KU awarded $1.5 million NSF grant

LAWRENCE-- Ilya Vakser, Professor and Director with the Computational Biology Program and Center for Computational Biology, and Professor with the Department of Molecular Biosciences at KU is leading a new NSF-funded project on modeling molecular systems using AI.

The 3-year $1.5 million collaborative grant is held in tandem with Stony Brook University and Stowers Institute. By splitting the funding between these 3 teams the project combines highly complementary areas of expertise on structure-based modeling of molecular interactions, theoretical foundation of reinforcement learning (a major direction in AI) and experimental approaches to protein aggregation.

The grant, "Modeling of large macromolecular systems at extra-long time scales using AI”, will allow Vakser and his colleagues, to expand scientific knowledge of protein structures and their assembly in the crowded cell-like environments. According to Vakser "This research is essential for scientists to better understand the work of the molecular machines that enable biological processes, allowing for the increased ability to modulate these processes when appropriate."

The research will generate the energy landscape of the macromolecular system as the foundation of the simulation approach, which samples the landscape in space and time. The accuracy of the energy landscape will be significantly improved by application of the AI-based predictions. The modeling protocol will be radically advanced to a new AI-driven paradigm. Instead of explicitly simulating the entire trajectory of the system, the project will put forward AI methods that learn from the early segments of simulations and from the system parameters to model the long-term behavior. The emergent protein behavior will be investigated for the eye lens proteome. Crystallin protein oligomerization competes with aggregation and how it is autonomously regulated is poorly understood.

The project puts forward a hypothesis that the lens crystallins likely operate through an entropic buffering mechanism. The aggregates promote their own growth by excluding volume that would otherwise be accessible to monomers, which creates an entropic drive for further aggregation. The modeling approach, combined with wet-lab experiments, will provide a unique opportunity to test this hypothesis and to explore fundamental biology of protein homeostasis.

The grant will involve graduate and undergraduate level students in projects that apply AI to key problems in molecular biology. "This education experience will allow for better career prospects in the current time of technological revolution," Vakser said.

According to Vakser "The research will lead to the radical expansion of the existing approaches to modeling macromolecular processes in cells by incorporating the AI techniques, to address extra-long simulation time scales at the atomic resolution, with application to fundamental problems of emergent protein behavior. Thus the project will open up an uncharted territory for research, with unprecedented opportunities for life sciences that will have significant impacts on medicine, biotechnology, agriculture, and beyond."