Metcalf - XSD - Pham, Hoai Ahn - 2.24.26

Improving AI-based RNA Structure Models with MD Simulations and SAXS Data

AI-based 3D structure prediction for RNA has lagged behind that for proteins, largely due to the limited availability of high-resolution RNA structures and the intrinsic conformational heterogeneity of RNA. To address this challenge, we are generating large-scale dynamic datasets by integrating molecular dynamics (MD) simulations with X-ray scattering measurements for a diverse set of RNA molecules. These datasets will be used to train and validate AI models, with the goal of improving RNA structure and ensemble predictions.

The summer student will work with other team members and will primarily focus on performing MD simulations of RNA molecules, extracting dynamic formation, calculating SAXS profiles, and contributing to the training of AI-based prediction models. The project will leverage the computational resources at the Argonne Leadership Computing Facility (ALCF) and will also provide opportunities to use the Advanced Photon Source (APS) to experimentally characterize RNA dynamics.

Education and Experience Requirements

• The entirety of the appointment must be conducted within the United States.
• Must be 18 years or older at the time the appointment begins.
• Applicants must be:

• Currently enrolled in undergraduate or graduate studies at an accredited institution.
• Graduated from an accredited institution within the past 3 months; or
• Actively enrolled in a graduate program at an accredited institution.

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