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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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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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Induced-fit Model01:13

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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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Binding sites linkages can regulate a protein's function.  For example, enzyme activity is often regulated through a feedback mechanism where the end product of the biochemical process serves as an inhibitor.
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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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Un modelo basado en la evolución para el diseño de enzimas chorismato mutasa

William P Russ1, Matteo Figliuzzi2, Christian Stocker3

  • 1University of Texas Southwestern Medical Center, Dallas, TX, USA.

Science (New York, N.Y.)
|July 25, 2020
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Resumen

Los científicos desarrollaron un método para diseñar nuevas proteínas utilizando datos evolutivos. Este enfoque permite la creación de enzimas con funciones similares a las naturales y una gran diversidad de secuencias, allanando el camino para el diseño de proteínas artificiales.

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Área de la Ciencia:

  • La bioquímica
  • Ingeniería de proteínas
  • Biología computacional

Sus antecedentes:

  • El diseño racional de las enzimas es crucial para la investigación fundamental y las aplicaciones prácticas.
  • Los métodos existentes a menudo luchan con la complejidad de las relaciones de función de la secuencia de proteínas.

Objetivo del estudio:

  • Desarrollar un proceso general para el diseño basado en la evolución de proteínas artificiales.
  • Aprender las restricciones de especificación de proteínas directamente de los datos de la secuencia evolutiva.

Principales métodos:

  • Aprender las restricciones de la secuencia de proteínas de los datos evolutivos.
  • Diseñar y sintetizar bibliotecas de genes sintéticos.
  • Prueba de las bibliotecas de genes in vivo mediante ensayos cuantitativos de complementación.
  • Aplicando el proceso a la corismato mutasa, una enzima en la biosíntesis de aminoácidos aromáticos.

Principales resultados:

  • Demostró el diseño de una función catalítica similar a la natural con una diversidad sustancial de secuencias para la corismato mutasa.
  • Mostró que los modelos estadísticos basados en secuencias pueden especificar proteínas funcionales.
  • Optimizado el modelo generativo para la función dentro de un contexto genómico específico.
  • Se confirmó el acceso a un enorme espacio de secuencias de proteínas funcionales.

Conclusiones:

  • Los datos de secuencia evolutiva son suficientes para especificar las proteínas.
  • El proceso desarrollado proporciona una base para el diseño general de proteínas artificiales.
  • Este trabajo abre nuevas vías para la ingeniería de proteínas con las funciones deseadas.