J.M. Carazo

Astex-Faro improves cryo-EM hit identification by detecting structural deviations and enhancing weak features across diverse datasets.

ReconSIREN is a deep learning framework that improves CryoEM map accuracy by addressing preferred orientation errors in heterogeneous samples.

AlignPCA-2D improves 2D classification in cryo-EM by using PCA-reduced Euclidean vector alignment, achieving competitive accuracy with lower computational cost.

This study compares extracellular vesicles (EVs) and mechanically induced vesicles (MVs) for membrane protein structure determination, finding that MVs provide better structural representation of HER2 in native environments.

This study determines the structure of TbAQP2 bound to drugs and glycerol, revealing how mutations in the channel lead to drug resistance in Trypanosoma brucei.

The paper introduces FlexConsensus, an algorithm that merges conformational landscapes from different CryoEM heterogeneity methods into a shared consensus space to improve reliability and reproducibility.

The paper investigates using p-values to estimate the number of distinguishable classes in cryoEM data by testing if classifications differ from random partitions.

The paper presents a cryo-EM conformational heterogeneity challenge to evaluate and improve methods for analyzing molecular motions in heterogeneous samples.

HetSIREN is a deep learning method for CryoEM conformational state reconstruction and refinement, improving structural accuracy by decoupling pose and CTF information from conformational landscapes.

The paper investigates the structural and functional impact of the S:A222V mutation in SARS-CoV-2, showing increased RBD opening and ACE2 binding without reducing neutralization capacity.

DeepEMhancer improves cryo-EM map quality using deep learning, achieving cleaner and more detailed maps on test cases like SARS-CoV-2 RNA polymerase.

MicrographCleaner uses deep learning to improve cryo-EM micrograph preprocessing by identifying suitable regions for particle picking.

The paper improves a marker-free image alignment method for electron tomography to enhance 3D reconstruction quality by addressing deformations during acquisition.

This work introduces a soft-alignment validation method to assess alignment precision, accuracy, and data homogeneity in cryo-EM single particle analysis, improving map resolution through data quality enhancement.