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Related Concept Videos

Induced-fit Model01:13

Induced-fit Model

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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.
Enzymes exhibit substrate specificity, meaning that they can only bind to certain substrates. This is mainly determined by the shape and chemical...
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Enzymes02:34

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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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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
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Introduction to Mechanisms of Enzyme Catalysis01:13

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For many years, scientists thought that enzyme-substrate binding took place in a simple "lock-and-key" fashion. This model stated that the enzyme and substrate fit together perfectly in one instantaneous step. However, current research supports a more refined view scientists call induced fit. The induced-fit model expands upon the lock-and-key model by describing a more dynamic interaction between enzyme and substrate. As the enzyme and substrate come together, their interaction causes...
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Introduction to Enzymes01:22

Introduction to Enzymes

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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.’
Most enzymes are proteins that speed up biochemical reactions without being consumed. Enzymes contain one or more active sites that...
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Introduction to Enzyme Kinetics01:19

Introduction to Enzyme Kinetics

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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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Updated: Jun 4, 2025

Identification of Kinase-substrate Pairs Using High Throughput Screening
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Deep Learning-Driven Insights into Enzyme-Substrate Interaction Discovery.

Wenjia Qian1, Xiaorui Wang1, Yuansheng Huang1

  • 1College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China.

Journal of Chemical Information and Modeling
|December 25, 2024
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Summary
This summary is machine-generated.

A new machine learning model, the molecule-enzyme interaction (MEI) model, accurately predicts enzyme-substrate relationships. This computational tool enhances enzyme research across various fields, offering superior performance to existing methods.

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Area of Science:

  • Biotechnology and Bioinformatics
  • Computational Chemistry
  • Enzyme Engineering

Background:

  • Enzymes are crucial biological catalysts with broad applications in medicine, chemistry, and biotechnology.
  • Predicting enzyme-substrate interactions, especially for novel molecules, is a significant challenge.
  • Existing computational methods offer efficiency but often lack accuracy compared to experimental approaches.

Purpose of the Study:

  • To develop a highly accurate computational model for predicting molecule-enzyme interactions.
  • To address the limitations of current methods in predicting substrate specificity for enzymes.
  • To create a versatile tool for advancing enzyme research and applications.

Main Methods:

  • Introduction of the molecule-enzyme interaction (MEI) model, a novel machine learning framework.
  • Integration of atomic environmental data and amino acid sequence features using an attention mechanism within a hierarchical neural network.
  • Training and evaluation on a comprehensive dataset of enzymatic reactions and enzyme sequences.

Main Results:

  • The MEI model achieved superior prediction accuracy (at least 6.7% higher) and AUROC (8.5% higher) compared to state-of-the-art models.
  • Demonstrated strong generalization capabilities across datasets of varying quality and size.
  • Successfully applied to predict CYP450 interactions and plastic degradation (90.5% accuracy), showcasing adaptability.

Conclusions:

  • The MEI model represents a significant advancement in accurately predicting enzyme-substrate relationships.
  • Its versatility and high performance make it a foundational tool for enzyme research, with potential applications beyond its initial scope.
  • The model's ability to transfer knowledge effectively enhances its utility in diverse biological and environmental contexts.