ReviewBriefings in bioinformatics2019
iProt-Sub: a comprehensive package for accurately mapping and predicting protease-specific substrates and cleavage sites.
Review in Briefings in bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 76 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
76 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Progress and trends on machine learning in proteomics during 1997-2024: a bibliometric analysis.Frontiers in medicine · 2025Pooled it
- Iron dysregulation in COVID-19 and reciprocal evolution of SARS-CoV-2: Natura nihil frustra facit.Journal of cellular biochemistry · 2022Pooled it
- Autoantigens PLA2R and THSD7A in membranous nephropathy share a common epitope motif in the N-terminal domain.Journal of autoimmunity · 2020Trial
- Artificial Intelligence and Machine Learning in Mass Spectrometry-Based Proteomics and Metabolomics.Methods in molecular biology (Clifton, N.J.) · 2027Article
- Review
- PhageScout: Protease Cleavage Site Prediction Using an Experimental Substrate Phage Display Motif-Based Approach.International journal of molecular sciences · 2026Article
- Celebrating 25 years of briefings in bioinformatics: a comprehensive bibliometric analysis.Briefings in bioinformatics · 2026Article
- Identification and engineering of highly functional potyviral proteases in cells using co-evolutionary models.Nature communications · 2026Article
- Prediction of peptide cleavage sites using protein language models and graph neural networks.Scientific reports · 2025Article
- A deeper evaluation of cytokeratin fragment 21-1 as a lung cancer tumor marker and comparison of different assays.Biosensors & bioelectronics: X · 2025Article
- iACVP-MR: Accurate Identification of Anti-coronavirus Peptide based on Multiple Features Information and Recurrent Neural Network.Current medicinal chemistry · 2025Article
- PredLyP: A computational tool for predicting tissue-specific (phago-)lysosomal post-digestion peptides.Computational and structural biotechnology journal · 2025Article
- Protein cleaver: an interactive web interface forFrontiers in bioinformatics · 2025Article
- Downregulation of PIK3IP1/TrIP on T cells is controlled by TCR signal strength, PKC and metalloprotease-mediated cleavage.bioRxiv : the preprint server for biology · 2024Article
- ADCdb: the database of antibody-drug conjugates.Nucleic acids research · 2024Article
- 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
- N-Terminomic Changes in Neurons During Excitotoxicity Reveal Proteolytic Events Associated With Synaptic Dysfunctions and Potential Targets for Neuroprotection.Molecular & cellular proteomics : MCP · 2023Article
- Proteomic data and structure analysis combined reveal interplay of structural rigidity and flexibility on selectivity of cysteine cathepsins.Communications biology · 2023Article
- Prediction and Design of Protease Enzyme Specificity Using a Structure-Aware Graph Convolutional Network.bioRxiv : the preprint server for biology · 2023Article
- sAMPpred-GAT: prediction of antimicrobial peptide by graph attention network and predicted peptide structure.Bioinformatics (Oxford, England) · 2023Article
16 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Regulation of proteolysis plays a critical role in a myriad of important cellular processes. The key to better understanding the mechanisms that control this process is to identify the specific substrates that each protease targets. To address this, we have developed iProt-Sub, a powerful bioinformatics tool for the accurate prediction of protease-specific substrates and their cleavage sites. Importantly, iProt-Sub represents a significantly advanced version of its successful predecessor, PROSPER. It provides optimized cleavage site prediction models with better prediction performance and coverage for more species-specific proteases (4 major protease families and 38 different proteases). iProt-Sub integrates heterogeneous sequence and structural features and uses a two-step feature selection procedure to further remove redundant and irrelevant features in an effort to improve the cleavage site prediction accuracy. Features used by iProt-Sub are encoded by 11 different sequence encoding schemes, including local amino acid sequence profile, secondary structure, solvent accessibility and native disorder, which will allow a more accurate representation of the protease specificity of approximately 38 proteases and training of the prediction models. Benchmarking experiments using cross-validation and independent tests showed that iProt-Sub is able to achieve a better performance than several existing generic tools. We anticipate that iProt-Sub will be a powerful tool for proteome-wide prediction of protease-specific substrates and their cleavage sites, and will facilitate hypothesis-driven functional interrogation of protease-specific substrate cleavage and proteolytic events.
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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.