AIMC Topic: Crystallography, X-Ray

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Prediction of hydrophilic and hydrophobic hydration structure of protein by neural network optimized using experimental data.

Scientific reports Feb 7, 2023
The hydration structures of proteins, which are necessary for their folding, stability, and functions, were visualized using X-ray and neutron crystallography and transmission electron microscopy. However, complete visualization of hydration structur...
Water Membrane Proteins Chemical Phenomena Crystallography, X-Ray Neural Networks, Computer
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Robust deep learning-based protein sequence design using ProteinMPNN.

Science (New York, N.Y.) Sep 15, 2022
Although deep learning has revolutionized protein structure prediction, almost all experimentally characterized de novo protein designs have been generated using physically based approaches such as Rosetta. Here, we describe a deep learning-based pro...
Amino Acid Sequence Crystallography, X-Ray Protein Conformation Protein Engineering Deep Learning Cryoelectron Microscopy Proteins
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Protein sequence design with a learned potential.

Nature communications Feb 8, 2022
The task of protein sequence design is central to nearly all rational protein engineering problems, and enormous effort has gone into the development of energy functions to guide design. Here, we investigate the capability of a deep neural network mo...
Protein Folding Deep Learning Models, Molecular Protein Domains Amino Acid Sequence Crystallography, X-Ray Protein Engineering Computer Simulation
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Ensemble learning from ensemble docking: revisiting the optimum ensemble size problem.

Scientific reports Jan 10, 2022
Despite considerable advances obtained by applying machine learning approaches in protein-ligand affinity predictions, the incorporation of receptor flexibility has remained an important bottleneck. While ensemble docking has been used widely as a so...
Molecular Docking Simulation Protein Binding Support Vector Machine Structure-Activity Relationship Machine Learning Binding Sites Cyclin-Dependent Kinase 2 Ligands Crystallography, X-Ray Protein Conformation
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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...
Proteins Amino Acid Sequence Crystallography, X-Ray Humans Neural Networks, Computer Protein Conformation Hallucinations
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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...
Tetrahydrofolate Dehydrogenase Databases, Protein Models, Molecular Protein Domains Ligands Crystallography, X-Ray Trimethoprim Protein Binding Computational Biology Proteins COVID-19 SARS-CoV-2 Drug Design Machine Learning Pandemics Humans
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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 Image Processing, Computer-Assisted Algorithms 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...
Software Robotics Equipment Design Macromolecular Substances 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 Cryoelectron Microscopy Protein Folding Multiprotein Complexes ADAM Proteins Crystallography, X-Ray Protein Conformation Neural Networks, Computer Computer Simulation Deep Learning Receptors, G-Protein-Coupled Models, Molecular Amino Acid Sequence Membrane Proteins Proteins Databases, Protein
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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...
Amino Acid Motifs Cyclization Kelch-Like ECH-Associated Protein 1 Crystallography, X-Ray Hydrogen Bonding Fluorescence Polarization Nuclear Magnetic Resonance, Biomolecular NF-E2-Related Factor 2 Mutagenesis, Site-Directed Protein Binding Humans Recombinant Proteins Machine Learning Structure-Activity Relationship Peptides
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