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Related Concept Videos

Antifungal Agents01:15

Antifungal Agents

Amphotericin B is a broad-spectrum antifungal agent that exploits structural differences between fungal and mammalian cell membranes. Its amphipathic structure—featuring a hydrophobic polyene-lactone ring and a hydrophilic region containing mycosamine and carboxylic acid groups—enables selective binding to ergosterol, a sterol predominantly found in fungal plasma membranes. This selective interaction underlies the drug’s antifungal activity, although weak binding to cholesterol contributes to...
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Binding sites linkages can regulate a protein's function.  For example, enzyme activity is often regulated through a feedback mechanism where the end product of the biochemical process serves as an inhibitor.
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Induced-fit Model01:13

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Most chemical reactions in cells require enzymes—biological catalysts that speed up the reaction without being consumed or permanently changed. They reduce the activation energy needed to convert the reactants into products. Enzymes are proteins, that usually work by binding to a substrate—a reactant molecule that they act upon.
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Predicting Products: SN1 vs. SN202:27

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Nucleophilic substitution reactions of alkyl halides can proceed via an SN1 or an SN2 mechanism. While in SN2 reactions, the nucleophile attacks the substrate simultaneously as the leaving group departs, in SN1 reactions, the substrate first dissociates to give the carbocation intermediate. Various factors such as the structure of the substrate, the strength of the nucleophile, and the nature of the solvent promote one mechanism over the other.
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Polymer Classification: Stereospecificity01:26

Polymer Classification: Stereospecificity

Polymerization generates chiral centers along the entire backbone of a polymer chain. Accordingly, the stereochemistry of the substituent group has a significant effect on polymer properties. Polymers formed from monosubstituted alkene monomers feature chiral carbons at every alternate position in the polymer backbone. Relative to the predominant orientation of substituents at the adjacent chiral carbons, the polymer can exist in three different configurations: isotactic, syndiotactic, and...
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Allosteric proteins have more than one ligand binding site; the binding of a ligand to any of these sites influences the binding of ligands to the other sites. When a protein is allosteric, its binding sites are called coupled or linked.  In the case of enzymes, the site that binds to the substrate is known as the active site and the other site is known as the regulatory site. When a ligand binds to the regulatory site, this leads to conformational changes in the protein that can influence the...

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Updated: May 25, 2026

Defining Substrate Specificities for Lipase and Phospholipase Candidates
08:59

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Published on: November 23, 2016

Predicting substrate specificity in fungal type III polyketide synthases.

Nika Sokolova1, Stepan S Denisov2, Kristina Haslinger1

  • 1Department of Chemical and Pharmaceutical Biology, University of Groningen, Groningen, the Netherlands.

Methods in Enzymology
|May 23, 2026
PubMed
Summary

This study introduces a machine learning workflow to predict the substrate scope of fungal Type III polyketide synthases (T3PKSs). This tool aids in biocatalysis and understanding enzyme function.

Keywords:
FungiMachine leaningPromiscuous enzymesSubstrate scopeType III polyketide synthases

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Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Published on: January 26, 2024

Area of Science:

  • Biochemistry
  • Computational Biology
  • Enzymology

Background:

  • Type III polyketide synthases (T3PKSs) are crucial enzymes producing diverse natural products.
  • Their substrate promiscuity contributes to a broad product scope, important for ecological and clinical applications.
  • Predicting T3PKS substrate scope is key for biocatalysis and understanding biological roles.

Purpose of the Study:

  • To present a machine learning-based workflow for predicting substrate specificity in fungal T3PKSs.
  • To provide protocols for predicting T3PKS substrate scope and selecting enzymes for specific transformations.
  • To offer guidance on retraining the model with custom enzymatic activity data.

Main Methods:

  • Development of a machine learning workflow tailored for fungal T3PKSs.
  • Implementation of step-by-step protocols for substrate scope prediction.
  • Inclusion of instructions for model retraining and figure generation.

Main Results:

  • A functional machine learning workflow for predicting T3PKS substrate specificity.
  • Protocols enabling prediction of new T3PKS substrate scopes.
  • Guidance on selecting T3PKSs for targeted substrate transformations.

Conclusions:

  • The developed workflow facilitates accurate prediction of T3PKS substrate specificity.
  • This approach supports the application of T3PKSs in biocatalysis.
  • The workflow aids in elucidating the biological functions of T3PKSs.