Merging conformational landscapes in a single consensus space with FlexConsensus algorithm

Structural heterogeneity analysis in cryogenic electron microscopy is experiencing a breakthrough in estimating more accurate, richer and interpretable conformational landscapes derived from experimental data. The emergence of new methods designed to tackle the heterogeneity challenge reflects this...

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Vydáno v:Nature methods Ročník 22; číslo 10; s. 2118
Hlavní autoři: Herreros, David, Perez Mata, Carlos, Sanchez Sorzano, Carlos Oscar, Carazo, Jose Maria
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States 01.10.2025
Témata:
Algorithms
Cryoelectron Microscopy - methods
Neural Networks, Computer
Protein Conformation
Proteins - chemistry
Reproducibility of Results
ISSN:1548-7105, 1548-7105
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Shrnutí:Structural heterogeneity analysis in cryogenic electron microscopy is experiencing a breakthrough in estimating more accurate, richer and interpretable conformational landscapes derived from experimental data. The emergence of new methods designed to tackle the heterogeneity challenge reflects this new paradigm, enabling users to gain a better understanding of protein dynamics. However, the question of how intrinsically different heterogeneity algorithms compare remains unsolved, which is crucial for determining the reliability, stability and correctness of the estimated conformational landscapes. Here, to overcome the previous challenge, we introduce FlexConsenus: a multi-autoencoder neural network able to learn the commonalities and differences among several conformational landscapes, enabling them to be placed in a shared consensus space with enhanced reliability. The consensus space enables the measurement of reproducibility in heterogeneity estimations, allowing users to either focus their analysis on particles with a stable estimation of their structural variability or concentrate on specific particle subsets detected by only certain methods.
Bibliografie:ObjectType-Article-1
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ISSN:1548-7105
1548-7105
DOI:10.1038/s41592-025-02841-w