Glycoinformatics-assisted N-, C- and O-linked Glycosylation Site Prediction Service
Empowering Precise Glycosylation Site Predictions with Advanced Glycoinformatics Technology
Glycosylation site prediction is the use of bioinformatics methods and computational models to predict where glycosylation modifications are likely to occur in a protein sequence. These predictions are usually based on known glycosylation patterns and protein sequence features. CD BioGlyco has been innovating its glycoinformatics tools for many years and has been providing our clients with satisfactory Glycoinformatics-assisted Analysis/Prediction Services. Our comprehensive Glycoinformatics-assisted Structural and Functional Prediction Service provides our clients with a wide range of assistance in their glycobiological research. Our glycoinformatics experts use machine learning methods to help you predict N-, O-, and C-glycosylation sites.
Two-step feature selection strategy for improved predictive performance
Our experts two-step feature selection strategy to extract the most relevant features. In the first step, they use mutual information and correlation coefficients to evaluate the correlation between features and glycosylation sites and select the most relevant features. In the second step, they used a recursive feature elimination algorithm to further filter the features to improve the predictive performance of the model.
Feature selection and model construction in glycosylation prediction
After feature selection, we use a random forest algorithm to construct the prediction model. We take protein sequences as input features and use known glycosylation sites for training. The performance of the model is evaluated by cross-validation of the training set and the predictive accuracy of the model is evaluated using receiver operating characteristic (ROC) curves and area under the ROC curve (AUC) values.
Prediction of glycosylation site
After evaluating the predictive accuracy of the model, we set the specificity level to 99.0% and screened potential glycosylated proteins and their corresponding glycosylation sites for you in the complete human proteome. We provide you with a complete list of predicted glycosylated proteins and their glycosylation sites.
Publication
Technology: Machine learning
Journal: PloS one
IF: 2.9
Published: 2017
Results: This article presents a computational method for predicting N-linked glycosylation sites. The article describes the importance and characteristics of N-linked glycosylation in proteins and points out the problems of existing models. To solve these problems, the authors propose a computational method based on machine learning and comprehensive feature extraction techniques. The method is trained using a dataset from the UniProt database and features related to post-translational modification sites are extracted. Then, a neural network is trained using backpropagation methods and the model is validated using a variety of quantitative metrics. The article also describes some methods of calculating statistical moments and how to transform a one-dimensional protein sequence into a two-dimensional matrix for calculation.
Fig.1 The verification of the predictive model. (Akmal, et al., 2017)
Applications
- Glycosylation is a common form of protein modification. The prediction of glycosylation sites helps researchers understand the effects of glycosylation on protein function and thus explore its role in many biological processes.
- The prediction of glycosylation sites is used for biomarker discovery in the early stages of disease.
- Integrating the prediction of glycosylation sites with, e.g., gene expression, protein interaction networks, etc., can be used to build more comprehensive disease models that reveal the role and impact of glycosylation in overall biological systems.
Advantages
- We utilize computer algorithms and models for prediction, which rapidly analyze large amounts of protein sequence data, greatly accelerating research progress and data processing.
- Our glycobioinformatics tools and algorithms are highly automated, reducing human error, and capable of handling complex data analysis tasks.
- The accuracy and precision of glycosylation site prediction continue to improve as we enhance the optimization of our machine-learning techniques.
Frequently Asked Questions
- How is uncertainty or variability in the sequence handled in forecasting?
- Common strategies include identifying conserved glycosylation sites using sequence conservation analysis, integrating protein structure or functional domain information to provide additional clues, evaluating the extent to which regions of variability may be affected through sequence alignment and multiple sequence analysis, and training with machine learning models to improve prediction accuracy. Ultimately, experimental validation is a critical step to confirm the veracity and biological significance of the prediction results, especially for protein sequences with large variability or unknown functional domains.
- How are prediction results interpreted and used?
- Prediction results typically list possible glycosylation sites and their associated confidence scores or predicted probabilities. Our researchers interpret and use these results in conjunction with biological background knowledge and experimental validation to ensure their practical application.
CD BioGlyco has a range of tools for predicting the structure and function of glycans, offering a comprehensive glycoinformatics-based N-, C- and O-linked glycosylation site prediction service. If you further details, please feel free to contact us.
References
- Akmal, M.A.; et al. Prediction of N-linked glycosylation sites using position relative features and statistical moments. PloS one. 2017, 12(8): e0181966.
- Hassan, H.; et al. Prediction of O-glycosylation sites using random forest and GA-tuned PSO technique. Bioinformatics and Biology insights. 2015, 9: BBI. S26864.
For research use only. Not intended for any diagnostic use.
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