Affordable Access

deepdyve-link
Publisher Website

MULocDeep: A deep-learning framework for protein subcellular and suborganellar localization prediction with residue-level interpretation

Authors
  • Jiang, Yuexu1
  • Wang, Duolin1
  • Yao, Yifu1
  • Eubel, Holger2
  • Künzler, Patrick2
  • Møller, Ian Max3
  • Xu, Dong1
  • 1 r, Columbia, MO, USA
  • 2 Institute of Plant Genetics, Leibniz University Hannover, Hannover, Germany
  • 3 Department of Molecular Biology and Genetics, Aarhus University, Forsøgsvej 1, DK-4200 Slagelse, Denmark
Type
Published Article
Journal
Computational and Structural Biotechnology Journal
Publisher
Elsevier
Publication Date
Aug 18, 2021
Volume
19
Pages
4825–4839
Identifiers
DOI: 10.1016/j.csbj.2021.08.027
PMID: 34522290
PMCID: PMC8426535
Source
PubMed Central
Keywords
Disciplines
  • Research Article
License
Unknown

Abstract

Prediction of protein localization plays an important role in understanding protein function and mechanisms. In this paper, we propose a general deep learning-based localization prediction framework, MULocDeep, which can predict multiple localizations of a protein at both subcellular and suborganellar levels. We collected a dataset with 44 suborganellar localization annotations in 10 major subcellular compartments—the most comprehensive suborganelle localization dataset to date. We also experimentally generated an independent dataset of mitochondrial proteins in Arabidopsis thaliana cell cultures, Solanum tuberosum tubers, and Vicia faba roots and made this dataset publicly available. Evaluations using the above datasets show that overall, MULocDeep outperforms other major methods at both subcellular and suborganellar levels. Furthermore, MULocDeep assesses each amino acid’s contribution to localization, which provides insights into the mechanism of protein sorting and localization motifs. A web server can be accessed at http://mu-loc.org .

Report this publication

Statistics

Seen <100 times