ArticleGenomics, proteomics & bioinformatics2018
DeepNitro: Prediction of Protein Nitration and Nitrosylation Sites by Deep Learning.
Article in Genomics, proteomics & bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.
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Who cites it
43 citing papers in PubMed.
- Protein S-nitrosylation in the heart: Mechanisms, physiological roles, and pathological remodeling.Redox biology · 2026Review
- Artificial intelligence in plant salt stress research: from predictive models to multi-omics integration.Journal of experimental botany · 2026Review
- A redox-dependent switch governing sensory axon degeneration and regeneration.Scientific reports · 2025Article
- First In Silico Study of TwoInternational journal of molecular sciences · 2025Article
- An NRF2/β3-Adrenoreceptor Axis Drives a Sustained Antioxidant and Metabolic Rewiring Through the Pentose-Phosphate Pathway to Alleviate Cardiac Stress.Circulation · 2025Article
- Post-translational modification prediction via prompt-based fine-tuning of a GPT-2 model.Nature communications · 2024Article
- Protein S-Nitrosylation: A Chemical Modification with Ubiquitous Biological Activities.The protein journal · 2024Review
- SNO-DCA: A model for predictingHeliyon · 2024Article
- A metabolic perspective on nitric oxide function in melanoma.Biochimica et biophysica acta. Reviews on cancer · 2024Review
- Nitroproteomics is instrumental for stratification and targeted treatments of astrocytoma patients: expert recommendations for advanced 3PM approach with improved individual outcomes.The EPMA journal · 2023Review
- Reactive oxygen species- and nitric oxide-dependent regulation of ion and metal homeostasis in plants.Journal of experimental botany · 2023Article
- Comparison of the Nitric Oxide Synthase Interactomes and S-Nitroso-Proteomes: Furthering the Case for Enzymatic S-Nitrosylation.Antioxidants & redox signaling · 2023Article
- The ROP2 GTPase Participates in Nitric Oxide (NO)-Induced Root Shortening in Arabidopsis.Plants (Basel, Switzerland) · 2023Article
- pLMSNOSite: an ensemble-based approach for predicting protein S-nitrosylation sites by integrating supervised word embedding and embedding from pre-trained protein language model.BMC bioinformatics · 2023Article
- Thirty years of molecular dynamics simulations on posttranslational modifications of proteins.Physical chemistry chemical physics : PCCP · 2022Review
- Stimulation of Akt Phosphorylation and Glucose Transport by Metalloporphyrins with Peroxynitrite Decomposition Catalytic Activity.Catalysts (Basel, Switzerland) · 2022Article
- Thiol-based Oxidative Posttranslational Modifications (OxiPTMs) of Plant Proteins.Plant & cell physiology · 2022Review
- Hsp70 in Redox Homeostasis.Cells · 2022Review
- Deep Learning-Based Advances In Protein Posttranslational Modification Site and Protein Cleavage Prediction.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Characterization of the B-Cell Epitopes ofFrontiers in cellular and infection microbiology · 2022Article
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Authors and funding
11 authors.
Funding
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Abstract
Protein nitration and nitrosylation are essential post-translational modifications (PTMs) involved in many fundamental cellular processes. Recent studies have revealed that excessive levels of nitration and nitrosylation in some critical proteins are linked to numerous chronic diseases. Therefore, the identification of substrates that undergo such modifications in a site-specific manner is an important research topic in the community and will provide candidates for targeted therapy. In this study, we aimed to develop a computational tool for predicting nitration and nitrosylation sites in proteins. We first constructed four types of encoding features, including positional amino acid distributions, sequence contextual dependencies, physicochemical properties, and position-specific scoring features, to represent the modified residues. Based on these encoding features, we established a predictor called DeepNitro using deep learning methods for predicting protein nitration and nitrosylation. Using n-fold cross-validation, our evaluation shows great AUC values for DeepNitro, 0.65 for tyrosine nitration, 0.80 for tryptophan nitration, and 0.70 for cysteine nitrosylation, respectively, demonstrating the robustness and reliability of our tool. Also, when tested in the independent dataset, DeepNitro is substantially superior to other similar tools with a 7%-42% improvement in the prediction performance. Taken together, the application of deep learning method and novel encoding schemes, especially the position-specific scoring feature, greatly improves the accuracy of nitration and nitrosylation site prediction and may facilitate the prediction of other PTM sites. DeepNitro is implemented in JAVA and PHP and is freely available for academic research at http://deepnitro.renlab.org.
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