MULocDeep

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

authored by
Yuexu Jiang, Duolin Wang, Yifu Yao, Holger Eubel, Patrick Künzler, Ian Max Møller, Dong Xu
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 mu-loc.org.

Organisation(s)
Institute of Plant Genetics
External Organisation(s)
MU Bond Life Sciences Center
Aarhus University
Type
Article
Journal
Computational and structural biotechnology journal
Volume
19
Pages
4825-4839
No. of pages
15
ISSN
2001-0370
Publication date
2021
Publication status
Published
Peer reviewed
Yes
ASJC Scopus subject areas
Genetics, Biophysics, Structural Biology, Biochemistry, Biotechnology, Computer Science Applications
Electronic version(s)
https://doi.org/10.1016/j.csbj.2021.08.027 (Access: Open)