Glycoinformatics-assisted GlcNAcylation Site Prediction Service
Unlocking Insights with Glycoinformatics: Precision GlcNAcylation Site Prediction
GlcNAcylation refers to glucose aminoacylation, a modification process in which an N-acetylglucose amino acid (GlcNAc) group is added to a hydroxyl amino acid residue of a protein or other biomolecule. CD BioGlyco has many years of experience in providing
Glycoinformatics-assisted Structural and Functional Prediction Services to provide professional prediction services about
GPI-anchored Sites,
Mannosylation Sites, Mucin-type Glycosylation Sites, and GlcNAcylation sites.
Protein sequence analysis for GlcNAcylation site prediction
We predict GlcNAcylation sites by analyzing conserved modalities and specific amino acid patterns in protein sequences. By utilizing the glycoinformatics technique, we compare the known GlcNAcylation site and non-site protein sequences to find out the common features between them.
Biophysical and chemical features analysis for GlcNAcylation site prediction
Our experts extract biophysical and chemical features from the protein sequence. These features include amino acid composition, secondary structure, solvent accessibility, and hydrophobicity. Factors such as protein folding speed, stability, and interactions with other molecules are also taken into account when constructing predictive models.
Machine learning algorithms for GlcNAcylation site prediction
Subsequently, machine learning algorithms are utilized to construct prediction models that are trained with both locus and non-locus datasets to be able to accurately predict new sites. This method is faster and more accurate in discovering GlcNAcylation modification sites on proteins, which provides an important reference for further research on protein function and related diseases. Meanwhile, by accumulating and updating the dataset and combining it with the continuous optimization of machine learning algorithms, we continue to improve the accuracy and stability of the prediction model.
Integrated prediction and experimental validation
Finally, to enhance prediction accuracy, we integrate various forecasting methods based on demand and employ experimental validation to validate the predictions.
Publication
Technology: Machine learning
Journal: BMC Bioinformatics
IF: 3.242
Published: 2015
Results: This article describes a two-layer machine learning approach for identifying O-GlcNAcylation sites and O-GlcNAc transferase substrate templates of proteins. The researchers manually extracted 410 experimentally confirmed O-GlcNAcylation sites from dbOGAP, OGlycBase, and UniProtKB and detected conserved modalities using maximum dependency decomposition. Then, a first layer model was learned for each identified O-GlcNAc transferase (OGT) substrate modality using a profile hidden Markov model (profile HMM). Next, a second layer model was generated using a support vector machine (SVM) based on the output values of the first layer profile HMM. This two-layer predictive model was evaluated by five-fold cross-validation, yielding a sensitivity of 85.4%, a specificity of 84.1%, and an accuracy of 84.7%. In addition, an independent test set from PhosphoSitePlus was used, demonstrating that the method can provide promising accuracy (84.05%) and outperform other O-GlcNAcylation site prediction tools.
Fig.1 Proposed schematic for building a dual-layered predictive model using substrate motifs identified from MDD. (Kao, et al., 2015)
Applications
- Predicting GlcNAcylation sites is used to identify patterns of protein modifications associated with disease, which can help discover new biomarkers.
- GlcNAcylation site predictions are used to reveal the modification status of functional regions of proteins, thus helping to explain their biological functions.
- GlcNAcylation site prediction is used in drug development to provide information on targeting proteins that are dependent on specific modification states.
Advantages
- Our GlcNAcylation site prediction technology has been optimized and validated over many years to provide highly accurate and precise predictions.
- The glycobiology-based prediction technology at CD BioGlyco enables rapid processing of large-scale protein sequence data and supports high-throughput analysis and screening.
- We integrate a large amount of bioinformatics data, including protein sequence, structure, and function information, which enables us to provide comprehensive and detailed prediction results.
Frequently Asked Questions
- Why is it important to predict GlcNAcylation sites?
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Predicting GlcNAcylation sites helps to understand the mechanisms of protein function regulation, discover new biomarkers, identify potential drug targets, and delve deeper into biological processes associated with disease.
- How to assess the accuracy of the prediction results?
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Predicted GlcNAcylation sites can be confirmed by experimental validation. Commonly used validation methods include mass spectrometry, immunoprecipitation combined with mass spectrometry, and transgenic techniques. Experimental validation is one of the most direct and reliable methods for assessing prediction accuracy. Colleagues compare predictions with independent experimental datasets, e.g., using experimental validation results provided in the published literature. This approach validates the generalization ability and robustness of the predictive models on different datasets.
CD BioGlyco is the top choice for glycoinformatics-assisted GlcNAcylation site prediction service. We offer comprehensive support to our clients from sequence analysis to machine learning. If you are interested in our services, please don't hesitate to contact us about your needs and specifications.
Reference
- Kao, H.J.; et al. A two-layered machine learning method to identify protein O-GlcNAcylation sites with O-GlcNAc transferase substrate motifs. BMC bioinformatics. 2015, 16, 1-11.
For research use only. Not intended for any diagnostic use.
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