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Evolutionary Relationships through Genome Comparisons02:54

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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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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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Related Experiment Video

Updated: Sep 6, 2025

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
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rrQNet: Protein contact map quality estimation by deep evolutionary reconciliation.

Rahmatullah Roche1, Sutanu Bhattacharya2, Md Hossain Shuvo1

  • 1Department of Computer Science, Virginia Tech, Blacksburg, Virginia, USA.

Proteins
|June 25, 2022
PubMed
Summary

rrQNet is a new deep learning method for assessing protein contact map quality. It evaluates evolutionary consistency of residue pairs to accurately predict contact map quality, improving protein structure prediction.

Keywords:
deep learninginter-residue contactsprotein modelingquality estimation

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Area of Science:

  • Computational biology
  • Structural bioinformatics
  • Deep learning

Background:

  • Protein contact maps are crucial for protein structure prediction, notably with AlphaFold2.
  • Current quality assessment methods focus on 3D coordinates, not 2D contact maps.
  • A direct 2D contact map quality estimation method is needed.

Purpose of the Study:

  • To introduce rrQNet, a deep learning method for 2D contact map quality estimation.
  • To develop a method that assesses quality based on evolutionary context.
  • To provide a versatile tool for evaluating protein contact maps.

Main Methods:

  • rrQNet employs a deep neural network with two modules: evolutionary context encoding and reconciliation.
  • Quality scores are estimated at the residue-pair level and aggregated for overall map quality.
  • The model is trained on diverse contact predictors for generalizability.

Main Results:

  • rrQNet accurately reproduces true quality scores of predicted contact maps.
  • The method effectively distinguishes between accurate and inaccurate contact maps from various predictors.
  • rrQNet demonstrates versatility across different resolutions, from residue pairs to full maps.

Conclusions:

  • rrQNet offers a robust deep learning approach for self-assessing protein contact map quality.
  • The method's reliance on evolutionary context provides a novel perspective on quality estimation.
  • rrQNet enhances the reliability of protein structure prediction by enabling accurate contact map evaluation.