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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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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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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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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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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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Conservation of Protein Domains Over Different Proteins02:26

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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 WISDOM: A Workbench for In silico De novo Design of BioMolecules
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Constraint Guided Beta-Sheet Refinement for Protein Structure Prediction.

M A Hakim Newton1, Rianon Zaman2, Fereshteh Mataeimoghadam2

  • 1School of Information and Physical Sciences, The University of Newcastle, Australia.

Computational Biology and Chemistry
|October 1, 2022
PubMed
Summary
This summary is machine-generated.

This study refines protein structure prediction by enhancing beta-sheet accuracy using novel local search methods. The improved protein structure prediction (PSP) approach significantly outperforms existing algorithms.

Keywords:
Backbone anglesMachine learningNeighbour generationProtein structure predictionSearch-based optimisation

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Structural Biology

Background:

  • Protein structure prediction (PSP) is vital for drug discovery and understanding biological functions.
  • Accurate prediction of protein beta-sheet structures is a challenging but critical step in PSP.
  • Non-local interactions and complex geometries in beta-sheets hinder precise prediction.

Purpose of the Study:

  • To refine protein beta-sheet structure prediction using advanced local search techniques.
  • To improve the overall accuracy of protein structure prediction by focusing on key structural elements.
  • To develop a novel method that overcomes limitations in predicting complex protein structures.

Main Methods:

  • Utilized a local search method to specifically refine protein beta-sheet structures.
  • Employed a secondary local search method for full protein structure refinement.
  • Integrated residue-residue distance scores and deep learning-derived geometric constraints.
  • Implemented a strategy to identify and modify low-scoring conformational regions.

Main Results:

  • Achieved significant performance improvements on a standard dataset of 88 proteins.
  • Demonstrated over 12% enhancement in average root mean squared distance (RMSD), Template Modelling score (TM-score), and Global Distance Test (GDT) values.
  • Outperformed state-of-the-art protein structure prediction search algorithms.

Conclusions:

  • The proposed local search methods effectively refine protein beta-sheet structures and overall protein conformations.
  • This approach offers a substantial advancement in protein structure prediction accuracy.
  • The method shows promise for accelerating drug discovery and other bioinformatics research areas.