MIT model could improve some drugs' eff...(s to fuel. Lippow conducted the research...)

Making drugs out of huge, complicated molecules like antibodies is incredibly hard, said Janna Wehrle, who oversees computational biology grants at the National Institute of General Medical Sciences, which partially supported the research. Dr. Tidor's 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. This is a perfect example of how modern computing can be harnessed to speed up the development of new drugs.

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.

Using the new approach, researchers can predict the effectiveness of mutations that might never arise by natural evolution.

The work demonstrates that by building on the physics underlying biological molecules, you can engineer improvements in a very precise way, said Tidor.

Expanding on that theme, Wittrup and Tidor also co-teach a class and are writing a textbook focusing on connecting fundamental molecular and cellular events to biological function through the use of mathematical models and computer simulations.

The team also used the model with an anti-lysozyme antibody called D44.1, and they were
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