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Pharmacogenetics of Drug Targets: β₂-Adrenergic Receptors, Apo E, Thymidylate Synthase01:11

Pharmacogenetics of Drug Targets: β₂-Adrenergic Receptors, Apo E, Thymidylate Synthase

Genetic polymorphisms in drug targets have emerged as critical determinants of interindividual variability in drug response and toxicity. Pharmacogenomic investigations increasingly focus on identifying these variations to personalize and optimize therapeutic interventions. A drug target may be a receptor, enzyme, or signaling protein involved in pharmacologic responses or disease-related pathways. While early pharmacogenetic studies focused primarily on drug metabolism, current research...
Pharmacogenetics of Phase II Enzymes: N-acetyltransferase, Thiopurine S-methyltransferase, UDP-glucuronosyltransferase01:27

Pharmacogenetics of Phase II Enzymes: N-acetyltransferase, Thiopurine S-methyltransferase, UDP-glucuronosyltransferase

Phase II biotransformation reactions are essential for detoxifying and eliminating xenobiotics, including many pharmaceutical compounds. These reactions typically involve conjugation, the covalent attachment of polar endogenous groups such as glucuronic acid, sulfate, methyl, or acetyl moieties to functional groups introduced during Phase I metabolism. The resulting conjugates are more water-soluble, enabling efficient renal or biliary excretion.The major classes of Phase II enzymes include...
Regioselectivity of Electrophilic Additions-Peroxide Effect02:35

Regioselectivity of Electrophilic Additions-Peroxide Effect

In the presence of organic peroxides, the addition of hydrogen bromide to an alkene yields the isomer that is not predicted by Markovnikov’s rule. For example, the addition of hydrogen bromide to 2-methylpropene in the presence of peroxides gives 1-bromo-2-methylpropane. This addition reaction proceeds via a free radical mechanism, which reverses the regioselectivity. The free radical reaction mechanism involves three stages: initiation, propagation, and termination.
Regioselectivity of Electrophilic Additions to Alkenes: Markovnikov's Rule02:17

Regioselectivity of Electrophilic Additions to Alkenes: Markovnikov's Rule

If a set of reactants can yield multiple constitutional isomers, but one of the isomers is obtained as the major product, the reaction is said to be regioselective. In such reactions, bond formation or breaking is favored at one reaction site over others.
The hydrohalogenation of an unsymmetrical alkene can yield two haloalkane products, depending on which vinylic carbon takes up the halogen. However, one product usually predominates, where hydrogen adds to the vinylic carbon bearing the...
Pharmacogenetics of Phase I Enzymes: Cytochrome P450 Isozymes01:28

Pharmacogenetics of Phase I Enzymes: Cytochrome P450 Isozymes

Cytochrome P450 (CYP450) enzymes are a superfamily of heme-containing monooxygenases that play a pivotal role in Phase I drug metabolism by catalyzing oxidation and reduction reactions.These enzymes transform lipophilic xenobiotics into more hydrophilic metabolites, facilitating subsequent Phase II conjugation and eventual excretion. The CYP450 family is classified into families (e.g., CYP1–CYP3) and subfamilies (e.g., CYP2A, CYP2C), based on amino acid sequence homology.CYP450 isoenzymes,...
Predicting Reaction Outcomes02:24

Predicting Reaction Outcomes

Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...

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Related Experiment Video

Updated: May 31, 2026

Biosynthesis of a Flavonol from a Flavanone by Establishing a One-pot Bienzymatic Cascade
09:50

Biosynthesis of a Flavonol from a Flavanone by Establishing a One-pot Bienzymatic Cascade

Published on: August 14, 2019

Predicting Flavonoid UGT Regioselectivity.

Rhydon Jackson1, Debra Knisley, Cecilia McIntosh

  • 1Technology Division, Momentx Corporation, Plano, TX 75024-3106, USA.

Advances in Bioinformatics
|July 13, 2011
PubMed
Summary
This summary is machine-generated.

Machine learning accurately predicts flavonoid UGT regioselectivity using novel residue indices and time series analysis. This approach improves upon standard sequence alignment for protein classification.

More Related Videos

Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics
08:09

Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics

Published on: June 17, 2012

Related Experiment Videos

Last Updated: May 31, 2026

Biosynthesis of a Flavonol from a Flavanone by Establishing a One-pot Bienzymatic Cascade
09:50

Biosynthesis of a Flavonol from a Flavanone by Establishing a One-pot Bienzymatic Cascade

Published on: August 14, 2019

Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics
08:09

Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics

Published on: June 17, 2012

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Machine Learning

Background:

  • Predicting enzyme regioselectivity is crucial for understanding biological pathways.
  • Flavonoid UDP-glucuronosyltransferases (UGTs) play a key role in plant secondary metabolism.

Purpose of the Study:

  • To develop and evaluate machine learning models for predicting flavonoid UGT acceptor regioselectivity from primary amino acid sequences.
  • To explore novel sequence-derived features for improved protein classification.

Main Methods:

  • Proposed novel graphical residue indices to characterize protein sequences.
  • Modeled UGT subsequences linked to regioselectivity using these indices.
  • Applied time series distance functions within nearest neighbor and support vector machine classifiers.
  • Utilized Bayesian neural networks for classification.

Main Results:

  • The novel indices effectively clustered residues and improved classification performance.
  • Machine learning models incorporating index sequences outperformed standard sequence alignment methods.
  • Identified strong correlations between specific UGT subsequences and acceptor regioselectivity.

Conclusions:

  • Machine learning, particularly with novel sequence indices and time series analysis, offers a powerful approach for predicting enzyme regioselectivity.
  • This method enhances our ability to classify proteins and understand enzyme function.