Browse research papers in Structural Biology - Biochemistry, Genetics and Molecular Biology Discover 9 academic papers with AI-powered analysis - Paperzilla

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Biochemistry, Genetics and Molecular Biology

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Structural Biology

Structural Biology

The determination and analysis of three-dimensional structures of biological macromolecules, using techniques like crystallography, NMR, and cryo-EM to understand structure-function relationships and drug design

9 papers in this specialization

Papers

Accurate prediction of protein structures and interactions using a three-track neural network

This paper introduces RoseTTAFold, a three-track neural network that predicts protein structures and interactions with high accuracy, approaching that of DeepMind's AlphaFold2. This network integrates information at the sequence, distance map, and 3D coordinate levels, enabling rapid solutions for X-ray crystallography, cryo-EM modeling, and prediction of protein-protein complex structures directly from sequence information.

Jul 14, 05:20 PM

Highly accurate protein structure prediction with AlphaFold

AlphaFold is a deep learning-based method that predicts protein structures with atomic accuracy, even in cases where no similar structures are known. It achieved high accuracy in the CASP14 assessment, outperforming other methods, and its performance generalizes to a large set of recently released PDB structures.

s41586-021-03819-2.pdf

Jul 14, 05:20 PM

Accurate structure prediction of biomolecular interactions with AlphaFold 3

AlphaFold 3 demonstrates significant improvements in predicting the structure of biomolecular interactions across various categories, including protein-ligand, protein-nucleic acid, and antibody-antigen complexes. The model's updated diffusion-based architecture allows for the prediction of complexes with diverse molecular types, achieving higher accuracy than previous specialized tools in most cases.

s41586-024-07487-w_reference.pdf

Jul 14, 05:19 PM

Structural Biology in the Clouds: The WeNMR-EOSC Ecosystem

The WeNMR project has provided web-based structural biology tools and high-throughput computing resources to researchers for over a decade, transitioning into a Thematic Service within the European Open Science Cloud (EOSC). The ecosystem has served a large international user base, with increased usage during the COVID-19 pandemic, and continues to facilitate research and education in structural biology.

Jul 14, 05:11 PM

Topaz-Denoise: general deep denoising models for cryoEM and cryoET

This paper presents Topaz-Denoise, a general deep-learning method and trained models for removing noise from cryoEM micrographs and cryoET tomograms. The authors demonstrate that denoising with Topaz allows for the identification of particles previously unseen due to low SNR, thus allowing for solving protein structures with more complete particle orientations and identifying new conformations, as well as substantially reducing electron dose and microscope exposure time without sacrificing data quality.

s41467-020-18952-1.pdf

Jul 14, 11:28 AM

Estimation of high-order aberrations and anisotropic magnification from cryo-EM data sets in RELION-3.1

This paper introduces new methods in RELION-3.1 to detect and correct for symmetrical and antisymmetrical optical aberrations and anisotropic magnification in cryo-EM data. Applying these corrections to several publicly available datasets improved the resolution of the 3D reconstructions, especially when these optical effects were prominent.

Jul 14, 11:28 AM

DeepEMhancer: a deep learning solution for cryo-EM volume post-processing

DeepEMhancer, a deep learning-based algorithm, enhances cryo-EM maps by performing masking and sharpening-like operations, leading to improved map quality and easier atomic model building. Tested on 20 different maps, DeepEMhancer demonstrated a median resolution improvement of ~0.6 Å compared to the original maps, outperforming traditional B-factor-based approaches.

s42003-021-02399-1.pdf

Jul 14, 11:28 AM

Isotropic reconstruction for electron tomography with deep learning

IsoNet, a deep learning software package, was developed to enhance the resolution and interpretability of cryo-electron tomography (cryoET) data by iteratively filling in missing information and improving the signal-to-noise ratio. Applied to various cryoET datasets, IsoNet reduced resolution anisotropy and allowed for visualization of previously obscured structural details in viruses, cellular organelles, and neuronal synapses, without needing sub-tomogram averaging.

s41467-022-33957-8.pdf

Jul 14, 11:28 AM

A Bayesian approach to single-particle electron cryo-tomography in RELION-4.0

This paper introduces a new Bayesian approach for subtomogram averaging that operates directly on 2D tilt series images within RELION 4.0. This method eliminates the need for missing wedge correction and offers improved accuracy for subtomogram alignment and classification, demonstrated by achieving resolutions sufficient for de novo atomic modeling in two test cases: the HIV immature capsid and the C. crescentus S-layer.

2022.02.28.482229.full.pdf

Jul 14, 11:28 AM

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