New Experimental Method Reveals Protein Energy Landscapes on Larger Scale - News Center

Proteins fluctuate among multiple conformational states that differ in energy and population, including low-energy native folded forms, higher-energy unfolded forms, and excited states that affect function, interactions, and aggregation. Determining the energies of these conformations and how common or rare they are has been difficult because high-energy states are transient and rare, and existing experimental and AI approaches have limited ability to capture them. Small sequence changes from mutations or protein engineering can strongly shift the populations of conformational states. Earlier approaches could study proteins individually, while computational simulations exist but experimental measurement at scale has lagged. A new experimental method enables parallel analysis across tens or hundreds of proteins, illuminating protein dynamics on a large scale and supporting data-driven modeling, biology, and protein engineering.

"“Proteins move around between different structures but understanding what the energies of those different conformations are and how rare or common those conformations is totally unknown for most proteins. This study was really about developing a new method that let us illuminate all these different dynamics of proteins on a large scale for the first time,” said Gabriel Rocklin, PhD, assistant professor of Pharmacology, who was senior author of the study."

"“Nearly all biological processes rely on the folding of proteins, from conducting electrical signals in nerve cells to inducing immune responses throughout the body. All folded proteins fluctuate between different conformational states, including a low-energy native folded state, a higher-energy unfolded state, and other excited states at different energy levels that can influence protein function, interactions and aggregation.”"

"“Historically, studying conformational fluctuations in protein energy landscapes has been challenging. High-energy states are rare and transient, making them difficult with current experimental methods, and current AI methods have limited ability to predict them. Additionally, even small changes to a protein's sequence due to a mutation or intentional protein engineering can cause large changes in the populations of different conformational states.”"

"“Previously, we could study one protein at a time, but we couldn't look at tens or hundreds of these proteins to analyze protein dynamics in parallel. We have computational tools like molecular dynamics simulations to model how these proteins are behaving, but experimental tools have lagged in their ability to measure them at scale,” said Állan R"

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