Biophysical Society Thematic Meeting | Trieste 2024

Emerging Theoretical Approaches to Complement Single-Particle Cryo-EM

Poster Abstracts

28-POS Board 28 REFINEMENT A PYTHON PACKAGE FOR CRYO-EM IMAGE-TEMPLATE LIKELIHOOD ENSEMBLE Wai Shing Tang 1,2 ; Aaditya Rangan 3 ; Pilar Cossio 1,2 ; 1 Flatiron Institute, Center of Computational Mathematics, New York, NY, USA 2 Flatiron Institute, Center of Computational Biology, New York, NY, USA 3 New York University, Courant Institute of Mathematical Sciences, New York, NY, USA Extracting conformational heterogeneity from cryo-electron microscopy (cryo-EM) data is challenging, especially for heterogeneous and flexible samples where 3D classification fails. To tackle this challenge, we previously proposed a Bayesian ensemble refinement approach for estimating the conformational ensemble probability density using structures MD simulations and a set of cryo-EM particle images. Here, we present our recent efforts in applying the ensemble refinement framework on experimental datasets by introducing a computationally efficient algorithm for evaluating the image-to-structure likelihood for large image sets and ensembles. We packaged the algorithm in a user-friendly Python workflow. This work presents a valuable tool for quickly extracting conformational ensemble probabilities from experimental cryo-EM images.

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