AIMC Topic: Catalytic Domain

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Machine Learning-Enhanced Quantum Chemistry-Assisted Refinement of the Active Site Structure of Metalloproteins.

Inorganic chemistry May 28, 2024
Understanding the fine structural details of inhibitor binding at the active site of metalloenzymes can have a profound impact on the rational drug design targeted to this broad class of biomolecules. Structural techniques such as NMR, cryo-EM, and X...
Humans Catalytic Domain Matrix Metalloproteinase 12 Metalloproteins Models, Molecular Quantum Theory Machine Learning
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De novo design of protein structure and function with RFdiffusion.

Nature Jul 11, 2023
There has been considerable recent progress in designing new proteins using deep-learning methods. Despite this progress, a general deep-learning framework for protein design that enables solution of a wide range of design challenges, including de no...
Proteins Deep Learning Hemagglutinin Glycoproteins, Influenza Virus Catalytic Domain Cryoelectron Microscopy Protein Binding
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A Highly Sensitive Model Based on Graph Neural Networks for Enzyme Key Catalytic Residue Prediction.

Journal of chemical information and modeling Jul 3, 2023
Determining the catalytic site of enzymes is a great help for understanding the relationship between protein sequence, structure, and function, which provides the basis and targets for designing, modifying, and enhancing enzyme activity. The unique l...
Neural Networks, Computer Amino Acid Sequence Catalytic Domain Proteins Amino Acids
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Research and Evaluation of the Allosteric Protein-Specific Force Field Based on a Pre-Training Deep Learning Model.

Journal of chemical information and modeling Apr 14, 2023
Allosteric modulators are important regulation elements that bind the allosteric site beyond the active site, leading to the changes in dynamic and/or thermodynamic properties of the protein. Allosteric modulators have been a considerable interest as...
Deep Learning Proteins Molecular Dynamics Simulation Allosteric Regulation Catalytic Domain Allosteric Site
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De novo design of luciferases using deep learning.

Nature Feb 22, 2023
De novo enzyme design has sought to introduce active sites and substrate-binding pockets that are predicted to catalyse a reaction of interest into geometrically compatible native scaffolds, but has been limited by a lack of suitable protein structur...
Catalytic Domain Enzyme Stability Hot Temperature Luciferases Biocatalysis Substrate Specificity Luciferins Oxidation-Reduction Luminescence Deep Learning
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Machine learning reveals sequence-function relationships in family 7 glycoside hydrolases.

The Journal of biological chemistry Jul 1, 2021
Family 7 glycoside hydrolases (GH7) are among the principal enzymes for cellulose degradation in nature and industrially. These enzymes are often bimodular, including a catalytic domain and carbohydrate-binding module (CBM) attached via a flexible li...
Catalytic Domain Glycoside Hydrolases Machine Learning
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Machine learning differentiates enzymatic and non-enzymatic metals in proteins.

Nature communications Jun 17, 2021
Metalloenzymes are 40% of all enzymes and can perform all seven classes of enzyme reactions. Because of the physicochemical similarities between the active sites of metalloenzymes and inactive metal binding sites, it is challenging to differentiate b...
Enzymes Metalloproteins Static Electricity Binding Sites Metals Machine Learning Databases, Protein Catalysis Algorithms Models, Molecular Catalytic Domain
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Active Learning and the Potential of Neural Networks Accelerate Molecular Screening for the Design of a New Molecule Effective against SARS-CoV-2.

BioMed research international May 25, 2021
A global pandemic has emerged following the appearance of the new severe acute respiratory virus whose official name is the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), strongly affecting the health sector as well as the world econom...
Protein Structure, Secondary Protein Interaction Domains and Motifs Coronavirus 3C Proteases Thermodynamics Molecular Docking Simulation Protease Inhibitors Drug Design Small Molecule Libraries Catalytic Domain Antiviral Agents Substrate Specificity Humans Neural Networks, Computer Gene Expression Kinetics Protein Binding SARS-CoV-2 COVID-19 Drug Treatment Biological Products
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Accelerating Drug Design against Novel Proteins Using Deep Learning.

Journal of chemical information and modeling Jan 25, 2021
In the world plagued by the emergence of new diseases, it is essential that we accelerate the drug design process to develop new therapeutics against them. In recent years, deep learning-based methods have shown some success in ligand-based drug desi...
Humans Drug Design Ligands Catalytic Domain Deep Learning Proteins
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Machine learning-based prediction of enzyme substrate scope: Application to bacterial nitrilases.

Proteins Nov 10, 2020
Predicting the range of substrates accepted by an enzyme from its amino acid sequence is challenging. Although sequence- and structure-based annotation approaches are often accurate for predicting broad categories of substrate specificity, they gener...
Chemical Phenomena Protein Binding Aminohydrolases Machine Learning Catalytic Domain Nitriles Bacterial Proteins Molecular Docking Simulation Ligands
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