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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 Organization01:13

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Protein Folding01:25

Protein Folding

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Proteins are chains of amino acids linked together by peptide bonds. Upon synthesis, a protein folds into a three-dimensional conformation, critical to its biological function. Interactions between its constituent amino acids guide protein folding, and hence the protein structure is primarily dependent on its amino acid sequence.
Protein Structure Is Critical to Its Biological Function
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Protein Folding01:22

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Protein and Protein Structure02:15

Protein and Protein Structure

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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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Conserved Binding Sites01:49

Conserved Binding Sites

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Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...
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A Protocol for Computer-Based Protein Structure and Function Prediction
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A Protocol for Computer-Based Protein Structure and Function Prediction

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Toward the solution of the protein structure prediction problem.

Robin Pearce1, Yang Zhang2

  • 1Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA.

The Journal of Biological Chemistry
|June 13, 2021
PubMed
Summary

Deep learning has revolutionized protein structure prediction, largely solving the problem without relying on existing protein databases. This breakthrough enables accurate protein fold prediction for most single-domain proteins, independent of template availability.

Keywords:
contact mapdeep learningdistance predictionend-to-end structure predictionfree modelingmultiple sequence alignmentprotein structure predictiontemplate-based modeling;

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Area of Science:

  • Structural Biology
  • Computational Biology
  • Bioinformatics

Background:

  • The protein structure prediction problem, determining 3D atomic coordinates from amino acid sequences, has been a long-standing challenge since 1973.
  • Traditional methods relied on template-based modeling (TBM) using homologous structures from the Protein Data Bank (PDB), with accuracy declining significantly without templates.

Purpose of the Study:

  • To assess the impact of end-to-end deep machine learning on protein structure prediction.
  • To evaluate the performance of deep learning methods in relation to template availability and sequence homology.

Main Methods:

  • Utilized end-to-end deep machine learning techniques for protein structure prediction.
  • Evaluated model quality against the availability and quality of template structures and the number of detected sequence homologs.

Main Results:

  • Deep learning models achieved high accuracy in predicting correct folds for nearly all single-domain proteins without using PDB templates.
  • Model quality showed minimal correlation with template structure quality or the number of identified sequence homologs.

Conclusions:

  • Deep learning has overcome the traditional limitations between template-based modeling (TBM) and template-free modeling (FM).
  • High-resolution protein structure prediction is now significantly less dependent on the availability of templates in the PDB library.