex with a pyridazinone-based inhibitor and a pyridazinone-based activator and calmodulin. The structures reveal that both activator and inhibitor bind to the same cavity of the voltage-sensing-like domain and allow us to describe how structural changes from the ligand binding site can be transmitted to the central ion-conducting pore of TRPC4. Calmodulin binds to the rib helix of TRPC4, which results in the ordering of a previously disordered region, fixing the channel in its closed conformation. This represents a novel calmodulin-induced regulatory mechanism of canonical TRP channels.Competing Interest StatementThe authors declare no competing interests. M.Y., M.W.L, and G.M. are or were shareholders of Goldfinch Bio.View Full Text

biochemistry↗

bioRxiv2020

TranSPHIRE: Automated and feedback-optimized on-the-fly processing for cryo-EM

Single particle electron cryomicroscopy (cryo-EM) requires full automation to allow high-throughput structure determination which is especially important for drug discovery research. Although several software packages exist where parts of the cryo-EM pipeline are automated, a complete solution that offers reliable, quality-optimized on-the-fly processing, resulting in a high-resolution three-dimensional reconstruction does not exist. Here we present TranSPHIRE: A software package for fully automated processing of cryo-EM data sets during data acquisition. TranSPHIRE transfers data from the microscope, automatically applies the common pre-processing steps, picks particles, performs 2D clustering, and 3D refinement parallel to image recording. Importantly, TranSPHIRE introduces a machine learning-based feedback loop to re-train its internally used picking model to adapt to any given data set live during processing. This elegant approach enables TranSPHIRE to process data more effectively, producing high-quality particle stacks. TranSPHIRE collects, and displays all microscope settings and metrics generated by its individual tools, in order to allow users to quickly evaluate data during acquisition. TranSPHIRE can run on a single work station and also includes the automated processing of filaments.

biophysics↗

bioRxiv2020

Two particle picking procedures for filamentous proteins: SPHIRE-crYOLO filament mode and SPHIRE-STRIPER

Structure determination of filamentous molecular complexes involves the selection of filaments from cryo-EM micrographs. The automatic selection of helical specimens is particularly difficult and thus many challenging samples with issues such as contamination or aggregation are still manually picked. Here we present two approaches for selecting filamentous complexes: one uses a trained deep neural network to identify the filaments and is integrated in SPHIRE-crYOLO, the other one, called SPHIRE-STRIPER, is based on a classical line detection approach. The advantage of the crYOLO based procedure is that it accurately performs on very challenging data sets and selects filaments with high accuracy. Although STRIPER is less precise, the user benefits from less intervention, since in contrast to crYOLO, STRIPER does not require training. We evaluate the performance of both procedures on tobacco mosaic virus and filamentous F-actin data sets to demonstrate the robustness of each method.

biophysics↗

bioRxiv2018

SPHIRE-crYOLO: A fast and well-centering automated particle picker for cryo-EM

Selecting particles from digital micrographs is an essential step in single particle electron cryomicroscopy (cryo-EM). Since manual selection is a tedious and time-consuming process, many automatic particle pickers have been developed. However, they have problems especially with non-ideal datasets. Here, we present a novel automated particle picking software called crYOLO, which is based on the deep learning object detection system \"You Only Look Once\" (YOLO). After training the network with 500 - 2,500 particles per dataset, it automatically selects the particles with high accuracy and precision reaching a speed of up to six micrographs per second. Importantly, we show that crYOLO can be trained to select previously unseen datasets paving the way for completely automated \"on-the-fly\" cryo-EM data pre-processing during data acquisition. CrYOLO is available as standalone program under http://sphire.mpg.de/ and will be part of the image processing workflow in SPHIRE.

biophysics↗