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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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The Proteasome02:18

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Eukaryotic cells can degrade proteins through several pathways. One of the most important amongst these is the ubiquitin-proteasome pathway. It helps the cell eliminate the misfolded, damaged, or unwarranted cytoplasmic proteins in a highly specific manner.
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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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The ubiquitin-proteasome pathway is a well-known mechanism utilized by eukaryotic cells to remove cytoplasmic proteins that are misfolded, damaged, or no longer needed. In this pathway, the protein that needs to be eliminated undergoes a process called ubiquitination, where a chain of ubiquitin molecules is attached to the 48th lysine residue of the target protein. This ubiquitin modification helps the proteasome distinguish between a target protein and a healthy protein.
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Updated: Jan 13, 2026

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
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Diseño de sustratos de proteasa guiado por aprendizaje profundo

Carmen Martin-Alonso1,2, Sarah Alamdari3, Tahoura S Samad1

  • 1Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA.

Nature communications
|January 6, 2026
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Resumen

CleaveNet, una línea de producción de IA, diseña sustratos de proteasa de manera eficiente. Esta herramienta acelera el estudio y la aplicación de la actividad de proteasa para diagnósticos y terapéutica.

Palabras clave:
aprendizaje profundodiseño de sustratosproteasasCleaveNetdiagnósticoterapéutica

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Área de la Ciencia:

  • Bioquímica
  • Biología Computacional
  • Enzimología

Sus antecedentes:

  • Las proteasas son enzimas cruciales involucradas en diversos procesos biológicos y enfermedades.
  • La identificación de sustratos de proteasa es vital para comprender la función de la proteasa y desarrollar diagnósticos/terapéuticos.
  • El diseño actual de sustratos está limitado por el vasto espacio de secuencias y la falta de herramientas de alto rendimiento.

Objetivo del estudio:

  • Desarrollar una línea de producción de IA, CleaveNet, para el diseño eficiente y ajustable de sustratos de proteasa.
  • Mejorar la escala y la precisión de la identificación de sustratos de proteasa.
  • Permitir el diseño dirigido de sustratos con perfiles de escisión específicos.

Principales métodos:

  • Se desarrolló CleaveNet, una línea de producción de IA de extremo a extremo para el diseño de sustratos de proteasa.
  • Se aplicó CleaveNet a metaloproteinasas (MMP) para la generación de sustratos.
  • Se incorporó una etiqueta de condicionamiento para la generación controlada de sustratos con perfiles de escisión deseados.
  • Se validaron los sustratos generados por CleaveNet mediante cribado in vitro a gran escala.

Principales resultados:

  • CleaveNet generó con éxito sustratos peptídicos con propiedades biofísicas deseables.
  • Se identificaron motivos de escisión conocidos y novedosos para proteasas diana.
  • Se demostró el diseño exitoso de sustratos altamente selectivos, ejemplificado por MMP13.
  • La validación experimental confirmó la eficacia de los sustratos generados por CleaveNet.

Conclusiones:

  • CleaveNet mejora significativamente la eficiencia, la escala y la ajustabilidad del diseño de sustratos de proteasa.
  • La línea de producción de IA puede capturar motivos de escisión complejos y permitir la generación dirigida de sustratos.
  • CleaveNet muestra una gran promesa para acelerar la investigación y el desarrollo en diagnósticos y terapéuticos basados en proteasas.
  • Este enfoque allana el camino para herramientas de diseño in silico en diversas clases de enzimas.