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MULocDeep: An Interpretable Deep Learning Model for Protein Localization Prediction with Sub-organelle Resolution

ABSTRACT: 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.

INSTRUMENT(S): Q Exactive

ORGANISM(S): Vicia Faba Var. Faba Solanum Tuberosum (potato) Arabidopsis Thaliana (mouse-ear Cress)

TISSUE(S): Plant Cell, Root, Tuber, Cell Culture

SUBMITTER: Holger Eubel

LAB HEAD: Holger Eubel

PROVIDER: PXD019987 | Pride | 2022-02-15

REPOSITORIES: Pride

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

Jiang Yuexu Y Wang Duolin D Yao Yifu Y Eubel Holger H Künzler Patrick P Møller Ian Max IM Xu Dong D

Computational and structural biotechnology journal 20210818

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 experime ...[more]

PMID: 34522290

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