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Related Concept Videos

Protein Organization01:24

Protein Organization

8.0K
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....
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Protein-protein Interfaces02:04

Protein-protein Interfaces

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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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Protein-Protein Interfaces

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Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

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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.
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Protein Networks02:26

Protein Networks

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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,...
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Ligand Binding Sites02:40

Ligand Binding Sites

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Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...
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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

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Structure-based protein design with deep learning.

Sergey Ovchinnikov1, Po-Ssu Huang2

  • 1John Harvard Distinguished Science Fellowship Program, Harvard University, Cambridge, MA, 02138, USA.

Current Opinion in Chemical Biology
|September 21, 2021
PubMed
Summary
This summary is machine-generated.

Deep learning is revolutionizing protein design by integrating diverse data for novel protein structures and functions. This review compares deep learning with traditional methods, highlighting future opportunities in protein engineering.

Keywords:
Deep learningNeural networksProtein designProtein sequence designProtein structureProtein structure design

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Area of Science:

  • Biochemistry and Structural Biology
  • Computational Biology and Bioinformatics
  • Protein Engineering

Background:

  • Proteins function as molecular machines, driving biochemical processes through their complex three-dimensional structures.
  • Understanding protein structure is crucial for deciphering biological mechanisms and has spurred interdisciplinary research.
  • Conventional protein design methods integrate information piece-by-piece, from sequence statistics to atomic-level modeling.

Purpose of the Study:

  • To review the progress of deep learning in protein structure and function design.
  • To compare deep learning approaches with traditional protein design methodologies.
  • To outline current strategies and future opportunities in data-driven protein design.

Main Methods:

  • Review of current literature on protein design, focusing on structure-based modeling and deep learning.
  • Comparative analysis of conventional and deep learning-based design strategies.
  • Exploration of data-driven approaches and their integration into protein design.

Main Results:

  • Deep learning methods offer a transformative approach to protein design, moving beyond piece-by-piece integration of data.
  • Significant advancements have been made in designing proteins with novel functionalities and shapes using computational methods.
  • The integration of deep learning promises to accelerate the discovery of new proteins and biological insights.

Conclusions:

  • Deep learning represents a paradigm shift in protein design, enabling more holistic and data-driven strategies.
  • Future opportunities lie in leveraging deep learning to overcome limitations of conventional methods and unlock new protein capabilities.
  • This review provides a foundation for understanding the evolving landscape of protein engineering through artificial intelligence.