EMPIAR-11578 - Cryo-EM structure of METTL6 bound to tRNA and SerRS [8544 multi-frame micrographs composed of 40 frames each in TIFF format] [8544 multi-frame micrographs composed of 40 frames each in TIFF format]

EMPIAR-11578

Cryo-EM structure of METTL6 bound to tRNA and SerRS [8544 multi-frame micrographs composed of 40 frames each in TIFF format] [8544 multi-frame micrographs composed of 40 frames each in TIFF format]

Publication:

Structural basis of the cooperation between the m3C-RNA-methyltransferase METTL6 with SerRS amino-acyl-synthetase for tRNA recognition

Throll P, Dolce LG, Lastres PR, Arnold K, Tengo L, Basu S, Kaiser S, Schneider R, Kowalinski E

(---)

Related EMDB entry:

EMD-17530 EMD-17529 EMD-17531 EMD-17532 EMD-17528

Deposited:

2023-06-07

Released:

2024-06-13

Last modified:

2024-06-13

Imageset size:

1.41 TB

Imageset DOI:

10.6019/EMPIAR-11578

Experimental metadata:

Download xml json

Contains:

micrographs - multiframe

Image Sets

1. Unaligned multi-frame micrographs of METTL6 bound to SerRS and native tRNA

Category:

micrographs - multiframe

Image format:

TIFF

No. of images or tilt series:

8544

Image size:

(5760, 4092)

Pixel type:

UNSIGNED BYTE

Pixel spacing:

(0.645, 0.645)

Details:

---

Files:

このエントリはPDBjからダウンロードすることはできません。

選択したファイルを無圧縮zipファイルにアーカイブ化してダウンロードします。

ダウンロードするファイル数やサイズによっては、膨大な時間がかかります。また、この機能は不安定で正常に動作しない可能性があり、ダウンロードされたファイルのチェックサムは検証されません。

rsync、aspera、globus 等の利用をおすすめします。

選択したファイルのリストをダウンロードします。

rsyncコマンド等でファイルリストを指定してダウンロードすることが可能です。

rsync -av --files-from=(path-to-filelist) rsync://empiar.pdbj.org/empiar/archive (path-to-destination)

Browse All Files

Quick Links

Browse EMPIAR

Sample Preparation Widget

Volume Browser

Tomo Viewer Prototype

Claim entries to your ORCiD

Talks and Tutorials

EMPIAR Quick tour

Publications

Re-use study

Statistics

EMPIAR in the news

COVID-19 Data Portal

EMDB

BioImage Archive

PDBe

EMNavigator@PDBj

PDBj

Citations

CryoSIP: unleashing protein high-resolution Cryo-EM via semantic-instance collaborative picking.

Deng Y, Wang S, Xiang M, Li Y, Zhuo L, Cao D, Fu X, Zou Q. (2026)

prismPYP: Power-spectrum and image domain learning for self-supervised micrograph evaluation.

He L, Bartesaghi A. (2026)

Unbend, correction of local beam-induced sample motion in cryo-EM images using a 3D spline model.

Kong L, Zottig X, Elferich J, Grigorieff N. (2026)

Chart builder: an interactive tool for user driven data visualization in the electron microscopy data bank.

Fonseca N, Duraisamy AK, Wang Z, Somasundharam S, Tayebinia M, de Oliveira LC, Ma M, Turner J, Patwardhan A, Kleywegt GJ, Hartley M, Morris KL. (2026)

CryoVirusDB: An Annotated Dataset for AI-Based Virus Particle Identification in Cryo-EM Micrographs.

Gyawali R, Dhakal A, Wang L, Cheng J. (2026)

Dual site targeting of the bacterial 70S ribosome by tetracyclines.

Devarkar SC, Lomakin IB, Wang J, Grada A, Bunick CG. (2026)

Deep-learning methods for contrast enhancement and artifact reduction in cryo-electron tomography: a systematic analysis of the state of the art and proposed improvements.

Jones HN, Deshmukh A, Pande K. (2026)

CryoGS: High-Quality Cryo-EM Homogeneous Reconstruction by Gaussian Splatting

Zeng B, Liu S, Cheng S, Xu G, Fan H. (2026)

Point cloud deformation modeling for particle selection following cryo-EM 2D classification.

Wang X, Mo Z, Li F, Zhang F, Wan X. (2026)

ICECREAM: high-fidelity equivariant cryo-electron tomography.

Kishore V, Debarnot V, Righetto RD, Engel BD, Dokmanić I. (2026)