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Related Concept Videos

Enzymes02:34

Enzymes

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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.
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Introduction to Enzymes01:22

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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.’
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Catalytically Perfect Enzymes01:07

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The theory of catalytically perfect enzymes was first proposed by W.J. Albery and J. R. Knowles in 1976. These enzymes catalyze biochemical reactions at high-speed. Their catalytic efficiency values range from 108-109 M-1s-1. These enzymes are also called 'diffusion-controlled' as the only rate-limiting step in the catalysis is that of the substrate diffusion into the active site. Examples include triose phosphate isomerase, fumarase, and superoxide dismutase.
 
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Classification of Titrimetric Analysis Based on Reaction Types01:01

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Titrimetric analysis in solution chemistry involves measuring the volume of solutions and is often called volumetric analysis. The standard solution of known concentration in the burette is called the titrant, whereas the solution of unknown concentration in the flask is called the analyte, or titrand. Titrimetric analyses can be classified into four types based on the reactions between the titrant and analyte.
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Classification of Systems-I01:26

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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
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Introduction to Enzyme Kinetics01:19

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Enzyme kinetics studies the rates of biochemical reactions. Scientists monitor the reaction rates for a particular enzymatic reaction at various substrate concentrations. Additional trials with inhibitors or other molecules that affect the reaction rate may also be performed.
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Multi-enzyme Screening Using a High-throughput Genetic Enzyme Screening System
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A hierarchical deep learning based approach for multi-functional enzyme classification.

Kinaan Aamir Khan1, Safyan Aman Memon1, Hammad Naveed1

  • 1Computational Biology Research Lab, National University of Computer and Emerging Sciences, Islamabad, Pakistan.

Protein Science : a Publication of the Protein Society
|June 12, 2021
PubMed
Summary
This summary is machine-generated.

Computational methods can now predict the specific functions of multi-functional enzymes. This approach accurately identifies enzyme roles up to the fourth level of the Enzyme Commission (EC) hierarchy, saving time and resources.

Keywords:
assisted learningenzyme function predictionhierarchical classificationisoformsmulti-functional enzyme

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Area of Science:

  • Biochemistry and Bioinformatics
  • Enzymology
  • Computational Biology

Background:

  • Enzymes are vital proteins catalyzing essential biological reactions.
  • Experimental determination of enzyme function is resource-intensive.
  • Multi-functional enzymes offer efficiency by performing diverse roles.

Purpose of the Study:

  • To develop a computational method for predicting multi-functional enzyme functions.
  • To achieve high specificity in enzyme function prediction, reaching the fourth level of the Enzyme Commission (EC) hierarchy.

Main Methods:

  • Utilized a dataset of 2,583 multi-functional enzymes for training and validation.
  • Developed a predictive model capable of hierarchical classification.
  • Tested model robustness on a dataset of multi-functional enzyme isoforms.

Main Results:

  • Achieved 71.4% hierarchical subset accuracy at the fourth EC level.
  • Obtained a Macro F1 Score of 96.1% at the fourth EC level.
  • Demonstrated broad applicability and robustness of the prediction method.

Conclusions:

  • The proposed computational approach accurately predicts multi-functional enzyme roles to a high level of specificity.
  • This method offers a more efficient alternative to traditional experimental techniques.
  • The tool has the potential to accelerate enzyme discovery and application in various industries.