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Related Concept Videos

Structural Classification of Joints01:20

Structural Classification of Joints

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Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
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The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses  or amphiarthroses, whereas all synovial joints are classified as diarthroses.
Synarthrosis
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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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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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Atom-ProteinQA: Atom-level protein model quality assessment through fine-grained joint learning.

Yatong Han1, Yingfeng Lu1, Xu Yan1

  • 1Future Network of Intelligence Institute, the Chinese University of Hong Kong (Shenzhen), Shenzhen, 518172, China; School of Science and Engineering, the Chinese University of Hong Kong (Shenzhen), Shenzhen, 518172, China.

Computer Methods and Programs in Biomedicine
|March 27, 2024
PubMed
Summary

This study introduces Atom-ProteinQA, an atom-level protein model quality assessment method. It achieves state-of-the-art performance by integrating geometric and topological atom-level features for improved protein structure evaluation.

Keywords:
3D representation learningGraph neural networkMulti-model learningProtein quality assessment

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Area of Science:

  • Computational Biology
  • Structural Bioinformatics
  • Protein Science

Background:

  • Protein model quality assessment (ProteinQA) is crucial for protein structure refinement and design.
  • Previous methods focused on global or residue-level assessments, overlooking atom-level details.
  • An atom-level perspective offers precise cues for enhanced ProteinQA.

Purpose of the Study:

  • To develop an innovative atom-level ProteinQA model named Atom-ProteinQA.
  • To extract both geometric and topological atom-level relationships for improved assessment.
  • To enhance the accuracy of protein quality evaluation at both atom and residue levels.

Main Methods:

  • Proposed Atom-ProteinQA model with two novel modules: geometric and topological perception.
  • Utilized 3D sparse convolution for capturing atom-level geometric features.
  • Constructed an atom-level graph using chemical bonds and applied message passing for topological features.
  • Integrated features through a cross-model aggregation module.

Main Results:

  • Atom-ProteinQA significantly outperforms existing methods in both residue-level and atom-level assessment.
  • Achieved state-of-the-art results on benchmark datasets: CATH-2084, Decoy-8000, CASP13, CASP14, and CAMEO.
  • Demonstrated the efficacy of integrating fine-grained atom-level features.

Conclusions:

  • Atom-ProteinQA represents a significant advancement in protein model quality assessment.
  • The atom-level approach provides a more comprehensive and accurate evaluation of protein structures.
  • The model's superior performance highlights the importance of fine-grained analysis in structural bioinformatics.