Cryo–electron microscopy (cryo-EM) is an experimental technique that produces high-resolution images of proteins. Using these images to recover a single 3D protein structure has been extremely successful. However, proteins are inherently flexible--the arrangement of atoms in 3D space, called a molecular conformation, is constantly changing. Recovering the entire probability distributions of conformations from cryo-EM data is a challenging inverse problem due to extreme noise levels and unknown imaging parameters. In this talk, I analyze ensemble reweighting, a recently introduced Bayesian approach that adjusts probabilities of conformations generated from molecular simulations to match experimental cryo-EM images. Using sensitivity analysis on the non-parametric maximum likelihood estimation problem, I identify when uninformative, noisy images can be discarded without losing information about rare conformations, thereby reducing the computational cost.

Emily Almgren is an applied mathematics Ph.D. student at Cornell University focusing on numerical analysis with chemistry applications. She has a B.S. in Mathematics from Haverford College.

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