Protein Motion Insights Boost Drug Design Potential

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Researchers at Arizona State University's School of Molecular Sciences have developed a new computational method that can rapidly map the slow, complex motions of proteins. By harnessing the power of ASU's 'Sol' supercomputer, the team can now observe meaningful protein shape changes in less than a day, a process that previously took weeks or months. This breakthrough could lead to the design of more dynamic and effective protein-based drugs, as well as a better understanding of allosteric effects that are crucial for targeted therapies.

Why it matters

Understanding the dynamic behavior of proteins is crucial for drug discovery and design. Most current drug candidates are based on rigid protein structures, but nature's proteins are highly flexible and undergo complex conformational changes that enable their diverse functions. By mapping these protein motions, researchers can design more effective drugs that can fine-tune protein behavior and overcome challenges like antibiotic resistance.

The details

The research team, led by Associate Professor Matthias Heyden, developed a method that can identify the slow, sweeping motions of proteins from short computer simulations lasting only billionths of a second. This approach allows them to map a protein's 'landscape' - where it prefers to linger, where it resists change, and how much energy it takes to shift between different shapes. The key is harnessing the natural fluctuations caused by molecular collisions, which reveal the protein's underlying 'rhythms' and preferred pathways of motion.

• The research was recently published in the journal Science Advances.
• The team's work was supported by grants from the National Science Foundation and the National Institutes of Health.

What they’re saying

What’s next

The researchers plan to use their new method to generate large datasets that can be used to train advanced machine learning models, further expanding the understanding of the relationship between protein sequence, structure, and dynamics. This could lead to even faster and more accurate protein design for drug discovery.