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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.
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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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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.
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ER is the primary site for the maturation and folding of soluble and transmembrane secretory proteins. The calnexin cycle is a specific chaperone system that folds and assesses the confirmation of N-glycosylated proteins before they can exit the ER lumen. The primary players of this quality check pipeline are the lectins, ER-resident chaperones, and a glucosyl transferase enzyme. In case the calnexin system in the lumen fails to salvage a misfolded protein, it is transported to the cytoplasm...
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A Protocol for Computer-Based Protein Structure and Function Prediction
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High-accuracy protein structure prediction in CASP14.

Joana Pereira1, Adam J Simpkin2, Marcus D Hartmann1

  • 1Department of Protein Evolution, Max Planck Institute for Developmental Biology, Tübingen, Germany.

Proteins
|July 4, 2021
PubMed
Summary
This summary is machine-generated.

State-of-the-art deep learning, including AlphaFold2 (AF2), has revolutionized protein structure prediction, achieving high accuracy across all targets in CASP14. This advancement highlights the importance of community benchmarking in driving progress in the field.

Keywords:
CASP14high-accuracymolecular replacement

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Area of Science:

  • Computational Biology
  • Structural Biology
  • Artificial Intelligence

Background:

  • Protein structure prediction is crucial for understanding biological function.
  • Previous Critical Assessment of protein Structure Prediction (CASP) assessments have driven progress.
  • Deep learning has emerged as a powerful tool in computational biology.

Purpose of the Study:

  • To evaluate the performance of deep learning methods, specifically AlphaFold2 (AF2), in the CASP14 protein modeling challenge.
  • To assess the utility of AF2-generated models for molecular replacement using AMPLE.
  • To introduce DipDiff, a novel metric for evaluating backbone geometry improvements.

Main Methods:

  • Analysis of models submitted by all groups participating in CASP14 (>=10 targets).
  • Evaluation of AF2 models for molecular replacement with AMPLE.
  • Application of the new DipDiff metric to assess backbone geometry against templates.

Main Results:

  • Deep learning, particularly AF2, significantly expanded the 'high-accuracy' category in CASP14, covering all targets.
  • AF2 models demonstrated utility as molecular replacement search models.
  • The second-best method in CASP14 surpassed the top method from CASP13, indicating rapid field advancement.

Conclusions:

  • Deep learning approaches have dramatically advanced protein structure prediction accuracy.
  • Community-based benchmarking, as exemplified by CASP, is vital for the evolution of protein structure prediction.
  • New metrics like DipDiff are valuable for detailed model assessment.