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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.
Enzyme deficiencies can often translate into life-threatening diseases. For example, a genetic abnormality resulting in the deficiency of the enzyme G6PD...
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Enzyme Kinetics01:19

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Enzymes speed up reactions by lowering the activation energy of the reactants. The speed at which the enzyme turns reactants into products is called the rate of reaction. Several factors impact the rate of reaction, including the number of available reactants. Enzyme kinetics is the study of how an enzyme changes the rate of a reaction.
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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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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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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.’
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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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Related Experiment Video

Updated: Jul 26, 2025

Defining Substrate Specificities for Lipase and Phospholipase Candidates
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In-depth Sequence-Function Characterization Reveals Multiple Pathways to Enhance Enzymatic Activity.

Vikas D Trivedi1, Todd C Chappell1, Naveen B Krishna2

  • 1Department of Chemical and Biological Engineering, Tufts University, Medford, USA 02155.

ACS Catalysis
|June 16, 2023
PubMed
Summary
This summary is machine-generated.

Deep mutational scanning (DMS) revealed key mutations for improving Phenylalanine ammonia-lyase (PAL) enzyme engineering. Computational analysis identified mechanisms like stabilizing intermediates and enhancing substrate diffusion for better enzyme activity.

Keywords:
PALPKUQM/MMdeep mutational scanningdirected evolutionmolecular dynamicsphenylalanine ammonia-lyasephenylketonuria

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Area of Science:

  • Biochemistry and Molecular Biology
  • Enzyme Engineering
  • Protein Science

Background:

  • Deep mutational scanning (DMS) is a powerful tool for protein sequence-function studies.
  • Phenylalanine ammonia-lyase (PAL) has applications in synthesis, agriculture, and medicine, notably in treating Phenylketonuria (PKU).
  • Rational engineering of PAL enzymes is hindered by unknown sequence determinants and catalytic cycle limitations.

Purpose of the Study:

  • To demonstrate DMS as a guide for enzyme engineering and improving catalytic cycles.
  • To create a detailed sequence-function landscape for *Anabaena variabilis* PAL (AvPAL*).
  • To identify mutations enhancing AvPAL* activity and understand their mechanistic basis.

Main Methods:

  • Deep mutational scanning (DMS) to map protein sequence-function relationships.
  • Single- and multi-site saturation mutagenesis guided by DMS fitness data.
  • Quantum Mechanics/Molecular Mechanics (QM/MM) and Molecular Dynamics (MD) simulations for mechanistic insights.

Main Results:

  • A sequence-function landscape of AvPAL* revealing 112 beneficial mutations at 79 sites.
  • Identification of mutation combinations enhancing enzyme kinetics in vitro and in vivo.
  • Mechanistic understanding of beneficial mutations, including stabilization of transition states and improved substrate/product dynamics.

Conclusions:

  • DMS effectively guides enzyme engineering by identifying beneficial mutations.
  • Combined DMS and computational analysis elucidates mechanisms of enzyme improvement.
  • This approach enhances enzyme activity through diverse strategies like stabilizing intermediates and optimizing active site dynamics.