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The Bowman lab devises new ways to interpret genetic variation and combat global health threats by understanding/exploiting protein dynamics using a combination of biophysical experiments, machine learning, physics-based simulations, and the world’s largest distributed computer.

To achieve our goals, we
1) Develop new computational and experimental methods for mapping out the ensemble of structures that a protein adopts instead of settling for a single static snapshot.
2) Understand how proteins function an malfunction in the context of global health threats like Alzheimer’s disease and COVID-19 by iteratively using simulations to gain mechanistic insight, conducting our own experimental tests of our models, and refining our models based on feedback from experiments.
3) Exploit our models to predict the impact of genetic variations on phenotype and to design new proteins and drugs.

To enable the massive calculations underlying our work, we run the Folding@home distributed computing project. This project empowers anyone with a computer and an internet connection to become a citizen scientist and accelerate biomedical research by running simulations on their personal computer(s). At present, over 200K devices are participating in Folding@home, enabling us to run calculations that would be impossible by any other means. We also develop Markov state model (MSM) methods, adaptive sampling algorithms, and machine learning tools to efficiently build maps of protein dynamics and extract valuable insights.

We work with a wide network of collaborators to achieve our ambitious objectives. Here is a word from some of the people and organizations supporting our work on COVID-19.