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Related Concept Videos

Protein Folding01:22

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Overview
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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 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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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 form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
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Learning Protein Embedding to Improve Protein Fold Recognition Using Deep Metric Learning.

Guan-Yu Zhu1, Yan Liu1, Peng-Hao Wang1

  • 1School of Computer Science and Engineering, Nanjing University of Science and Technology, 200 Xiaolingwei, Nanjing 210094, P. R. China.

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Summary

This study introduces NPCFold and NPCFoldpro, novel deep learning models for protein fold recognition. These methods improve prediction accuracy by effectively extracting fold-specific features and integrating protein similarities.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Structural Bioinformatics

Background:

  • Protein fold recognition is crucial for predicting protein structure and function.
  • Deep learning has significantly advanced protein fold recognition.
  • Existing methods may benefit from improved feature extraction and integration.

Purpose of the Study:

  • To develop novel deep learning frameworks for enhanced protein fold recognition.
  • To extract and leverage fold-specific features for improved prediction accuracy.
  • To integrate diverse protein properties for a more robust recognition model.

Main Methods:

  • Proposed NPCFold, a unified deep metric learning framework with a joint loss function.
  • Developed NPCFoldpro, an integrated machine learning model utilizing protein property similarities.
  • Employed benchmark experiments to validate the performance of the proposed methods.

Main Results:

  • Both NPCFold and NPCFoldpro demonstrated superior performance compared to existing methods at the fold level.
  • The proposed strategies of fusing loss functions and features significantly improved fold recognition.
  • The models effectively extracted fold-specific features and integrated protein similarities.

Conclusions:

  • NPCFold and NPCFoldpro represent significant advancements in protein fold recognition.
  • Fusing loss functions and features are effective strategies for improving prediction accuracy.
  • These models offer improved tools for protein structure and function prediction.