Journal article

DeepCleave: A deep learning predictor for caspase and matrix metalloprotease substrates and cleavage sites

F Li, J Chen, A Leier, T Marquez-Lago, Q Liu, Y Wang, J Revote, AI Smith, T Akutsu, GI Webb, L Kurgan, J Song

Bioinformatics | OXFORD UNIV PRESS | Published : 2020

Abstract

Motivation: Proteases are enzymes that cleave target substrate proteins by catalyzing the hydrolysis of peptide bonds between specific amino acids. While the functional proteolysis regulated by proteases plays a central role in the 'life and death' cellular processes, many of the corresponding substrates and their cleavage sites were not found yet. Availability of accurate predictors of the substrates and cleavage sites would facilitate understanding of proteases' functions and physiological roles. Deep learning is a promising approach for the development of accurate predictors of substrate cleavage events. Results: We propose DeepCleave, the first deep learning-based predictor of protease-s..

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University of Melbourne Researchers

Grants

Awarded by National Institutes of Health


Funding Acknowledgements

This work was supported by grants from the Australian Research Council (ARC) (LP110200333 and DP120104460), National Health and Medical Research Council of Australia (NHMRC) (1092262, 490989), the National Institute of Allergy and Infectious Diseases of the National Institutes of Health (R01 AI111965) and a Major Inter-Disciplinary Research (IDR) Grant Awarded by Monash University, and the Collaborative Research Program of Institute for Chemical Research, Kyoto University (2019-32). LK was supported in part by the Robert J. Mattauch Endowment funds.