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Related Concept Videos

Introduction to Mechanisms of Enzyme Catalysis01:13

Introduction to Mechanisms of Enzyme Catalysis

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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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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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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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Modeling an Enzyme Active Site using Molecular Visualization Freeware
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SubTuner leverages physics-based modeling to complement AI in enzyme engineering toward non-native substrates.

Qianzhen Shao1, Asher C Hollenbeak2, Yaoyukun Jiang1

  • 1Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States.

Chem Catalysis
|July 28, 2025
PubMed
Summary
This summary is machine-generated.

SubTuner, a new computational tool, accelerates enzyme engineering for novel substrates by predicting beneficial enzyme mutants. This physics-based approach outperforms existing methods for expanding enzyme substrate scope.

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Area of Science:

  • Biochemistry
  • Computational Biology
  • Enzyme Engineering

Background:

  • Enzyme engineering aims to create enzymes with novel functions.
  • Identifying enzyme mutants for non-native substrates is challenging.
  • Existing bioinformatics and machine learning tools have limitations.

Purpose of the Study:

  • To develop SubTuner, a physics-based computational tool for identifying enzyme mutants with enhanced activity on non-native substrates.
  • To evaluate SubTuner's accuracy, speed, generalizability, and a priori predictivity.

Main Methods:

  • Developed SubTuner, a physics-based computational tool.
  • Tested SubTuner on anion methyltransferase mutants for non-native S-adenosyl-l-methionine analog synthesis.
  • Performed three tasks involving single-point and multi-point mutants with varying substrates.
  • Combined computational predictions with experimental characterization.

Main Results:

  • SubTuner successfully identified beneficial enzyme mutants for non-native substrates.
  • Demonstrated superior performance in accuracy, speed, and generalizability compared to existing tools.
  • Validated a priori predictivity for bulkier substrates.

Conclusions:

  • SubTuner significantly accelerates enzyme engineering for substrate scope expansion.
  • The tool's physics-based approach offers quantitative accuracy and mechanism insights.
  • SubTuner holds significant potential for advancing enzyme engineering applications.