With the new model prediction of
mutations' effectivness gets easier
“Protein modelling can reduce the cost of developing antibody-based drugs,” says Dane Wittrup, who holds appointments in Massachusettes Institute of Technology's Department of Biological and Chemical Engineering. The developed model can predict structural changes in an antibody that will improve its effectiveness. The team has already used the model to create a new version of cetuximab, a drug commonly used to treat colorectal cancer, that binds to its target with ten times greater affinity than the original molecule.
Traditionally, researchers have developed antibody-based drugs using an evolutionary approach. They remove antibodies from mice and further evolve them in the laboratory, screening for improved efficacy. This can lead to improved binding affinities but the process is time-consuming, and it restricts the control that researchers have over the design of antibodies. In contrast, the MIT 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. Starting with a specific antibody, the MIT model looks at many 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.
Using the new approach, researchers can predict the effectiveness of mutations that might never arise by natural evolution. “Combining information about protein structure with calculations that address the underlying atomic interactions allows us to make rational choices about which changes should be made to a protein to improve its function,” said Shaun Lippow, lead author of the Nature Biotechnology paper.
COMPAMED.de; Source: Massachusetts Institute of Technology