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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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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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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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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: May 10, 2025

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
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Shared-weight graph framework for comprehensive protein stability prediction across diverse mutation types.

Gen Li1, Sijie Yao1, Long Fan1

  • 1Production and R&D Center I of LSS, GenScript (Shanghai) Biotech Co., Ltd., 186 He Dan Road, Pudong New Area, Shanghai 200131, China.

Briefings in Bioinformatics
|April 24, 2025
PubMed
Summary
This summary is machine-generated.

UniMutStab accurately predicts protein stability changes from any mutation type, including complex multipoint and indel mutations. This advances protein engineering and therapeutic development by enabling precise protein modification.

Keywords:
deep learningdiverse mutationprotein embeddingprotein stabilityshared-weight

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Area of Science:

  • Biochemistry and Molecular Biology
  • Computational Biology
  • Protein Engineering

Background:

  • Protein stability is crucial for understanding diseases and industrial enzyme function.
  • Existing models primarily address single-point mutations, neglecting multipoint and indel mutations.
  • Predicting stability changes for complex mutations is challenging with current methods.

Purpose of the Study:

  • To develop a novel computational method, UniMutStab, capable of predicting protein stability changes for arbitrary mutation types.
  • To overcome limitations of existing models that focus mainly on single-point mutations.
  • To enhance the accuracy and scope of protein stability prediction.

Main Methods:

  • UniMutStab utilizes a shared-graph convolutional network architecture.
  • It integrates protein language models and residue interaction networks.
  • An embedded edge weight module improves the incorporation of residue features and interactions.

Main Results:

  • UniMutStab surpasses existing methods in predicting protein stability changes.
  • The model demonstrates robust generalization across multiple prediction tasks.
  • It accurately predicts stability changes for single-point, multipoint, and indel mutations.

Conclusions:

  • UniMutStab offers a purely sequence-based approach for predicting arbitrary mutation types.
  • This method significantly improves the prediction of protein stability changes.
  • It holds potential for advancing protein engineering, personalized therapeutics, and diagnostics.