In Bioinformatics (Oxford, England)
MOTIVATION : Protein essentiality is usually accepted to be a conditional trait and strongly affected by cellular environments. However, existing computational methods often do not take such characteristics into account, preferring to incorporate all available data and train a general model for all cell lines. In addition, the lack of model interpretability limits further exploration and analysis of essential protein predictions.
RESULTS : In this study, we proposed DeepCellEss, a sequence-based interpretable deep learning framework for cell line-specific essential protein predictions. DeepCellEss utilizes convolutional neural network and bidirectional long short-term memory to learn short- and long-range latent information from protein sequences. Further, a multi-head self-attention mechanism is used to provide residue-level model interpretability. For model construction, we collected extremely large-scale benchmark datasets across 323 cell lines. Extensive computational experiments demonstrate that DeepCellEss yields effective prediction performance for different cell lines, and outperforms existing sequence-based methods as well as network-based centrality measures. Finally, we conducted some case studies to illustrate the necessity of considering specific cell lines and the superiority of DeepCellEss. We believe that DeepCellEss can serve as a useful tool for predicting essential proteins across different cell lines.
AVAILABILITY : The DeepCellEss web server is available at http://csuligroup.com:8000/DeepCellEss. The source code can be obtained from https://github.com/CSUBioGroup/DeepCellEss.
SUPPLEMENTARY INFORMATION : Supplementary data are available at Bioinformatics online.
Li Yiming, Zeng Min, Zhang Fuhao, Wu Fang-Xiang, Li Min
2022-Dec-02
