Antibodies play a crucial role in the immune system by recognizing and neutralizing foreign pathogens and facilitating clearance of infected cells. Due to their specificity and high affinity for their targets, antibodies have great potential for the treatment of a wide range of diseases, including cancer, autoimmune diseases, and infectious diseases. However, the process of developing effective antibody therapies is both time-consuming and expensive. This is where CD ComputaBio comes in, offering cutting-edge antibody protein modeling services to streamline the drug discovery process.
At CD ComputaBio, we offer a comprehensive range of solutions to address the challenges encountered during antibody development. Our services cover the following areas:
We offer the following two methods for antibody structure prediction:
Template Selection: Our template-based modeling service uses known antibody structures as templates to select the most similar template by comparing the sequence similarity between the antibody sequence and a library of known antibody structures.
Template-Based Modeling: We use templates to model matching fragments and fold prediction and simulation methods to model mismatched fragments.
Ab initio Antibody Prediction: We predict the three-dimensional structure of antibodies based on amino acid sequence information without relying on template.
There are several main methods for humanizing computer antibodies:
Human Antibody Libraries: We collect human cells and then utilize molecular biotechnology and bioinformatics methods to create a library of antibody sequences containing a wide range of possible human antibodies.
Antibody Chimerization: We use computer models and algorithms to predict the structure and function of non-human and human antibodies, and then design and optimize the structure of the chimeric antibody through computer simulations.
Antibody Affinity Prediction Service: We use computer simulations and algorithms to predict the affinity between antibodies and antigens that can provide critical information for antibody drug development.
The main methods we offer for antibody optimization include the following:
3D Modeling and Simulation: constructing the 3D structure of an antibody based on its amino acid sequence and then optimizing the structure by simulating molecular dynamics.
Machine Learning and Artificial Intelligence: these techniques are utilized to find the optimal antibody design theory.
Sequence-Based Thermal Stability Prediction: find the most suitable sequence by analyzing the trend of thermal stability change of different antibody sequences.
Molecular Simulation Techniques: They are used to predict the fine-grained interactions of antibodies with antigens.
The success of our antibody protein modeling service is due in large part to our use of advanced algorithms, and we offer the following algorithms for you to choose from:
Homology Modeling
This algorithm predicts the structure of a target protein based on a template with a known structure similar to the target protein sequence. This is the most commonly used modeling method.
Molecular Dynamics Simulation
This algorithm studies the structure and function of proteins by simulating their dynamic behavior. This algorithm can be used to simulate large-scale biomolecular systems such as antibody.
Deep learning algorithms
We use Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), etc., which can be used to learn complex patterns of antibody sequences and structures and improve prediction accuracy.
CD ComputaBio has a team of experts in computational biology and molecular modeling. With years of experience and a deep understanding of antibody structure and function, our team is well-equipped to tackle the most challenging projects. We utilize advanced computational tools and algorithms to deliver accurate and reliable results. We are committed to staying at the forefront of technological advances to ensure the best solution for our clients' needs. Contact us for more information on our services.