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

Pages

4825-4839

No. of pages

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)