DeepmRNALoc: A Novel Predictor of Eukaryotic mRNA Subcellular Localization Based on Deep Learning

Shihang Wang; Zhehan Shen; Taigang Liu; Wei Long; Linhua Jiang; Sihua Peng

doi:10.3390/molecules28052284

DeepmRNALoc: A Novel Predictor of Eukaryotic mRNA Subcellular Localization Based on Deep Learning

Molecules. 2023 Mar 1;28(5):2284. doi: 10.3390/molecules28052284.

Authors

Shihang Wang^{1

2

3}, Zhehan Shen^{4

5}, Taigang Liu⁵, Wei Long¹, Linhua Jiang^{1

6}, Sihua Peng^{1

3}

Affiliations

¹ School of Information Engineering, Huzhou University, Huzhou 313000, China.
² Shanghai Institute for Advanced Immunochemical Studies, School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China.
³ College of Fisheries and Life Science, Shanghai Ocean University, Shanghai 201306, China.
⁴ Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
⁵ College of Information Technology, Shanghai Ocean University, Shanghai 201306, China.
⁶ European Institute of Innovation and Management, Universitat Politècnica de València, 46022 Valencia, Spain.

Abstract

The subcellular localization of messenger RNA (mRNA) precisely controls where protein products are synthesized and where they function. However, obtaining an mRNA's subcellular localization through wet-lab experiments is time-consuming and expensive, and many existing mRNA subcellular localization prediction algorithms need to be improved. In this study, a deep neural network-based eukaryotic mRNA subcellular location prediction method, DeepmRNALoc, was proposed, utilizing a two-stage feature extraction strategy that featured bimodal information splitting and fusing for the first stage and a VGGNet-like CNN module for the second stage. The five-fold cross-validation accuracies of DeepmRNALoc in the cytoplasm, endoplasmic reticulum, extracellular region, mitochondria, and nucleus were 0.895, 0.594, 0.308, 0.944, and 0.865, respectively, demonstrating that it outperforms existing models and techniques.

Keywords: artificial intelligence; chaos-game representation; deep learning; mRNA subcellular localization.

MeSH terms

Computational Biology / methods
Deep Learning*
Endoplasmic Reticulum / metabolism
Eukaryota* / metabolism
Proteins / metabolism
RNA, Messenger

Substances

Proteins
RNA, Messenger

Grants and funding

This research received funding from the National Natural Science Foundation of China (Nos. 11601324 and 62175037), the Shanghai Science and Technology Innovation Action Plan (No. 20JC1416500), and the Natural Science Foundation of Shanghai (No. 15zr1420800).