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Related Concept Videos

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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Molecular Chaperones and Protein Folding03:00

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The native conformation of a protein is formed by interactions between the side chains of its constituent amino acids. When the amino acids cannot form these interactions, the protein cannot fold by itself and needs chaperones. Notably, chaperones do not relay any additional information required for the folding of polypeptides; the native conformation of a protein is determined solely by its amino acid sequence. Chaperones catalyze protein folding without being a part of the folded protein.
The...
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Amyloid Fibrils03:03

Amyloid Fibrils

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Amyloid fibrils are aggregates of misfolded proteins.  Under most circumstances, misfolded proteins are either refolded by chaperone proteins or degraded by the proteasome. However, in the case of a mutation or a disease, these proteins can accumulate to form large clusters and often further assemble to form elongated fibers, called fibrils. 
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Protein Folding Quality Check in the RER01:29

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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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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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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.
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A Protocol for Computer-Based Protein Structure and Function Prediction
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Predicting absolute protein folding stability using generative models.

Matteo Cagiada1, Sergey Ovchinnikov2, Kresten Lindorff-Larsen1

  • 1Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark.

Protein Science : a Publication of the Protein Society
|December 14, 2024
PubMed
Summary
This summary is machine-generated.

Predicting absolute protein stability is now more feasible using a generative model for protein sequences. This new method achieves high accuracy for small to medium proteins, aiding future protein design and stability studies.

Keywords:
machine learningprotein foldingprotein stabilitythermodynamics

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Area of Science:

  • Computational biology
  • Protein engineering
  • Biophysics

Background:

  • Predicting changes in protein stability from amino acid substitutions is advancing, but predicting absolute protein stability remains challenging.
  • Existing methods often struggle with accuracy when determining the intrinsic stability of a protein.

Purpose of the Study:

  • To develop and validate a generative model for predicting the absolute stability of proteins.
  • To assess the model's performance across a diverse set of natural proteins.

Main Methods:

  • Utilized a generative model for protein sequences to predict absolute protein stability.
  • Benchmarked prediction accuracy on a range of natural, small- to medium-sized proteins (up to ~150 amino acids).

Main Results:

  • Achieved a mean error of 1.5 kcal/mol for absolute stability predictions.
  • Obtained a correlation coefficient of 0.7 for absolute stability predictions.
  • Demonstrated model effectiveness on various natural protein sequences.

Conclusions:

  • Generative models offer a promising approach for predicting absolute protein stability.
  • The developed model provides a simple, accessible tool for protein stability assessment.
  • Future work may extend this approach to predict conformational free energies.