AIMC Topic: Crystallography

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Generalized biomolecular modeling and design with RoseTTAFold All-Atom.

Science (New York, N.Y.) Apr 19, 2024
Deep-learning methods have revolutionized protein structure prediction and design but are presently limited to protein-only systems. We describe RoseTTAFold All-Atom (RFAA), which combines a residue-based representation of amino acids and DNA bases w...
Proteins DNA Deep Learning Protein Engineering Crystallography Models, Molecular Amino Acids
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Deep learning applications in protein crystallography.

Acta crystallographica. Section A, Foundations and advances Jan 1, 2024
Deep learning techniques can recognize complex patterns in noisy, multidimensional data. In recent years, researchers have started to explore the potential of deep learning in the field of structural biology, including protein crystallography. This f...
Algorithms Crystallography Deep Learning Crystallization Compulsive Behavior
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AlphaFold predictions are valuable hypotheses and accelerate but do not replace experimental structure determination.

Nature methods Nov 30, 2023
Artificial intelligence-based protein structure prediction methods such as AlphaFold have revolutionized structural biology. The accuracies of these predictions vary, however, and they do not take into account ligands, covalent modifications or other...
Artificial Intelligence Mental Processes Protein Conformation Crystallography
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A deep learning solution for crystallographic structure determination.

IUCrJ Jul 1, 2023
The general de novo solution of the crystallographic phase problem is difficult and only possible under certain conditions. This paper develops an initial pathway to a deep learning neural network approach for the phase problem in protein crystallogr...
Neural Networks, Computer Deep Learning Crystallography Proteins Databases, Protein
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Mathematical Geometry and Groups for Low-Symmetry Metal Complex Systems.

Molecules (Basel, Switzerland) Jun 2, 2023
Since chemistry, materials science, and crystallography deal with three-dimensional structures, they use mathematics such as geometry and symmetry. In recent years, the application of topology and mathematics to material design has yielded remarkable...
Mathematics Crystallography Artificial Intelligence
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Accelerating crystal structure determination with iterative AlphaFold prediction.

Acta crystallographica. Section D, Structural biology Feb 27, 2023
Experimental structure determination can be accelerated with artificial intelligence (AI)-based structure-prediction methods such as AlphaFold. Here, an automatic procedure requiring only sequence information and crystallographic data is presented th...
Databases, Protein Models, Structural Crystallography Artificial Intelligence
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Graph-Convolutional Neural Net Model of the Statistical Torsion Profiles for Small Organic Molecules.

Journal of chemical information and modeling Dec 1, 2022
We present a graph-convolutional neural network (GCNN)-based method for learning and prediction of statistical torsional profiles (STP) in small organic molecules based on the experimental X-ray structure data. A specialized GCNN torsion profile mode...
Crystallography Databases, Factual Neural Networks, Computer
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DeepRank: a deep learning framework for data mining 3D protein-protein interfaces.

Nature communications Dec 3, 2021
Three-dimensional (3D) structures of protein complexes provide fundamental information to decipher biological processes at the molecular scale. The vast amount of experimentally and computationally resolved protein-protein interfaces (PPIs) offers th...
Protein Interaction Domains and Motifs Crystallography Datasets as Topic Deep Learning Data Mining Protein Interaction Mapping Protein Interaction Maps Molecular Docking Simulation
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[A giant step towards elucidating the function of proteins].

Medecine sciences : M/S Feb 16, 2021
Protein Structure, Tertiary Myoglobin Protein Folding Proteins Crystallography Algorithms Artificial Intelligence Caspases Deep Learning X-Ray Diffraction Caspase 1
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Pre- and Post-publication Verification for Reproducible Data Mining in Macromolecular Crystallography.

Methods in molecular biology (Clifton, N.J.) Jan 1, 2022
Like an article narrative is deemed by an editor and referees to be worthy of being a version of record on acceptance as a publication, so must the underpinning data also be scrutinized before passing it as a version of record. Indeed without the und...
Crystallography, X-Ray Humans Synchrotrons Artificial Intelligence Data Mining Crystallography COVID-19 Macromolecular Substances SARS-CoV-2
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