ArticleBioinformatics (Oxford, England)2020
DeepCleave: a deep learning predictor for caspase and matrix metalloprotease substrates and cleavage sites.
Article in Bioinformatics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers.
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Who cites it
58 citing papers in PubMed, 124 citations in OpenAlex.
- Review
- PhageScout: Protease Cleavage Site Prediction Using an Experimental Substrate Phage Display Motif-Based Approach.International journal of molecular sciences · 2026Article
- Targeting the Undruggable: Deep Learning-Driven Design of Peptide Therapeutics in Cancer.Pharmaceuticals (Basel, Switzerland) · 2026Review
- The rise and future of peptide-based antimicrobials.Journal of microbiology (Seoul, Korea) · 2026Review
- Deep learning guided design of protease substrates.Nature communications · 2026Article
- Lab to codes: multimodel advanced AI-driven pipelines to drive antimicrobial peptide design for the post-antibiotic era.Frontiers in cellular and infection microbiology · 2026Review
- ProjFusNet: deep neural network for peptide precursor prediction using projection-fused protein language model and structural features.Journal of cheminformatics · 2025Article
- MDDeep-Ace: species-specific acetylation site prediction based on multi-domain adaptation.PeerJ · 2025Article
- Protein cleaver: an interactive web interface forFrontiers in bioinformatics · 2025Article
- Abrupt-mediated control of ninjurins regulates Drosophila sessile haemocyte compartments.Development (Cambridge, England) · 2024Article
- A bird's-eye view of the biological mechanism and machine learning prediction approaches for cell-penetrating peptides.Frontiers in artificial intelligence · 2024Review
- Prediction and design of protease enzyme specificity using a structure-aware graph convolutional network.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Molecular de-extinction of ancient antimicrobial peptides enabled by machine learning.Cell host & microbe · 2023Article
- Development of Matrix Metalloproteinases-Mediated Extracellular Matrix Remodeling in Regenerative Medicine: A Mini Review.Tissue engineering and regenerative medicine · 2023Review
- HypoRiPPAtlas as an Atlas of hypothetical natural products for mass spectrometry database search.Nature communications · 2023Article
- PromGER: Promoter Prediction Based on Graph Embedding and Ensemble Learning for Eukaryotic Sequence.Genes · 2023Article
- Predicting Structural Susceptibility of Proteins to Proteolytic Processing.International journal of molecular sciences · 2023Article
- DeepAR: a novel deep learning-based hybrid framework for the interpretable prediction of androgen receptor antagonists.Journal of cheminformatics · 2023Article
- Prediction and Design of Protease Enzyme Specificity Using a Structure-Aware Graph Convolutional Network.bioRxiv : the preprint server for biology · 2023Article
- Approaches to Avoid Proteolysis During Protein Expression and Purification.Methods in molecular biology (Clifton, N.J.) · 2023Article
Corrections and comments
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Authors and funding
12 authors at 6 institutions in 4 countries.
Funding
Abstract
motivationProteases 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.
resultsWe propose DeepCleave, the first deep learning-based predictor of protease-specific substrates and cleavage sites. DeepCleave uses protein substrate sequence data as input and employs convolutional neural networks with transfer learning to train accurate predictive models. High predictive performance of our models stems from the use of high-quality cleavage site features extracted from the substrate sequences through the deep learning process, and the application of transfer learning, multiple kernels and attention layer in the design of the deep network. Empirical tests against several related state-of-the-art methods demonstrate that DeepCleave outperforms these methods in predicting caspase and matrix metalloprotease substrate-cleavage sites. AVAILABILITY AND IMPLEMENTATION: The DeepCleave webserver and source code are freely available at http://deepcleave.erc.monash.edu/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.