AIMC Topic: Crystallography, X-Ray

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Facing the phase problem.

IUCrJ Sep 1, 2023
The marvel of X-ray crystallography is the beauty and precision of the atomic structures deduced from diffraction patterns. Since these patterns record only amplitudes, phases for the diffracted waves must also be evaluated for systematic structure d...
Artificial Intelligence Crystallography, X-Ray Explosive Agents Genomics Databases, Protein
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The current role and evolution of X-ray crystallography in drug discovery and development.

Expert opinion on drug discovery Aug 17, 2023
INTRODUCTION: Macromolecular X-ray crystallography and cryo-EM are currently the primary techniques used to determine the three-dimensional structures of proteins, nucleic acids, and viruses. Structural information has been critical to drug discovery...
Humans Computational Biology Proteins Artificial Intelligence Drug Discovery Crystallography, X-Ray
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AlphaFold and the future of structural biology.

Acta crystallographica. Section D, Structural biology Jul 1, 2023
This editorial acknowledges the transformative impact of new machine-learning methods, such as the use of AlphaFold, but also makes the case for the continuing need for experimental structural biology.
Crystallography, X-Ray Machine Learning Protein Conformation Biology Cryoelectron Microscopy
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AlphaFold and the future of structural biology.

IUCrJ Jul 1, 2023
This editorial acknowledges the transformative impact of new machine-learning methods, such as the use of AlphaFold, but also makes the case for the continuing need for experimental structural biology.
Cryoelectron Microscopy Machine Learning Biology Crystallography, X-Ray
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Best Practices of Using AI-Based Models in Crystallography and Their Impact in Structural Biology.

Journal of chemical information and modeling Jun 12, 2023
The recent breakthrough made in the field of three-dimensional (3D) structure prediction by artificial intelligence softwares, such as initially AlphaFold2 (AF2) and RosettaFold (RF) and more recently large Language Models (LLM), has revolutionized t...
Crystallography, X-Ray Software Biology Artificial Intelligence
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High-Throughput Screening to Obtain Crystal Hits for Protein Crystallography.

Journal of visualized experiments : JoVE Mar 10, 2023
X-ray crystallography is the most commonly employed technique to discern macromolecular structures, but the crucial step of crystallizing a protein into an ordered lattice amenable to diffraction remains challenging. The crystallization of biomolecul...
Proteins Artificial Intelligence High-Throughput Screening Assays Reproducibility of Results 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...
Neural Networks, Computer Crystallography, X-Ray Water Membrane Proteins Chemical Phenomena
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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...
Deep Learning Proteins Crystallography, X-Ray Amino Acid Sequence Protein Engineering Protein Conformation Cryoelectron Microscopy
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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 Crystallography, X-Ray Protein Engineering Amino Acid Sequence Models, Molecular Protein Domains Computer Simulation Deep Learning
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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...
Binding Sites Support Vector Machine Cyclin-Dependent Kinase 2 Structure-Activity Relationship Protein Conformation Ligands Crystallography, X-Ray Machine Learning Molecular Docking Simulation Protein Binding
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