PXD019987

PXD019987 is an original dataset announced via ProteomeXchange.

Title	MULocDeep: An Interpretable Deep Learning Model for Protein Localization Prediction with Sub-organelle Resolution
Description	Prediction of protein localization plays an important role in understanding protein function and mechanism. A deep learning-based localization prediction tool (“MULocDeep”) assessing each amino acid’s contribution to the localization process provides insights into the mechanism of protein sorting and localization motifs. A dataset with 45 sub-organellar localization annotations under 10 major sub-cellular compartments was produced and the tool was tested on an independent dataset of mitochondrial proteins that were extracted from Arabidopsis thaliana cell cultures, Solanum tuberosum tubers, and Vicia faba roots, and analyzed by shotgun mass spectrometry.
HostingRepository	PRIDE
AnnounceDate	2024-10-22
AnnouncementXML	Submission_2024-10-22_05:32:03.150.xml
DigitalObjectIdentifier
ReviewLevel	Peer-reviewed dataset
DatasetOrigin	Original dataset
RepositorySupport	Unsupported dataset by repository
PrimarySubmitter	Holger Eubel
SpeciesList	scientific name: Arabidopsis thaliana (Mouse-ear cress); NCBI TaxID: 3702; scientific name: Solanum tuberosum (Potato); NCBI TaxID: 4113; scientific name: Vicia faba var. faba; NCBI TaxID: 1706215;
ModificationList	monohydroxylated residue; acetylated residue; iodoacetamide derivatized residue; deamidated residue
Instrument	Q Exactive

Revision	Datetime	Status	ChangeLog Entry
0	2020-06-24 04:52:05	ID requested
1	2022-02-15 13:14:27	announced
⏵ 2	2024-10-22 05:32:04	announced	2024-10-22: Updated project metadata.

10.1016/j.csbj.2021.08.027;

Jiang Y, Wang D, Yao Y, Eubel H, K, ü, nzler P, M, ø, ller IM, Xu D, MULocDeep: A deep-learning framework for protein subcellular and suborganellar localization prediction with residue-level interpretation. Comput Struct Biotechnol J, 19():4825-4839(2021) [pubmed]

submitter keyword: protein localization prediction, shotgun proteomics,Protein targeting, plant mitochondria isolation, deep learning

Holger Eubel
contact affiliation	Leibniz Universität Hannover Institute of Plant Genetics
contact email	heubel@genetik.uni-hannover.de
lab head
Holger Eubel
contact affiliation	Leibniz Universtät Hannover
contact email	heubel@genetik.uni-hannover.de
dataset submitter

Dataset FTP location NOTE: Most web browsers have now discontinued native support for FTP access within the browser window. But you can usually install another FTP app (we recommend FileZilla) and configure your browser to launch the external application when you click on this FTP link. Or otherwise, launch an app that supports FTP (like FileZilla) and use this address: ftp://ftp.pride.ebi.ac.uk/pride/data/archive/2022/02/PXD019987

PRIDE project URI

• PRIDE
  1. PXD019987
     1. Label: PRIDE project
     2. Name: MULocDeep: An Interpretable Deep Learning Model for Protein Localization Prediction with Sub-organelle Resolution