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Related Concept Videos

Enzymes02:34

Enzymes

Inside living organisms, enzymes act as catalysts for many biochemical reactions involved in cellular metabolism. The role of enzymes is to reduce the activation energies of biochemical reactions by forming complexes with its substrates. The lowering of activation energies favor an increase in the rates of biochemical reactions.
Enzyme deficiencies can often translate into life-threatening diseases. For example, a genetic abnormality resulting in the deficiency of the enzyme G6PD...
Molecular Models02:00

Molecular Models

Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.
Induced-fit Model01:13

Induced-fit Model

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.
Enzymes exhibit substrate specificity, meaning that they can only bind to certain substrates. This is mainly determined by the shape and chemical characteristics of...
Turnover Number and Catalytic Efficiency01:19

Turnover Number and Catalytic Efficiency

The turnover number of an enzyme is the maximum number of substrate molecules it can transform per unit time. Turnover numbers for most enzymes range from 1 to 1000 molecules per second. Catalase has the known highest turnover number, capable of converting up to 2.8×106 molecules of hydrogen peroxide into water and oxygen per second. Lysozyme has the lowest known turnover number of half a molecule per second.
Chymotrypsin is a pancreatic enzyme that breaks down proteins during digestion. The...
Introduction to Enzymes01:22

Introduction to Enzymes

The use of enzymes by humans dates to 7000 BCE. Humans first used enzymes to ferment sugars and produce alcohol without knowing that this was an enzyme-catalyzed reaction. Wilhelm Kuhne coined the term 'enzyme' in 1877 from the Greek words ‘en’ meaning ‘in’ or ‘within’ and ‘zyme’ meaning ‘yeast.’
Most enzymes are proteins that speed up biochemical reactions without being consumed. Enzymes contain one or more active sites that bind the substrates and convert them into products. Many enzymes also...
Introduction To Enzymes01:22

Introduction To Enzymes

The use of enzymes by humans dates to 7000 BCE. Humans first used enzymes to ferment sugars and produce alcohol without knowing that this was an enzyme-catalyzed reaction. Wilhelm Kuhne coined the term 'enzyme' in 1877 from the Greek words ‘en’ meaning ‘in’ or ‘within’ and ‘zyme’ meaning ‘yeast.’
Most enzymes are proteins that speed up biochemical reactions without being consumed. Enzymes contain one or more active sites that bind the substrates and convert them into products. Many enzymes also...

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Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web
09:51

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Enzymes/non-enzymes classification model complexity based on composition, sequence, 3D and topological indices.

Cristian Robert Munteanu1, Humberto González-Díaz, Alexandre L Magalhães

  • 1REQUIMTE/Faculty of Science, Chemistry Department, University of Porto, Porto 4169-007, Portugal. muntisa@gmail.com

Journal of Theoretical Biology
|July 9, 2008
PubMed
Summary

This study compares various protein descriptors for enzyme classification, finding no direct link between model complexity and accuracy. Different methods and data types yield varied results in distinguishing enzymes from non-enzymes.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Structural Bioinformatics

Background:

  • Rapid characterization of enzyme activity requires effective theoretical models.
  • Protein descriptors range from simple sequence-based indices to complex 3D structures.
  • Previous studies have not systematically compared the discriminatory power of different descriptor types for enzyme classification.

Purpose of the Study:

  • To compare the effectiveness of various protein descriptors (1D, 2D, 3D) in classifying proteins as enzymes or non-enzymes.
  • To assess the relationship between descriptor complexity, model accuracy, and computational methods.
  • To evaluate the utility of topological indices and 3D descriptors for enzyme/non-enzyme discrimination.

Main Methods:

  • Analysis of 966 proteins (enzymes and non-enzymes) using PDB/DSSP files.
  • Utilized Python/Biopython scripts, STATISTICA, and Weka for data processing.
  • Calculated various indices: composition, sequence, topological (TIs), 3D, mixed (composition-sequence, 3D-composition), and secondary structure-based.
  • Extended and applied topological indices to protein sequence star graphs using the Sequence to Star Graph (S2SG) application.
  • Employed general discriminant analysis (GDA), neural networks (NN), and machine learning (ML) models.

Main Results:

  • Compared the discriminatory power of 1D, 2D, and 3D protein descriptors for enzyme classification.
  • Evaluated model accuracy against descriptor complexity and Shannon's information entropy.
  • Demonstrated that no direct correlation exists between model complexity and accuracy.
  • Showcased the performance of different indices and machine learning methods in enzyme/non-enzyme discrimination.

Conclusions:

  • The study provides a comprehensive comparison of different protein descriptor types for enzyme classification.
  • Findings indicate that simpler descriptors can be as effective as complex ones, depending on the chosen method.
  • Highlights the importance of selecting appropriate descriptors and methods for accurate enzyme activity prediction.