ArticlePloS one2016
iSulf-Cys: Prediction of S-sulfenylation Sites in Proteins with Physicochemical Properties of Amino Acids.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- BioSemAF-BiLSTM: a protein sequence feature extraction framework based on semantic and evolutionary information.Frontiers in genetics · 2025Article
- CysModDB: a comprehensive platform with the integration of manually curated resources and analysis tools for cysteine posttranslational modifications.Briefings in bioinformatics · 2022Article
- pCysMod: Prediction of Multiple Cysteine Modifications Based on Deep Learning Framework.Frontiers in cell and developmental biology · 2021Article
- Functions and Mechanisms of Lysine Glutarylation in Eukaryotes.Frontiers in cell and developmental biology · 2021Review
- Accurately Predicting Glutarylation Sites Using Sequential Bi-Peptide-Based Evolutionary Features.Genes · 2020Article
- DeepCSO: A Deep-Learning Network Approach to Predicting Cysteine S-Sulphenylation Sites.Frontiers in cell and developmental biology · 2020Article
- SIMLIN: a bioinformatics tool for prediction of S-sulphenylation in the human proteome based on multi-stage ensemble-learning models.BMC bioinformatics · 2019Article
- An Information Entropy-Based Approach for Computationally Identifying Histone Lysine Butyrylation.Frontiers in genetics · 2019Article
- Recognition of Protein Pupylation Sites by Adopting Resampling Approach.Molecules (Basel, Switzerland) · 2018Article
- ADPredict: ADP-ribosylation site prediction based on physicochemical and structural descriptors.Bioinformatics (Oxford, England) · 2018Article
- SVM-SulfoSite: A support vector machine based predictor for sulfenylation sites.Scientific reports · 2018Article
- CIPPN: computational identification of protein pupylation sites by using neural network.Oncotarget · 2017Article
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Authors and funding
3 authors.
Funding
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Abstract
Cysteine S-sulfenylation is an important post-translational modification (PTM) in proteins, and provides redox regulation of protein functions. Bioinformatics and structural analyses indicated that S-sulfenylation could impact many biological and functional categories and had distinct structural features. However, major limitations for identifying cysteine S-sulfenylation were expensive and low-throughout. In view of this situation, the establishment of a useful computational method and the development of an efficient predictor are highly desired. In this study, a predictor iSulf-Cys which incorporated 14 kinds of physicochemical properties of amino acids was proposed. With the 10-fold cross-validation, the value of area under the curve (AUC) was 0.7155 ± 0.0085, MCC 0.3122 ± 0.0144 on the training dataset for 20 times. iSulf-Cys also showed satisfying performance in the independent testing dataset with AUC 0.7343 and MCC 0.3315. Features which were constructed from physicochemical properties and position were carefully analyzed. Meanwhile, a user-friendly web-server for iSulf-Cys is accessible at http://app.aporc.org/iSulf-Cys/.
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