AIMC Topic: Substrate Specificity

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NNKcat: deep neural network to predict catalytic constants (Kcat) by integrating protein sequence and substrate structure with enhanced data imbalance handling.

Briefings in bioinformatics May 1, 2025
Catalytic constant (Kcat) is to describe the efficiency of catalyzing reactions. The Kcat value of an enzyme-substrate pair indicates the rate an enzyme converts saturated substrates into product during the catalytic process. However, it is challengi...
Algorithms Computational Biology Proteins Amino Acid Sequence Catalysis Neural Networks, Computer Substrate Specificity
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Machine learning-assisted substrate binding pocket engineering based on structural information.

Briefings in bioinformatics Jul 25, 2024
Engineering enzyme-substrate binding pockets is the most efficient approach for modifying catalytic activity, but is limited if the substrate binding sites are indistinct. Here, we developed a 3D convolutional neural network for predicting protein-li...
Protein Binding Models, Molecular Neural Networks, Computer Machine Learning Acid Phosphatase Binding Sites Substrate Specificity Protein Engineering Ligands Bacillus anthracis Klebsiella Bacterial Proteins
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ProsperousPlus: a one-stop and comprehensive platform for accurate protease-specific substrate cleavage prediction and machine-learning model construction.

Briefings in bioinformatics Sep 22, 2023
Proteases contribute to a broad spectrum of cellular functions. Given a relatively limited amount of experimental data, developing accurate sequence-based predictors of substrate cleavage sites facilitates a better understanding of protease functions...
Substrate Specificity Peptide Hydrolases Machine Learning Algorithms
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Computational Phosphorylation Network Reconstruction: An Update on Methods and Resources.

Methods in molecular biology (Clifton, N.J.) Jan 1, 2021
Most proteins undergo some form of modification after translation, and phosphorylation is one of the most relevant and ubiquitous post-translational modifications. The succession of protein phosphorylation and dephosphorylation catalyzed by protein k...
Phosphorylation Protein Kinases Substrate Specificity Phosphoric Monoester Hydrolases Proteome Protein Processing, Post-Translational Neural Networks, Computer
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DeepCleave: a deep learning predictor for caspase and matrix metalloprotease substrates and cleavage sites.

Bioinformatics (Oxford, England) Feb 15, 2020
MOTIVATION: Proteases are enzymes that cleave target substrate proteins by catalyzing the hydrolysis of peptide bonds between specific amino acids. While the functional proteolysis regulated by proteases plays a central role in the 'life and death' c...
Deep Learning Caspases Substrate Specificity Metalloproteases Software
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Survey of Machine Learning Techniques for Prediction of the Isoform Specificity of Cytochrome P450 Substrates.

Current drug metabolism May 22, 2019
BACKGROUND: Determination or prediction of the Absorption, Distribution, Metabolism, and Excretion (ADME) properties of drug candidates and drug-induced toxicity plays crucial roles in drug discovery and development. Metabolism is one of the most com...
Machine Learning Pharmaceutical Preparations Isoenzymes Models, Biological Substrate Specificity
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Self-Organizing Map (SOM) and Support Vector Machine (SVM) Models for the Prediction of Human Epidermal Growth Factor Receptor (EGFR/ ErbB-1) Inhibitors.

Combinatorial chemistry & high throughput screening Jan 1, 2016
EGFR (ErbB-1/HER1) kinase plays an important role in cancer therapy. Two classification models were established to predict whether a compound is an inhibitor or a decoy of human EGFR (ErbR-1) by using Kohonen's self-organizing map (SOM) and support v...
Computing Methodologies Software Molecular Structure Humans Support Vector Machine Binding Sites ErbB Receptors Substrate Specificity
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Alignment-Free Methods for the Detection and Specificity Prediction of Adenylation Domains.

Methods in molecular biology (Clifton, N.J.) Jan 1, 2016
Identifying adenylation domains (A-domains) and their substrate specificity can aid the detection of nonribosomal peptide synthetases (NRPS) at genome/proteome level and allow inferring the structure of oligopeptides with relevant biological activiti...
Protein Structure, Tertiary Peptide Synthases Software Substrate Specificity Bacteria Catalytic Domain Computer Graphics Workflow Proteomics Models, Biological Support Vector Machine
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