Advanced Glycan Structure Prediction Through Glycoinformatics and Mass Solutions!

CD BioGlyco has been at the forefront of providing comprehensive Glycoinformatics-assisted Analysis/Prediction Services for over a decade, offering unparalleled expertise and cutting-edge technology to support clients in their research on glycans. The workflow of our highly recognized glycoinformatics-assisted mass-based glycan structure prediction service is as follows.

Organizing annotated spectral data for glycan structure prediction

Initially, we gather and arrange annotated spectral data from mass, which are derived from glycoconjugates encompassing various major types obtained from previous glycan experiments, such as N-linked, O-linked, and glycolipids. This dataset enables machine learning to understand the fragmentation patterns and tendencies (i.e., intensity ratios) of the spectra for predicting glycan structures.

Training prediction models through spectral data analysis and machine learning

We proceed to train the model using this dataset. Throughout the training process, our experts employ a diverse range of machine learning algorithms and deep learning techniques to ensure that the model can comprehensively comprehend the intricate information in the mass data and accurately predict the sugar structure. The model with high accuracy and stability is ultimately achieved after extensive experimentation and fine-tuning.

Enhanced prediction Python-based workflow and model optimization

Finally, our experts utilize a Python-based workflow we developed to convert raw data into a predictable format and make predictions of sugar structure. This workflow also includes automated data filtering and fragment annotation steps. Our experts also strive to further optimize our models to analyze and predict more complex structures for you, further reduce false positive rates, and estimate relative abundance.

Publication

Applications

• Glycoinformatics-assisted mass-based glycan structure prediction is used to analyze and monitor glycosylation structures in biopharmaceuticals and vaccines to ensure product quality and efficacy.
• The technique can be used to study changes in glycosylation on the surface of cancer cells to support early detection of cancer.
• The technique is used to study glycosylation modifications of cells and proteins in drug discovery.

Advantages

• By combining bioinformatics and mass, we help you predict and characterize complex glycan structures quickly.
• We integrate computational and experimental data to improve the accuracy and confidence of glycan structure prediction.
• Our experts use methods such as big data and machine learning to enhance our modeling and analytical capabilities for glycan structure prediction.

Frequently Asked Questions

• How is the accuracy of the sugar structure prediction?
  • For structure prediction of simple sugar molecules, the accuracy is usually high. However, as the complexity of the structure of a sugar molecule increases, the accuracy of the prediction decreases accordingly. For example, when complex structures such as polysaccharide chains and glycosylated modifications are involved, the difficulty and accuracy of prediction increases. Usually, we combine experimental data and computational modeling to improve the accuracy of prediction.
• How to choose the right glycoinformatics tools and software?
  • Choosing the right glycoinformatics tools and software requires consideration of its algorithmic principles, data support, ease of use accuracy, etc. We also refer to relevant literature and case studies to make a choice.

With a proven track record of success, CD BioGlyco helps numerous clients advance their understanding of glycans by providing a reliable glycoinformatics-assisted mass-based glycan structure prediction service. If you are interested in further details, please feel free to contact us.

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