Computational Model-assisted Carbohydrate Antibody Structure Prediction Service
Predict Carbohydrate Antibody Structure with Confidence
Carbohydrate antibodies are an important and specific class of biotherapeutic proteins. As artificial intelligence continues to evolve,
Computational Glycoengineering-assisted Modeling is becoming more mature. Accurate structural models are the basis for exploring the properties and activities of antibodies. The folding of antibodies is usually highly conserved. The most difficult to predict are the 6 highly variable loops, together with several framework residues. CD BioGlyco has a specialized
Carbohydrate Antibody Development team to provide precise carbohydrate antibody structure prediction services.
Carbohydrate antibody modeling
CD BioGlyco provides professional computational modeling services based on large datasets and antibody biology information provided by our clients. Heavy and light chain variable domains (Fv) are an important part of computational modeling because they are relevant to most or all of an antibody's specificity for its antigenic target. Our lab has a strong focus on modeling the Fv region of an antibody including the predictive framework (FR) and the variable loop (CDR) region.
Our researchers search the template database for FR and select templates for the CDR region from the typical ring conformations aggregated by homology, and obtain a structural model of the Fv region after refinement of the H3 ring. Moreover, we support computational modeling of the entire antibody including the constant region (Fc).
Structure prediction
By combining different homology modeling strategies or using other models, our researchers provide accurate carbohydrate antibody structure prediction services.
The Fc region is similar in all antibodies of the same type, so it is very important to predict the structure of Fv. Our computational team is very experienced in antibody structure prediction to help clients predict the structure of Fv and CDR. We provide customized antibody structure prediction solutions according to clients' needs.
The relative orientation of structural domains plays an important role in the analysis of antigen-antibody binding sites. Our lab provides relative structural domain orientation prediction services.
Based on deep learning, we provide FV structure direct prediction service by amino acid sequence.
Publication
Technology: Deep learning
Journal: Nature Communications
Published: 2023
IF: 14.7
Results: In this study, researchers developed a new model for predicting antibody structure. The prediction model consists of a training language model trained on 558 million sequences. Compared to other prediction methods, the model takes less time and is more predictive. Moreover, it also directly predicts 3D atomic coordinates and estimates the residual accuracy.
Fig.1 Comparison of antibody structure prediction methods. (Ruffolo, et al., 2023)
Applications
- Computational model-assisted carbohydrate antibody structure prediction can be used for large-scale screening of target molecules.
- Computational model-assisted carbohydrate antibody structure prediction can be used to optimize antibody affinity and specificity.
- Computational model-assisted carbohydrate antibody structure prediction can be used to quickly predict the complete atomic antibody structure of a single sequence.
Advantages of Us
- Our researchers have extensive knowledge and experience in carbohydrate antibody structure prediction and design.
- The structural prediction service we provide with high prediction accuracy and precision.
- Depending on the client's needs and the features of the different antibodies, our researchers provide the most appropriate solutions.
Frequently Asked Questions
- What are some of the common types of antibody prediction algorithms used?
- Common types of antibody prediction algorithms include deep learning, end-to-end prediction, and grafting-based approaches.
- How do grafting-based methods predict antibody structure?
- The Fv structure is predicted by a deep residual convolutional network, represented by the relative distances and directions between residual pairs. The network requires only light and heavy chain sequences as inputs and is designed with interpretable components to provide insight into model predictions.
Relying on the world's leading computational modeling, CD BioGlyco provides our clients with the most satisfactory carbohydrate antibody modeling and structure prediction services. Our goal is to provide high-precision and high-efficiency structural predictions to our clients all over the world. Please feel free to contact us.
Reference
- Ruffolo, J.A.; et al. Fast, accurate antibody structure prediction from deep learning on massive set of natural antibodies. Nature Communications. 2023, 14(1): 2389.
For research use only. Not intended for any diagnostic use.
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