Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Unaligned cryo-EM micrographs of HCN4 channel bound to Ivabradine

ABSTRACT:

SUBMITTER: Antonio Chaves-Sanjuan

PROVIDER: EMPIAR-11470 | biostudies-other |

REPOSITORIES: biostudies-other

ACCESS DATA

Json Xml

Publications

Structural determinants of ivabradine block of the open pore of HCN4.

Saponaro Andrea A Krumbach Jan H JH Chaves-Sanjuan Antonio A Sharifzadeh Atiyeh Sadat AS Porro Alessandro A Castelli Roberta R Hamacher Kay K Bolognesi Martino M DiFrancesco Dario D Clarke Oliver B OB Thiel Gerhard G Moroni Anna A

Proceedings of the National Academy of Sciences of the United States of America 20240625 27

HCN1-4 channels are the molecular determinants of the I<sub>f</sub>/I<sub>h</sub> current that crucially regulates cardiac and neuronal cell excitability. HCN dysfunctions lead to sinoatrial block (HCN4), epilepsy (HCN1), and chronic pain (HCN2), widespread medical conditions awaiting subtype-specific treatments. Here, we address the problem by solving the cryo-EM structure of HCN4 in complex with ivabradine, to date the only HCN-specific drug on the market. Our data show ivabradine bound inside ...[more]

PMID: 38917012

Similar Datasets

UPicker: a semi-supervised particle picking transformer method for cryo-EM micrographs.

Project description:Automatic single particle picking is a critical step in the data processing pipeline of cryo-electron microscopy structure reconstruction. In recent years, several deep learning-based algorithms have been developed, demonstrating their potential to solve this challenge. However, current methods highly depend on manually labeled training data, which is labor-intensive and prone to biases especially for high-noise and low-contrast micrographs, resulting in suboptimal precision and recall. To address these problems, we propose UPicker, a semi-supervised transformer-based particle-picking method with a two-stage training process: unsupervised pretraining and supervised fine-tuning. During the unsupervised pretraining, an Adaptive Laplacian of Gaussian region proposal generator is proposed to obtain pseudo-labels from unlabeled data for initial feature learning. For the supervised fine-tuning, UPicker only needs a small amount of labeled data to achieve high accuracy in particle picking. To further enhance model performance, UPicker employs a contrastive denoising training strategy to reduce redundant detections and accelerate convergence, along with a hybrid data augmentation strategy to deal with limited labeled data. Comprehensive experiments on both simulated and experimental datasets demonstrate that UPicker outperforms state-of-the-art particle-picking methods in terms of accuracy and robustness while requiring fewer labeled data than other transformer-based models. Furthermore, ablation studies demonstrate the effectiveness and necessity of each component of UPicker. The source code and data are available at https://github.com/JachyLikeCoding/UPicker.

| S-EPMC11631311 | biostudies-literature

CryoTransformer: A Transformer Model for Picking Protein Particles from Cryo-EM Micrographs.

Project description:Cryo-electron microscopy (cryo-EM) is a powerful technique for determining the structures of large protein complexes. Picking single protein particles from cryo-EM micrographs (images) is a crucial step in reconstructing protein structures from them. However, the widely used template-based particle picking process requires some manual particle picking and is labor-intensive and time-consuming. Though machine learning and artificial intelligence (AI) can potentially automate particle picking, the current AI methods pick particles with low precision or low recall. The erroneously picked particles can severely reduce the quality of reconstructed protein structures, especially for the micrographs with low signal-to-noise (SNR) ratios. To address these shortcomings, we devised CryoTransformer based on transformers, residual networks, and image processing techniques to accurately pick protein particles from cryo-EM micrographs. CryoTransformer was trained and tested on the largest labelled cryo-EM protein particle dataset - CryoPPP. It outperforms the current state-of-the-art machine learning methods of particle picking in terms of the resolution of 3D density maps reconstructed from the picked particles as well as F1-score and is poised to facilitate the automation of the cryo-EM protein particle picking.

| S-EPMC10634673 | biostudies-literature

Unaligned cryo-EM micrographs of Human SHMT1 in complex with RNA

Project description: Not available

| EMPIAR-11413 | biostudies-other

Unaligned and aligned cryo-EM micrographs of 82-kDa malate synthase G

Project description: Not available

| EMPIAR-11167 | biostudies-other

Single-particle cryo-EM unaligned micrographs of Sevenless extracellular domain (pH 6.6)

Project description: Not available

| EMPIAR-12304 | biostudies-other

Cryo-EM structure of K+-bound hERG channel

Project description: Not available

| EMPIAR-10628 | biostudies-other

Cryo-EM structure of substrate-bound human telomerase holoenzyme.

Project description:The enzyme telomerase adds telomeric repeats to chromosome ends to balance the loss of telomeres during genome replication. Telomerase regulation has been implicated in cancer, other human diseases, and ageing, but progress towards clinical manipulation of telomerase has been hampered by the lack of structural data. Here we present the cryo-electron microscopy structure of the substrate-bound human telomerase holoenzyme at subnanometre resolution, showing two flexibly RNA-tethered lobes: the catalytic core with telomerase reverse transcriptase (TERT) and conserved motifs of telomerase RNA (hTR), and an H/ACA ribonucleoprotein (RNP). In the catalytic core, RNA encircles TERT, adopting a well-ordered tertiary structure with surprisingly limited protein-RNA interactions. The H/ACA RNP lobe comprises two sets of heterotetrameric H/ACA proteins and one Cajal body protein, TCAB1, representing a pioneering structure of a large eukaryotic family of ribosome and spliceosome biogenesis factors. Our findings provide a structural framework for understanding human telomerase disease mutations and represent an important step towards telomerase-related clinical therapeutics.

| S-EPMC6223129 | biostudies-literature

Cryo-EM structure of VASH1-SVBP bound to microtubules.

Project description:The dynamic tyrosination-detyrosination cycle of α-tubulin regulates microtubule functions. Perturbation of this cycle impairs mitosis, neural physiology, and cardiomyocyte contraction. The carboxypeptidases vasohibins 1 and 2 (VASH1 and VASH2), in complex with the small vasohibin-binding protein (SVBP), mediate α-tubulin detyrosination. These enzymes detyrosinate microtubules more efficiently than soluble αβ-tubulin heterodimers. The structural basis for this substrate preference is not understood. Using cryo-electron microscopy (cryo-EM), we have determined the structure of human VASH1-SVBP bound to microtubules. The acidic C-terminal tail of α-tubulin binds to a positively charged groove near the active site of VASH1. VASH1 forms multiple additional contacts with the globular domain of α-tubulin, including contacts with a second α-tubulin in an adjacent protofilament. Simultaneous engagement of two protofilaments by VASH1 can only occur within the microtubule lattice, but not with free αβ heterodimers. These lattice-specific interactions enable preferential detyrosination of microtubules by VASH1.

| S-EPMC7449697 | biostudies-literature

Cryo-EM micrographs of RESC1-RESC2 complex bound to gRNA

Project description: Not available

| EMPIAR-11677 | biostudies-other

Cryo-EM micrographs of Tb ADAT2/3 bound to tRNA

Project description: Not available

| EMPIAR-11673 | biostudies-other