Computational Model-assisted Carbohydrate Antibody Development Service
Your Premier Partner for Computational and AI-assisted Carbohydrate Antibody Development
Antibodies not only play an important role in biochemistry and molecular biology but as technology continues to progress, they are widely used as diagnostic and therapeutic tools for autoimmune diseases and cancer. Relying on the development of computer and artificial intelligence, CD BioGlyco provides comprehensive carbohydrate antibody development services including antibody sequence analysis, antibody structure prediction and optimization, antibody-antigen interaction prediction, and antibody design, etc. CD BioGlyco has computer and artificial intelligence experts to provide private and
Specific Modeling services according to clients' needs.
Modeling
Based on accumulated databases and reliable algorithms, we provide carbohydrate antibody modeling services. Our researchers improve computational models for antibody development based on predictors of antibody sequence, structure, and functional properties.
The variable domains (Fv) of the heavy and light chains of antibodies are critical in structural modeling. This is because the variability of the variable domains is directly related to the majority of antigenic targets that the antibody is directed against. We mainly provide regional modeling services for the predictive framework and variable loop (CDR) regions.
Antibody profiling
Our lab provides comprehensive antibody sequence analysis services including analysis of antibody heavy chain, light chain variable region, constant region, variable domain, antibody post-translational modification glycosylation site prediction, and antigenic epitope prediction.
Our researchers provide carbohydrate antibody structure prediction and optimization services. Antibody structures and binding sites are explored on a case-by-case basis based on training models. Moreover, we provide antibody-antigen interaction prediction services.
CD BioGlyco mainly offers the following carbohydrate antibody-related services:
Publication
Technology: Structure-guided alanine scanning, Computational docking, Homology modeling, Enzyme-linked immunosorbent assays (ELISA)
Journal: Scientific Reports
Published: 2020
IF: 4.997
Results: In this study, researchers found that the binding between m102.4 and Nipah virus (NiV) is mainly mediated by two residues in the HCDR3 region. A comprehensive analysis was performed by constructing an interaction model through molecular docking, in which a total of 500 gestures were generated by the docking process. The researchers sorted and screened them to determine the optimal binding mode. The model suggests that m102.4 acts primarily on the hydrophobic center cavity and the hydrophilic edge on the glycoprotein. The increased binding ability of the antibody was associated with alanine mutations at some sites on the glycoprotein such as E505, E533, E554, E579, T531, Q559, and Y581.
Fig.1 Receptor-ligand interaction prediction and mechanism of antibody-glycoprotein attachment. (Tit-Oon, et al., 2020)
Applications of Computational Model-assisted Carbohydrate Antibody
- Computational model-assisted carbohydrate antibodies can be used to modulate the biological activity, function, or pharmacological properties of antibody drugs.
- Computational model-assisted carbohydrate antibodies can be used for therapeutic antibody development.
- Computational model-assisted carbohydrate antibody development can be used to predict antibody and antigen action sites.
Advantages of Us
- Our computational tools help our clients to develop customized carbohydrate antibodies efficiently and with high quality.
- Relying on a team of professional experts, we provide advanced computational structure prediction methods, high-throughput sequencing, and data analysis services.
- Our team has many years of successful experience in carbohydrate antibody development.
Frequently Asked Questions
- What are the limitations of traditional antibody engineering development?
- Obtaining the full range of specific epitopes on the target antigen is more difficult because most conventional screens only produce binders for a given epitope.
- Only a very small percentage of the antibodies obtained match the physicochemical properties required for therapeutic drugs.
- What are the models included in the database/tool-based deep learning approach for novel antibody design?
- Sequence-based models
- Structure-based models
- Structure and sequence-based models
CD BioGlyco has developed a variety of methods for computer-assisted carbohydrate antibody development. We provide you with raw computational data and processed analytical reports. Our team efficiently solves all your challenges in carbohydrate antibody development. Don't hesitate to contact us whenever you need assistance.
Reference
- Tit-Oon, P.; et al. Prediction of the binding interface between monoclonal antibody m102.4 and Nipah attachment glycoprotein using structure-guided alanine scanning and computational docking. Scientific Reports. 2020, 10(1): 18256.
For research use only. Not intended for any diagnostic use.
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