WikiSysop: Created page with "== Citation == Dingeldein, L., Silva-Sánchez, D., Evans, L., D’Imprima, E., Grigorieff, N., Covino, R. and Cossio, P. 2025. Amortized template matching of molecular conformations from cryoelectron microscopy images using simulation-based inference. Proceedings of the National Academy of Sciences. 122, 23 (2025), e2420158122. == Abstract == Characterizing the conformational ensemble of biomolecular systems is key to understand their functions. Cryoelectron microscop..."

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Created page with "== Citation == Dingeldein, L., Silva-Sánchez, D., Evans, L., D’Imprima, E., Grigorieff, N., Covino, R. and Cossio, P. 2025. Amortized template matching of molecular conformations from cryoelectron microscopy images using simulation-based inference. Proceedings of the National Academy of Sciences. 122, 23 (2025), e2420158122. == Abstract == Characterizing the conformational ensemble of biomolecular systems is key to understand their functions. Cryoelectron microscop..."

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== Citation ==

Dingeldein, L., Silva-Sánchez, D., Evans, L., D’Imprima, E., Grigorieff, N., Covino, R. and Cossio, P. 2025. Amortized template matching of molecular conformations from cryoelectron microscopy images using simulation-based inference. Proceedings of the National Academy of Sciences. 122, 23 (2025), e2420158122.

== Abstract ==

Characterizing the conformational ensemble of biomolecular systems is key to understand
their functions. Cryoelectron microscopy (cryo-EM) captures two-dimensional
snapshots of biomolecular ensembles, giving in principle access to thermodynamics.
However, these images are very noisy and show projections of the molecule in unknown
orientations, making it very difficult to identify the biomolecule’s conformation
in each individual image. Here, we introduce cryo-EM simulation-based inference
(cryoSBI) to infer the conformations of biomolecules and the uncertainties associated
with the inference from individual cryo-EM images. CryoSBI builds on simulationbased
inference, a merger of physics-based simulations and probabilistic deep learning,
allowing us to use Bayesian inference even when likelihoods are too expensive to
calculate. We begin with an ensemble of conformations, templates from experiments,
and molecular modeling, serving as structural hypotheses. We train a neural network
approximating the Bayesian posterior using simulated images from these templates
and then use it to accurately infer the conformation of the biomolecule from each
experimental image. Training is only done once on simulations, and after that, it
takes just a few milliseconds to make inference on an image, making cryoSBI suitable
for arbitrarily large datasets and direct analysis on micrographs. CryoSBI eliminates
the need to estimate particle pose and imaging parameters, significantly enhancing
the computational speed compared to explicit likelihood methods. Importantly, we
obtain interpretable machine learning models by integrating physics-based approaches
with deep neural networks, ensuring that our results are transparent and reliable.
We illustrate and benchmark cryoSBI on synthetic data and showcase its promise on
experimental single-particle cryo-EM data.

== Keywords ==

== Links ==

https://www.pnas.org/doi/abs/10.1073/pnas.2420158122

== Related software ==

== Related methods ==

== Comments ==

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