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Protein Networks02:26

Protein Networks

4.1K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.1K
Protein-protein Interfaces02:04

Protein-protein Interfaces

12.6K
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...
12.6K
Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

11.2K
Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to...
11.2K
Proteomics01:33

Proteomics

7.8K
A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term...
7.8K
Protein Families02:47

Protein Families

15.6K
Protein families are groups of homologous proteins; that is, they have similarities in amino acid sequences and three-dimensional structures. Protein families usually occur because of gene duplication, where an additional copy of a gene is inserted into the genome of an organism.   Mutations that change the amino acids but still allow the protein to be properly synthesized, will lead to new protein family members.   If these new proteins contain similar amino acids in key...
15.6K
Protein Organization01:24

Protein Organization

6.8K
Proteins are polymers of amino acid residues. They are versatile and responsible for different cellular functions, including DNA replication, molecular transport, catalysis, and structural support. Proteins have a hierarchical structure comprising at least three levels of organization: primary, secondary, and tertiary structure. Some large proteins have a quaternary structure where individual protein subunits are linked together.
The primary structure of a protein is its amino acid sequence....
6.8K

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Video Experimental Relacionado

Updated: Aug 28, 2025

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

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Diseño robusto de secuencias de proteínas basado en aprendizaje profundo utilizando ProteinMPNN

J Dauparas1,2, I Anishchenko1,2, N Bennett1,2,3

  • 1Department of Biochemistry, University of Washington, Seattle, WA, USA.

Science (New York, N.Y.)
|September 15, 2022
PubMed
Resumen
Este resumen es generado por máquina.

Un nuevo método de aprendizaje profundo, ProteinMPNN, sobresale en el diseño de secuencias de proteínas, superando a Rosetta en la recuperación de secuencias. Esta herramienta avanzada rediseñó con éxito varias estructuras de proteínas, incluidas las nanopartículas y las proteínas de unión, validadas por estudios experimentales.

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Área de la Ciencia:

  • Biología computacional
  • Ingeniería de proteínas
  • Aprendizaje profundo

Sus antecedentes:

  • El diseño tradicional de proteínas de novo se basa en métodos basados en la física como Rosetta.
  • El aprendizaje profundo ha transformado la predicción de la estructura de las proteínas, pero aún no el diseño de la secuencia.

Objetivo del estudio:

  • Introducir ProteinMPNN, un método de diseño de secuencias de proteínas basado en el aprendizaje profundo.
  • Demostrar su rendimiento superior en comparación con los métodos existentes.
  • Mostrar su versatilidad a través de diversos desafíos de diseño de proteínas.

Principales métodos:

  • Desarrolló ProteinMPNN, un modelo de aprendizaje profundo para el diseño de secuencias de proteínas.
  • Evalúa el rendimiento en las columnas vertebrales de proteínas nativas, comparando la recuperación de secuencias con Rosetta.
  • Aplicó el método a varias estructuras complejas de proteínas, incluidos monómeros, oligómeros, nanopartículas y proteínas de unión.

Principales resultados:

  • ProteinMPNN logró una recuperación de la secuencia del 52,4% en columnas vertebrales nativas, significativamente mayor que el 32,9% de Rosetta.
  • El método permite el diseño de secuencias acopladas a través de cadenas únicas y múltiples.
  • Rescató con éxito diseños previamente fallidos y validó nuevos diseños utilizando cristalografía de rayos X, microscopía criolectrónica y estudios funcionales.

Conclusiones:

  • ProteinMPNN ofrece un enfoque de aprendizaje profundo poderoso y preciso para el diseño de secuencias de proteínas de novo.
  • Su capacidad para manejar diseños complejos de múltiples cadenas amplía el alcance de la ingeniería de proteínas.
  • La validación experimental confirma la alta utilidad y precisión del método para diversas aplicaciones de diseño de proteínas.