ArticleInternational journal of biological sciences2018
PTM-ssMP: A Web Server for Predicting Different Types of Post-translational Modification Sites Using Novel Site-specific Modification Profile.
Article in International journal of biological sciences, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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29 citing papers in PubMed.
- Deep learning-driven decoding of ubiquitination: from regulatory mechanisms to targeted protein degradation.Biology direct · 2026Review
- Protein acetylation in atherosclerosis: beyond inflammation to core cellular processes and therapeutic potential.Frontiers in immunology · 2026Review
- Experimental measurement and computational prediction of bacterial Hanks-type Ser/Thr signaling system regulatory targets.Molecular microbiology · 2024Review
- Deep learning in structural bioinformatics: current applications and future perspectives.Briefings in bioinformatics · 2024Review
- m5U-SVM: identification of RNA 5-methyluridine modification sites based on multi-view features of physicochemical features and distributed representation.BMC biology · 2023Article
- SARS-CoV-2 Spike Protein Post-Translational Modification Landscape and Its Impact on Protein Structure and Function via Computational Prediction.Research (Washington, D.C.) · 2023Article
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- PScL-2LSAESM: bioimage-based prediction of protein subcellular localization by integrating heterogeneous features with the two-level SAE-SM and mean ensemble method.Bioinformatics (Oxford, England) · 2023Article
- PScL-DDCFPred: an ensemble deep learning-based approach for characterizing multiclass subcellular localization of human proteins from bioimage data.Bioinformatics (Oxford, England) · 2022Article
- Prediction of serine phosphorylation sites mapping on Schizosaccharomyces Pombe by fusing three encoding schemes with the random forest classifier.Scientific reports · 2022Article
- iProtGly-SS: A Tool to Accurately Predict Protein Glycation Site Using Structural-Based Features.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Protein acetylation: a novel modus of obesity regulation.Journal of molecular medicine (Berlin, Germany) · 2021Review
- Posttranslational modifications in proteins: resources, tools and prediction methods.Database : the journal of biological databases and curation · 2021Article
- Plant Proteoforms Under Environmental Stress: Functional Proteins Arising From a Single Gene.Frontiers in plant science · 2021Review
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- iHyd-LysSite (EPSV): Identifying Hydroxylysine Sites in Protein Using Statistical Formulation by Extracting Enhanced Position and Sequence Variant Feature Technique.Current genomics · 2020Article
- Ubiquitin-Like Modifiers: Emerging Regulators of Protozoan Parasites.Biomolecules · 2020Review
- Automated classification of protein subcellular localization in immunohistochemistry images to reveal biomarkers in colon cancer.BMC bioinformatics · 2020Article
- MusiteDeep: a deep-learning based webserver for protein post-translational modification site prediction and visualization.Nucleic acids research · 2020Article
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5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Protein post-translational modifications (PTMs) are chemical modifications of a protein after its translation. Owing to its play an important role in deep understanding of various biological processes and the development of effective drugs, PTM site prediction have become a hot topic in bioinformatics. Recently, many online tools are developed to prediction various types of PTM sites, most of which are based on local sequence and some biological information. However, few of existing tools consider the relations between different PTMs for their prediction task. Here, we develop a web server called PTM-ssMP to predict PTM site, which adopts site-specific modification profile (ssMP) to efficiently extract and encode the information of both proximal PTMs and local sequence simultaneously. In PTM-ssMP we provide efficient prediction of multiple types of PTM site including phosphorylation, lysine acetylation, ubiquitination, sumoylation, methylation, O-GalNAc, O-GlcNAc, sulfation and proteolytic cleavage. To assess the performance of PTM-ssMP, a large number of experimentally verified PTM sites are collected from several sources and used to train and test the prediction models. Our results suggest that ssMP consistently contributes to remarkable improvement of prediction performance. In addition, results of independent tests demonstrate that PTM-ssMP compares favorably with other existing tools for different PTM types. PTM-ssMP is implemented as an online web server with user-friendly interface, which is freely available at http://bioinformatics.ustc.edu.cn/PTM-ssMP/index/.
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