Computational Model-assisted Carbohydrate Antibody Structure Design Service
Easily Design Satisfactory Antibody Structures
The goal of carbohydrate antibody design is to design new antibodies with specific biological activities. CD BioGlyco provides our clients with high-quality
Carbohydrate Antibody Development services through
Computational Modeling. Computational methods have been an important tool for designing and screening biological antibodies in recent years. Not only that, it modifies the antibody to some extent to enhance its biological activity. Our experienced researchers provide the best carbohydrate antibody design solutions for our clients.
De novo design
Based on an advanced computational framework combined with artificial intelligence, we provide our clients with de novo design services for antibodies that bind specific epitopes.
Our researchers de novo design candidate antibodies with high affinity and specific binding ability through a series of steps including antigen-specific antibody selection, antigen epitope prediction, antibody affinity prediction, and antibody sequence analysis.
Relying on advanced variational autoencoders to generate the 3D backbone coordinates of the model output, our calculators incorporate a variety of constraints and optimization services to help clients quickly design different types of antibodies.
Grafting-based design
The grafting-based approach focuses on the design of complementarity-determining regions (CDRs) motifs and sequences. We mainly provide design services for the following two types of CDRs:
Complementary CDR fragments are obtained by combining extensive databases and high-throughput screening. Our researchers graft the CDR fragments onto the antibody scaffold to complete the design.
CD BioGlyco supports the de novo design of CDR fragments for antibody grafting.
Antibody molecule design and modification
CD BioGlyco provides professional antibody affinity maturation, antibody stability optimization, expression optimization, bispecific antibody design, and antibody aggregation effect analysis services.
Publication
Technology: Fragment assembly, CDR grafting
Journal: Science Advances
Published: 2022
IF: 11.7
Results: In this study, the researchers propose a fragment-based approach to design antibodies against structured epitopes. Combining de novo computational structure modeling and protein folding prediction, the researchers designed and tested single structural domain antibodies against different epitopes. The results of the study showed that all designs were stable and predictable. Moreover, the researchers drew on computational modeling and experimental data to design new antibody CDR ring structures targeting the epitopes and to test their solubility and conformational stability. In summary, fragment-based design facilitates antibody development and the computational program is easy to run and use.
Fig.1 Grafting of engineered CDR motifs into antibody frameworks. (Aguilar Rangel, et al., 2022)
Applications
- Computational model-assisted carbohydrate antibody structure design can be used to develop various types of AI drugs.
- Computational model-assisted carbohydrate antibody structure design can be used to customize new antibodies with epitope specificity and high affinity.
- Computational model-assisted carbohydrate antibody structure design can be used in the development of vaccines and adjuvants to further fill gaps in the clinical use of drugs.
Advantages of Us
- The fragment-based design service we provide does not require the calculation of interaction energies.
- Our researchers provide de novo design services with the advantages of high efficiency, high precision, high activity, and affinity.
- Our computational team provides algorithms that extend beyond the limitations of databases and experimental samples to provide customers with new antibodies with epitope specificity and high affinity.
Frequently Asked Questions
- What are the advantages of computational approaches to antibody design over traditional antibody discovery?
- Antibodies generated by traditional methods may bind effectively to the target molecule but may not always achieve the desired efficacy. Computational methods-assisted antibody structure design greatly overcomes these limitations and reduces the time and cost of antibody discovery. In addition, structure-based antibody design and optimization allow for the design of specific targeting epitopes.
- What are the main steps involved in designing antibodies using deep learning?
- Antibody design using deep learning mainly includes data collection and preprocessing, model training, antibody generation, and optimization and screening.
CD BioGlyco provides carbohydrate antibody structure design services to drastically reduce the time of follow-up time of our clients. We provide professional antibody design services according to the different needs of our clients. Our goal is to be your first choice in carbohydrate antibody development. Please feel free to contact us.
Reference
- Aguilar Rangel, M.; et al.. Fragment-based computational design of antibodies targeting structured epitopes. Science Advances. 2022, 8(45): eabp9540.
For research use only. Not intended for any diagnostic use.
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