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Related Concept Videos

Protein Networks02:26

Protein Networks

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
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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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Covalently Linked Protein Regulators02:04

Covalently Linked Protein Regulators

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Proteins can undergo many types of post-translational modifications, often in response to changes in their environment. These modifications play an important role in the function and stability of these proteins. Covalently linked molecules include functional groups, such as methyl, acetyl, and phosphate groups, and also small proteins, such as ubiquitin. There are around 200 different types of covalent regulators that have been identified.
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Mutations01:39

Mutations

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Overview
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Protein Folding01:25

Protein Folding

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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.
Protein Structure Is Critical to Its Biological Function
Proteins perform a wide range of biological functions such as catalyzing chemical reactions, providing...
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Conserved Binding Sites01:49

Conserved Binding Sites

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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.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...
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Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
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ProSTAGE: Predicting Effects of Mutations on Protein Stability by Using Protein Embeddings and Graph Convolutional

Gen Li1, Sijie Yao1, Long Fan1

  • 1Production and R&D Center I of LSS, GenScript (Shanghai) Biotech Co., Ltd., Shanghai 200131, China.

Journal of Chemical Information and Modeling
|January 3, 2024
PubMed
Summary

We developed ProSTAGE, a deep learning method that accurately predicts how mutations affect protein stability using structure and sequence data. This tool aids protein design and understanding genetic variations.

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Area of Science:

  • Biochemistry
  • Computational Biology
  • Structural Biology

Background:

  • Protein thermodynamic stability is crucial for understanding protein structure-function relationships and interactions.
  • Predicting mutation effects on protein stability aids protein design and explains phenotypic variations.
  • Protein embedding methods show promise for modeling sequence-dependent biological contexts.

Purpose of the Study:

  • To introduce ProSTAGE, a novel deep learning method for predicting protein stability changes upon single point mutations.
  • To integrate structural and sequential protein information for enhanced prediction accuracy.
  • To provide a user-friendly web server for practical application of the prediction tool.

Main Methods:

  • Developed ProSTAGE, a deep learning model fusing graph-based techniques and language models.
  • Utilized combined structure and sequence embeddings as input features.
  • Trained the model on an extensive dataset, nearly double the size of the S2648 dataset.

Main Results:

  • ProSTAGE consistently outperformed existing state-of-the-art methods on independent benchmark datasets.
  • The use of protein embeddings significantly improved prediction accuracy compared to previous approaches.
  • Demonstrated the potential of protein language models in predicting mutation effects on protein stability.

Conclusions:

  • ProSTAGE offers a faster and more accurate method for predicting mutation impacts on protein stability.
  • The fusion of structural and sequential data enhances predictive capabilities.
  • Protein language models hold significant promise for advancing variant effect prediction and protein engineering.