CD ComputaBio understands that each project is unique, and our team tailors the approach to address the specific requirements of our clients, ensuring that the delivered solutions are precisely aligned with their objectives and expectations.
CD ComputaBio is a pioneering company at the forefront of computational biology, specializing in antibody screening. Our innovative approach combines cutting-edge algorithms, deep learning and big data analytics to accelerate the discovery and optimization of antibodies for therapeutic, diagnostic and research applications. With a relentless pursuit of precision, efficiency and innovation, we are redefining the landscape of antibody screening, making it faster, more cost-effective and ultimately more accessible.
Collect relevant information including protein sequences, structural data of antibody-antigen complexes, immune response data, and pathogenicity data.
Algorithms (e.g. BLAST, Needleman-Wunsch, Smith-Waterman, etc.) are utilized to compare and recognize sequence similarities and patterns between antibodies and antigens.
After collecting sufficient data, various algorithms including homology modeling, Ab-initio prediction, threading, etc. are used to simulate the 3D structure of the antibody.
Using molecular docking software (e.g. AutoDock) and algorithms, the binding of antibodies to antigens is simulated, and successfully docked antibodies are possible candidates.
MD (Molecular Dynamics) simulation is used to check the binding stability of antibodies and antigens.
Based on the simulation results, the screening is performed by comparing and evaluating the characteristics (e.g., affinity, specificity, potency, etc.) of each antibody in combination with various experimental data.
finally, the screened preferred antibodies are subjected to experimental validation and further optimization. If the experimental results are not satisfactory, the screening step can be returned to again for iterative improvement.
| Software |
Descriptions |
|
IMGT/HighV-QUEST |
Part of the IMGT suite, this is a high-throughput antibody sequence analysis tool, widely used in immunogenetics. |
|
PyMOL |
An open-source molecular visualization system that can be used for antibody modeling and analysis. |
|
Rosetta Antibody |
A toolkit for antibody modeling and design, which is part of the Rosetta suite. The academic version is free for academic and non-profit use. |
|
AbNum |
A free tool for numbering antibody sequences according to the various schemes commonly used in the field. |
|
ANTHEPROT |
Integrates a wide array of functions for protein sequence analysis, which can be applied to antibodies. |
|
RaptorX-Property |
A web server for predicting protein structure and function, which can be used for antibodies. It predicts secondary structure, solvent accessibility, and disordered regions. |
|
IEDB Tools |
The Immune Epitope Database and Analysis Resource offers various tools for predicting epitopes, which can be useful in antibody research. |
|
CBTOPE |
A free web-based tool for predicting B-cell epitopes, which can be useful in understanding antibody binding. |
|
Clustal Omega |
A tool for aligning sequences, which can be used for comparing antibody sequences. |
Driven by cutting-edge computational algorithms, our approach to antibody modeling and design is at the vanguard of scientific innovation.
Machine Learning Models
We develop customized machine learning models that are trained on large datasets of antibody sequence and structural information. These models allow us to predict antibody-antigen interactions, assess binding affinity, and prioritize candidate antibodies with the highest likelihood of success.
Molecular Docking Simulation
Our computational platform incorporates advanced molecular docking simulations that allow us to explore the binding modes of antibodies to their target antigens at the molecular level. By analyzing these interactions, we can guide the rational design and optimization of antibodies.
Structure-Based Design Tools
We use structure-based design tools that facilitate rational modification and optimization of antibody sequences. Using structural information and computational modeling, these tools enable us to design antibodies with enhanced properties, including better binding affinity, stability and bioavailability, while minimizing immunogenicity.
Unrivaled efficiency: Accelerates the entire antibody development process. Precision and accuracy: Ensures extremely high precision and accuracy. Data-driven insights: Powerful computational analysis and interpretation capabilities Cost-effective solution: Substantial cost savings
CD ComputaBio addresses the most pressing challenges in antibody discovery and optimization, offering customers a range of attractive benefits and features. Our algorithms rapidly prioritize antibody candidates with the highest likelihood of success, enabling clients to focus their resources on the most promising leads and accelerate the progress of their antibody development programs. We tailor our optimization strategies to each client's specific goals and requirements, ensuring that the optimized antibody meets the requirements.
