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Catalytic Domain

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Evolutionary insights into the active-site structures of the metallo-β-lactamase superfamily from a classification study with support vector machine.

Journal of biological inorganic chemistry : JBIC : a publication of the Society of Biological Inorganic Chemistry 32945939
The metallo-β-lactamase (MβL) superfamily, which is intriguing due to its enzyme promiscuity, is a good model enzyme superfamily for studies of catalytic function evolution. Our previous study traced the evolution of the phosphotriesterase activity o...
beta-Lactamases Catalytic Domain Computational Biology Evolution, Molecular Kinetics Models, Molecular Support Vector Machine
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Profiling SARS-CoV-2 Main Protease (M) Binding to Repurposed Drugs Using Molecular Dynamics Simulations in Classical and Neural Network-Trained Force Fields.

ACS combinatorial science 33119257
The current COVID-19 pandemic caused by a novel coronavirus SARS-CoV-2 urgently calls for a working therapeutic. Here, we report a computation-based workflow for efficiently selecting a subset of FDA-approved drugs that can potentially bind to the SA...
Antiviral Agents Catalytic Domain Coronavirus 3C Proteases Coronavirus Protease Inhibitors Drug Discovery Drug Repositioning Molecular Dynamics Simulation Neural Networks, Computer Protein Binding Workflow
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Machine learning-based prediction of enzyme substrate scope: Application to bacterial nitrilases.

Proteins 33118210
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...
Aminohydrolases Bacterial Proteins Catalytic Domain Chemical Phenomena Ligands Machine Learning Molecular Docking Simulation Nitriles Protein Binding
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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 34124259
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...
Antiviral Agents Biological Products Catalytic Domain Coronavirus 3C Proteases COVID-19 Drug Treatment Drug Design Gene Expression Humans Kinetics Molecular Docking Simulation Neural Networks, Computer Protease Inhibitors Protein Binding Protein Interaction Domains and Motifs Protein Structure, Secondary SARS-CoV-2 Small Molecule Libraries Substrate Specificity Thermodynamics
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Accelerating Drug Design against Novel Proteins Using Deep Learning.

Journal of chemical information and modeling 33491455
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...
Catalytic Domain Deep Learning Drug Design Humans Ligands Proteins
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Machine learning differentiates enzymatic and non-enzymatic metals in proteins.

Nature communications 34140507
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...
Algorithms Binding Sites Catalysis Catalytic Domain Databases, Protein Enzymes Machine Learning Metalloproteins Metals Models, Molecular Static Electricity
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Machine learning reveals sequence-function relationships in family 7 glycoside hydrolases.

The Journal of biological chemistry 34216620
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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De novo design of luciferases using deep learning.

Nature 36813896
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...
Biocatalysis Catalytic Domain Deep Learning Enzyme Stability Hot Temperature Luciferases Luciferins Luminescence Oxidation-Reduction Substrate Specificity
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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 37057817
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...
Allosteric Regulation Allosteric Site Catalytic Domain Deep Learning Molecular Dynamics Simulation Proteins
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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 37399293
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...
Amino Acid Sequence Amino Acids Catalytic Domain Neural Networks, Computer Proteins
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