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Conserved Binding Sites01:49

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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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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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A Protocol for Computer-Based Protein Structure and Function Prediction
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Ranking near-native candidate protein structures via random forest classification.

Hongjie Wu1, Hongmei Huang1, Weizhong Lu2

  • 1School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, 215009, China.

BMC Bioinformatics
|December 26, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a new random forest classification method to accurately rank protein structure predictions. The method improves upon existing techniques for identifying near-native protein candidates from large decoy sets.

Keywords:
Protein structural predictionRandom forestSPICKER

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Area of Science:

  • Computational Biology
  • Structural Bioinformatics
  • Biophysics

Background:

  • Ab initio protein structure prediction generates numerous decoys requiring selection of top candidates.
  • Clustering methods often identify near-native structures but struggle with precise ordering.
  • Limitations in distance assessments hinder accurate ranking of protein decoys.

Purpose of the Study:

  • To develop an improved method for re-ranking protein structure decoys.
  • To enhance the accuracy of identifying top-ranked near-native protein structures.
  • To overcome limitations of current clustering and distance assessment methods.

Main Methods:

  • Utilized random forest classification for re-ranking protein structure decoys.
  • Incorporated intra- and inter-cluster features from clustering results.
  • Transformed the re-ranking problem into a binary classification task.

Main Results:

  • The proposed method demonstrated superior performance in ordering candidate structures compared to SPICKR, Calibur, and Durandal.
  • Achieved higher accuracy in identifying the top-ranked model closer to the native structure.
  • Showcased improved identification of near-native structures in multiple test cases.

Conclusions:

  • Presented an enhanced random forest classification method for protein structure decoy re-ranking.
  • The method effectively addresses the challenge of ordering candidate protein structures.
  • Results indicate the proposed approach outperforms existing methods in accuracy and effectiveness.