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Related Concept Videos

Protein Organization01:24

Protein Organization

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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 and Protein Structures02:15

Protein and Protein Structures

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Protein and Protein Structure02:15

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Proteins are one of the most abundant organic molecules in living systems and have the most diverse range of functions of all macromolecules. Proteins may be structural, regulatory, contractile, or protective. They may serve in transport, storage, or membranes; or they may be toxins or enzymes. Their structures, like their functions, vary greatly. They are all, however, amino acid polymers arranged in a linear sequence.
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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.
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Evolutionary Relationships through Genome Comparisons02:54

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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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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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Updated: Oct 7, 2025

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins
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From systems to structure - using genetic data to model protein structures.

Hannes Braberg1,2, Ignacia Echeverria1,2,3, Robyn M Kaake1,2,4

  • 1Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA.

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New computational methods, including deep learning, are revolutionizing our understanding of genetic variation. These approaches model protein structures and interactions, advancing systems biology and mechanistic insights.

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Area of Science:

  • Systems Biology
  • Genomics
  • Structural Biology

Background:

  • Understanding genetic variation requires analyzing sequence changes at both system-wide and mechanistic scales.
  • Protein interaction networks and genetic interaction networks are crucial for a systems view.
  • Traditionally, biophysical methods were needed to understand molecular mechanisms underlying protein interactions.

Purpose of the Study:

  • To review emerging methods for modeling protein structures and interactions.
  • To discuss the integration of diverse structural data sources.
  • To highlight the role of large-scale genetic datasets and deep learning.

Main Methods:

  • Coevolution analysis
  • Deep mutational scanning
  • Genome-scale genetic and chemical-genetic interaction mapping
  • Deep learning approaches

Main Results:

  • Emerging computational approaches enable modeling of protein structures and complexes.
  • Large-scale genetic datasets combined with deep learning offer new ways to study protein interactions.
  • Integration of structural data from various sources is becoming increasingly feasible.

Conclusions:

  • Computational methods are transforming the study of protein structure and interactions.
  • Deep learning and large-scale genetic data analysis provide powerful tools for systems biology.
  • Future research will likely focus on integrating diverse data for comprehensive structural and functional modeling.