AIMC Topic: Cryoelectron Microscopy

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Dimeric gold nanoparticles enable multiplexed labeling in cryoelectron tomography.

Proceedings of the National Academy of Sciences of the United States of America
Cryoelectron tomography (cryo-ET) enables three-dimensional visualization of molecular structures within tissue and intact cells, providing a powerful tool for studying the spatial organization of biological components at nanometer resolution. Realiz...

From sequence to scaffold: Computational design of protein nanoparticle vaccines from AlphaFold2-predicted building blocks.

Proceedings of the National Academy of Sciences of the United States of America
Self-assembling protein nanoparticles are being increasingly utilized in the design of next-generation vaccines due to their ability to induce antibody responses of superior magnitude, breadth, and durability. Computational protein design offers a ro...

AQuaRef: machine learning accelerated quantum refinement of protein structures.

Nature communications
Cryo-EM and X-ray crystallography provide crucial experimental data for obtaining atomic-detail models of biomacromolecules. Refining these models relies on library-based stereochemical data, which, in addition to being limited to known chemical enti...

cryoTIGER: deep-learning based tilt interpolation generator for enhanced reconstruction in cryo electron tomography.

Communications biology
Cryo-electron tomography enables the visualization of macromolecular complexes within native cellular environments but is limited by incomplete angular sampling and the maximal electron dose that biological specimens can be exposed to. Here, we devel...

Template Learning: Deep learning with domain randomization for particle picking in cryo-electron tomography.

Nature communications
Cryo-electron tomography (cryo-ET) enables three-dimensional visualization of biomolecules and cellular components in their near-native state. A key challenge in cryo-ET data analysis is particle picking, often performed by template matching, which r...

A Machine Learning Assisted Tool and Numerical Model for Analyzing Lipid Nanoparticles.

ACS nano
The transfection potency and biological fate of gene-loaded lipid nanoparticles (LNPs) are often determined by their morphological and physicochemical properties. Cryogenic-electron microscopy (cryo-EM) remains the most effective tool to analyze LNP ...

Discovery of CRISPR-Cas12a clades using a large language model.

Nature communications
CRISPR-Cas systems revolutionize life science. Metagenomes contain millions of unknown Cas proteins. Traditional mining relies on protein sequence alignments. In this work, we employ an evolutionary scale language model (ESM) to learn the information...

Inhibiting heme piracy by pathogenic Escherichia coli using de novo-designed proteins.

Nature communications
Iron is an essential nutrient for most bacteria and is often growth-limiting during infection, due to the host sequestering free iron as part of the innate immune response. To obtain the iron required for growth, many bacterial pathogens encode trans...

MIC: A deep learning tool for assigning ions and waters in cryo-EM and crystal structures.

Nature communications
At sufficiently high resolution, x-ray crystallography and cryogenic electron microscopy are capable of resolving small spherical map features corresponding to either water or ions. Correct classification of these sites provides crucial insight for u...

Amortized template matching of molecular conformations from cryoelectron microscopy images using simulation-based inference.

Proceedings of the National Academy of Sciences of the United States of America
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. How...