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Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

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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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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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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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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: Nov 6, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

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Improving deep learning-based protein distance prediction in CASP14.

Zhiye Guo1, Tianqi Wu1, Jian Liu1

  • 1Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA.

Bioinformatics (Oxford, England)
|May 7, 2021
PubMed
Summary
This summary is machine-generated.

Researchers developed deep learning models for protein distance prediction, achieving top rankings in CASP14. Their best model outperformed previous methods, highlighting the importance of multiple sequence alignment quality for accurate predictions.

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Area of Science:

  • Computational biology
  • Structural bioinformatics
  • Deep learning applications

Background:

  • Accurate prediction of residue-residue distances is crucial for protein structure prediction.
  • Deep learning methods, particularly deep residual neural networks with channel-wise attention, show promise for this task.
  • Input features derived from multiple sequence alignments (MSAs) are key components for these deep learning models.

Purpose of the Study:

  • To develop and evaluate deep learning-based protein distance predictors for the Critical Assessment of Protein Structure Prediction (CASP14).
  • To investigate the impact of different MSA generation strategies and deep learning configurations on prediction accuracy.
  • To compare the performance of developed predictors against existing methods and other automated servers.

Main Methods:

  • Utilized deep residual neural networks with channel-wise attention for classifying residue-residue distances into multiple intervals.
  • Generated input features using co-evolutionary and sequence-based information from MSAs created with various alignment methods and databases.
  • Developed five MULTICOM distance predictors with different configurations and training strategies for CASP14 participation.

Main Results:

  • The best MULTICOM predictor ranked 5th out of 30 automated servers for long-range contact prediction precision and 6th for 10-interval distance classification precision in CASP14.
  • Performance was superior to the best CASP13 distance prediction method.
  • Demonstrated that MSA quality, influenced by alignment methods and databases, significantly impacts prediction accuracy; larger datasets and complementary features improve results.

Conclusions:

  • The MULTICOM predictors demonstrate competitive performance in protein distance prediction, as evidenced by their CASP14 rankings.
  • The quality of multiple sequence alignments is a critical factor, and its generation requires careful consideration of alignment methods and sequence databases.
  • The average probability of predicted contacts serves as a reliable confidence measure for distance predictions and aids in selecting accurate distance maps.