ArticlePloS one2012
SUMOhydro: a novel method for the prediction of sumoylation sites based on hydrophobic properties.
Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed.
- Deep learning framework with interpretable feature selection for accurate SUMOylation site prediction.Scientific reports · 2026Article
- DRP-PSM: Multi-Level Feature Integration Reveals Hierarchical Contributions to Pathogenic Synonymous Mutation Prediction.ACS omega · 2025Article
- XGBoost-enhanced ensemble model using discriminative hybrid features for the prediction of sumoylation sites.BioData mining · 2025Article
- ProSol-multi: Protein solubility prediction via amino acids multi-level correlation and discriminative distribution.Heliyon · 2024Article
- PSSM-Sumo: deep learning based intelligent model for prediction of sumoylation sites using discriminative features.BMC bioinformatics · 2024Article
- SumoPred-PLM: human SUMOylation and SUMO2/3 sites Prediction using Pre-trained Protein Language Model.NAR genomics and bioinformatics · 2024Article
- Enhancing Sumoylation Site Prediction: A Deep Neural Network with Discriminative Features.Life (Basel, Switzerland) · 2023Article
- Article
- EdeepSADPr: an extensive deep-learning architecture for prediction of theFrontiers in cell and developmental biology · 2023Article
- nhKcr: a new bioinformatics tool for predicting crotonylation sites on human nonhistone proteins based on deep learning.Briefings in bioinformatics · 2021Article
- SUMOylation Regulates TDP-43 Splicing Activity and Nucleocytoplasmic Distribution.Molecular neurobiology · 2021Article
- An Ensemble Deep Learning based Predictor for Simultaneously Identifying Protein Ubiquitylation and SUMOylation Sites.BMC bioinformatics · 2021Article
- iLearnPlus: a comprehensive and automated machine-learning platform for nucleic acid and protein sequence analysis, prediction and visualization.Nucleic acids research · 2021Article
- Current Methods of Post-Translational Modification Analysis and Their Applications in Blood Cancers.Cancers · 2021Review
- RF-MaloSite and DL-Malosite: Methods based on random forest and deep learning to identify malonylation sites.Computational and structural biotechnology journal · 2020Article
- DeepKhib: A Deep-Learning Framework for Lysine 2-Hydroxyisobutyrylation Sites Prediction.Frontiers in cell and developmental biology · 2020Article
- Large-scale comparative assessment of computational predictors for lysine post-translational modification sites.Briefings in bioinformatics · 2019Review
- BioSeq-Analysis2.0: an updated platform for analyzing DNA, RNA and protein sequences at sequence level and residue level based on machine learning approaches.Nucleic acids research · 2019Article
- HseSUMO: Sumoylation site prediction using half-sphere exposures of amino acids residues.BMC genomics · 2019Article
- SumSec: Accurate Prediction of Sumoylation Sites Using Predicted Secondary Structure.Molecules (Basel, Switzerland) · 2018Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sumoylation is one of the most essential mechanisms of reversible protein post-translational modifications and is a crucial biochemical process in the regulation of a variety of important biological functions. Sumoylation is also closely involved in various human diseases. The accurate computational identification of sumoylation sites in protein sequences aids in experimental design and mechanistic research in cellular biology. In this study, we introduced amino acid hydrophobicity as a parameter into a traditional binary encoding scheme and developed a novel sumoylation site prediction tool termed SUMOhydro. With the assistance of a support vector machine, the proposed method was trained and tested using a stringent non-redundant sumoylation dataset. In a leave-one-out cross-validation, the proposed method yielded an excellent performance with a correlation coefficient, specificity, sensitivity and accuracy equal to 0.690, 98.6%, 71.1% and 97.5%, respectively. In addition, SUMOhydro has been benchmarked against previously described predictors based on an independent dataset, thereby suggesting that the introduction of hydrophobicity as an additional parameter could assist in the prediction of sumoylation sites. Currently, SUMOhydro is freely accessible at http://protein.cau.edu.cn/others/SUMOhydro/.
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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.