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Related Concept Videos

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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The function of proteins depends on their native three-dimensional structure, which is dictated by the amino acid sequence of the specific protein. Folding of the polypeptide chain takes place under specific conditions that energetically favor the folded conformation. In contrast, protein denaturation occurs spontaneously under unfavorable conditions that disrupt the integrity of the folded conformation. Thus, the chemical and physical environment of a protein, such as significant changes in pH...
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Proteins are chains of amino acids linked together by peptide bonds. Upon synthesis, a protein folds into a three-dimensional conformation, critical to its biological function. Interactions between its constituent amino acids guide protein folding, and hence the protein structure is primarily dependent on its amino acid sequence.
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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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Updated: Sep 16, 2025

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
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Predicting protein stability changes upon mutations with dual-view ensemble learning from single sequence.

Zhiwei Nie1,2,3, Yiming Ma1,3, Yutian Liu4

  • 1School of Electronic and Computer Engineering, Peking University, Shenzhen, China.

Briefings in Bioinformatics
|July 11, 2025
PubMed
Summary

Predicting protein stability changes from mutations aids protein engineering. The DVE-stability framework uses dual-view ensemble learning to accurately forecast these changes from single sequences, improving prediction efficiency and interpretability.

Keywords:
dual-viewensemble learningmicroenvironment simulationprotein stability changes

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Area of Science:

  • Biochemistry and Molecular Biology
  • Computational Biology and Bioinformatics
  • Protein Engineering

Background:

  • Protein stability is critical for protein function and engineering.
  • Accurate prediction of mutation-induced stability changes is essential for efficient protein design.
  • Existing methods often struggle with capturing complex mutational effects and generalizability.

Purpose of the Study:

  • To develop a novel computational framework for predicting mutation-induced protein stability changes.
  • To improve the accuracy and generalizability of protein stability change prediction using sequence data.
  • To provide an interpretable tool for identifying beneficial mutations in protein engineering.

Main Methods:

  • A dual-view ensemble learning framework (DVE-stability) was proposed.
  • The framework integrates global and local mutation dependencies via ensemble learning.
  • A structural microenvironment simulation module was incorporated to introduce structural information at the sequence level.

Main Results:

  • DVE-stability achieved state-of-the-art performance on seven single-point mutation datasets, outperforming existing methods on five.
  • The framework demonstrated superior generalizability in zero-shot inference for multiple-point mutation prediction, capturing epistasis.
  • DVE-stability showed strong performance in predicting rare beneficial mutations crucial for directed evolution.

Conclusions:

  • DVE-stability offers a flexible and efficient approach for predicting mutation-induced protein stability changes.
  • The framework provides interpretable insights into intramolecular interactions through attention scores.
  • This tool can significantly aid protein engineering and directed evolution efforts.