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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.
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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
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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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Updated: Jul 16, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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Prediction and design of protease enzyme specificity using a structure-aware graph convolutional network.

Changpeng Lu1, Joseph H Lubin2, Vidur V Sarma1

  • 1Institute for Quantitative Biomedicine, Rutgers-The State University of New Jersey, Piscataway, NJ 08854.

Proceedings of the National Academy of Sciences of the United States of America
|September 20, 2023
PubMed
Summary

Predicting protease specificity is crucial for understanding cellular processes and disease. A new Protein Graph Convolutional Network (PGCN) model uses molecular interaction energetics to accurately predict enzyme specificity and guide protease design.

Keywords:
geometric machine learningmachine learningprotease specificityprotein designyeast surface display

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A Protocol for Computer-Based Protein Structure and Function Prediction
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A Protocol for Computer-Based Protein Structure and Function Prediction
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Area of Science:

  • Biochemistry and Molecular Biology
  • Computational Biology and Bioinformatics
  • Enzymology

Background:

  • Site-specific proteolysis is a critical posttranslational modification influencing physiological and pathological processes.
  • Accurate prediction of protease-substrate specificity is essential for understanding enzyme function and for developing targeted proteases.
  • Current prediction methods rely on sequence patterns and are limited in scope and accuracy.

Purpose of the Study:

  • To develop a robust and accurate method for predicting protease specificity by integrating molecular interaction energetics.
  • To enable the design of novel proteases with tailored cleavage specificities.
  • To advance the understanding of protease-substrate recognition mechanisms.

Main Methods:

  • Development of a Protein Graph Convolutional Network (PGCN) model utilizing a structure-based molecular interaction graph representation.
  • Incorporation of molecular topology and interaction energetics into machine learning workflows.
  • Validation of PGCN against experimental cleavage data for multiple protease variants and noncanonical substrates.

Main Results:

  • PGCN accurately predicts the specificity landscapes of various protease variants.
  • Key graph elements influencing specificity prediction were identified, aligning with known biochemical principles.
  • The pretrained PGCN model successfully guided the design of protease libraries for novel substrates, showing good experimental agreement.
  • The model demonstrated accuracy in assessing novel designs with sequence variations not present in training data.

Conclusions:

  • The PGCN methodology offers a physically grounded, structure-based approach for predicting protease specificity.
  • This approach can be applied to a wide range of proteases, enabling the prediction of specificity landscapes.
  • The developed methodology facilitates the creation of custom protease editors for precise protein modification.