AIMC Topic: Crystallography, X-Ray

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De novo protein design by deep network hallucination.

Nature Dec 1, 2021
There has been considerable recent progress in protein structure prediction using deep neural networks to predict inter-residue distances from amino acid sequences. Here we investigate whether the information captured by such networks is sufficiently...
Amino Acid Sequence Crystallography, X-Ray Protein Conformation Hallucinations Humans Neural Networks, Computer Proteins
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MANORAA: A machine learning platform to guide protein-ligand design by anchors and influential distances.

Structure (London, England : 1993) Oct 5, 2021
The MANORAA platform uses structure-based approaches to provide information on drug design originally derived from mapping tens of thousands of amino acids on a grid. In-depth analyses of the pockets, frequently occurring atoms, influential distances...
Crystallography, X-Ray Trimethoprim Tetrahydrofolate Dehydrogenase Models, Molecular Protein Domains Ligands COVID-19 Humans SARS-CoV-2 Machine Learning Drug Design Pandemics Proteins Databases, Protein Protein Binding Computational Biology
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Machine learning-based real-time object locator/evaluator for cryo-EM data collection.

Communications biology Sep 7, 2021
In cryo-electron microscopy (cryo-EM) data collection, locating a target object is error-prone. Here, we present a machine learning-based approach with a real-time object locator named yoneoLocr using YOLO, a well-known object detection system. Imple...
Machine Learning Algorithms Image Processing, Computer-Assisted Data Collection Crystallography, X-Ray Cryoelectron Microscopy
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Robotic sample changers for macromolecular X-ray crystallography and biological small-angle X-ray scattering at the National Synchrotron Light Source II.

Journal of synchrotron radiation Aug 23, 2021
Here we present two robotic sample changers integrated into the experimental stations for the macromolecular crystallography (MX) beamlines AMX and FMX, and the biological small-angle scattering (bioSAXS) beamline LiX. They enable fully automated una...
Robotics Equipment Design Macromolecular Substances Software Synchrotrons Crystallography, X-Ray X-Rays Scattering, Small Angle
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Accurate prediction of protein structures and interactions using a three-track neural network.

Science (New York, N.Y.) Jul 15, 2021
DeepMind presented notably accurate predictions at the recent 14th Critical Assessment of Structure Prediction (CASP14) conference. We explored network architectures that incorporate related ideas and obtained the best performance with a three-track ...
Protein Subunits Sphingosine N-Acyltransferase Amino Acid Sequence ADAM Proteins Crystallography, X-Ray Protein Folding Multiprotein Complexes Protein Conformation Cryoelectron Microscopy Databases, Protein Receptors, G-Protein-Coupled Models, Molecular Deep Learning Membrane Proteins Proteins Neural Networks, Computer Computer Simulation
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Recapitulating the Binding Affinity of Nrf2 for KEAP1 in a Cyclic Heptapeptide, Guided by NMR, X-ray Crystallography, and Machine Learning.

Journal of the American Chemical Society Mar 8, 2021
Macrocycles, including macrocyclic peptides, have shown promise for targeting challenging protein-protein interactions (PPIs). One PPI of high interest is between Kelch-like ECH-Associated Protein-1 (KEAP1) and Nuclear Factor (Erythroid-derived 2)-li...
Peptides Protein Binding Humans Machine Learning NF-E2-Related Factor 2 Mutagenesis, Site-Directed Amino Acid Motifs Cyclization Kelch-Like ECH-Associated Protein 1 Hydrogen Bonding Recombinant Proteins Fluorescence Polarization Structure-Activity Relationship Nuclear Magnetic Resonance, Biomolecular Crystallography, X-Ray
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TopSuite Web Server: A Meta-Suite for Deep-Learning-Based Protein Structure and Quality Prediction.

Journal of chemical information and modeling Jan 19, 2021
Proteins carry out the most fundamental processes of life such as cellular metabolism, regulation, and communication. Understanding these processes at a molecular level requires knowledge of their three-dimensional structures. Experimental techniques...
Proteins Crystallography, X-Ray Protein Conformation Neural Networks, Computer Software Deep Learning
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Predicting protein model correctness in Coot using machine learning.

Acta crystallographica. Section D, Structural biology Jul 27, 2020
Manually identifying and correcting errors in protein models can be a slow process, but improvements in validation tools and automated model-building software can contribute to reducing this burden. This article presents a new correctness score that ...
Models, Molecular Crystallography, X-Ray Protein Conformation Machine Learning Software Proteins
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Need for Cross-Validation of Single Particle Cryo-EM.

Journal of chemical information and modeling Feb 26, 2020
Cross-validation is used to determine the validity of a model on unseen data by assessing if the model is overfitted to noise. It is widely used in many fields, from artificial intelligence to structural biology in X-ray crystallography and nuclear m...
Cryoelectron Microscopy Artificial Intelligence Signal-To-Noise Ratio Crystallography, X-Ray
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Integrated structural modeling and super-resolution imaging resolve GPCR oligomers.

Progress in molecular biology and translational science Dec 6, 2019
Formation of G protein-coupled receptors (GPCRs) dimers and higher order oligomers represents a key mechanism in pleiotropic signaling, yet how individual protomers function within oligomers remains poorly understood. For the Class A/rhodopsin subfam...
Protein Structure, Secondary Protein Multimerization Allosteric Site Fluorescence Resonance Energy Transfer Crystallography, X-Ray Protein Interaction Mapping Protein Binding Computational Biology Support Vector Machine Software Humans Molecular Dynamics Simulation Neural Networks, Computer Algorithms Ligands Machine Learning Animals Receptors, G-Protein-Coupled Mutation
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