MIT researchers have developed a computer model that could improve a class of drugs based on antibodies. The model can predict structural changes in an antibody that will improve its effectiveness. The team already has used the model to create a new version of cetuximab, a drug commonly used to treat colorectal cancer, that binds to its target with 10 times greater affinity than the original molecule.
Antibodies are part of our defense system against pathogens, and are often used for diagnostics and therapeutics. Starting with a specific antibody, the MIT model looks at possible amino-acid substitutions that could occur in the antibody. It then calculates which substitutions would result in a structure that would form a stronger interaction with the target.
Protein modeling can reduce the cost of developing antibody-based drugs, and enable design of additional protein-based products such as enzymes for the conversion of biomass to fuel. The new computational method can predict which changes in an antibody will make it work better, allowing chemists to focus their efforts on the most promising candidates.
The computational approach can quickly calculate a huge number of possible antibody variants and conformations, and predict the molecules' binding affinity for their targets based on the interactions that occur between atoms. Researchers can predict the effectiveness of mutations that might never arise by natural evolution.