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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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Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
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Improved cluster ranking in protein-protein docking using a regression approach.

Shahabeddin Sotudian1, Israel T Desta2, Nasser Hashemi1

  • 1Division of Systems Engineering, Boston University, Boston, USA.

Computational and Structural Biotechnology Journal
|May 17, 2021
PubMed
Summary

A new Regression-based Ranking by Pairwise Cluster Comparisons (RRPCC) method improves protein complex ranking. RRPCC enhances the ability to identify high-quality protein complex conformations from docking simulations.

Keywords:
Machine learningProtein dockingRanking

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Area of Science:

  • Computational Biology
  • Structural Biology
  • Bioinformatics

Background:

  • Protein complex modeling is crucial for understanding biological functions.
  • Automated docking programs generate numerous conformational clusters.
  • Ranking these clusters effectively remains a challenge.

Purpose of the Study:

  • To develop and validate a novel method for ranking protein complex clusters.
  • To improve the identification of high-quality protein complex conformations.

Main Methods:

  • Development of Regression-based Ranking by Pairwise Cluster Comparisons (RRPCC).
  • Application of RRPCC to clusters generated by the ClusPro docking server.
  • Utilizing robust regression for pairwise cluster quality prediction.

Main Results:

  • RRPCC demonstrated significant improvements in ranking acceptable or better quality clusters first (24-100%).
  • Ranking medium or better quality clusters first also improved (15-100%).
  • Comparison showed RRPCC performs comparably to other machine learning scoring approaches.

Conclusions:

  • RRPCC offers an effective strategy for ranking protein complex conformations.
  • Internal energy terms from local refinement are important for scoring.
  • Further improvements in sampling and scoring are anticipated.