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Protein Networks02:26

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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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An Integrated Approach for Microprotein Identification and Sequence Analysis
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ECNet is an evolutionary context-integrated deep learning framework for protein engineering.

Yunan Luo1, Guangde Jiang2, Tianhao Yu2

  • 1Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana-Champaign, IL, USA.

Nature Communications
|October 1, 2021
PubMed
Summary
This summary is machine-generated.

We developed ECNet, a deep-learning algorithm for protein engineering. ECNet improves accuracy by integrating evolutionary contexts, leading to better prediction of protein function and successful protein design.

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Area of Science:

  • Biochemistry
  • Computational Biology
  • Machine Learning

Background:

  • Machine learning is increasingly applied to protein engineering.
  • Current algorithms have limited accuracy due to non-specific sequence contexts.
  • Predicting protein function from sequence remains a challenge.

Purpose of the Study:

  • To develop a more accurate machine learning algorithm for protein engineering.
  • To improve the prediction of functional fitness by integrating evolutionary information.
  • To guide the design of proteins with enhanced properties.

Main Methods:

  • Developed ECNet (evolutionary context-integrated neural network), a deep-learning algorithm.
  • Integrated local evolutionary context from homologous sequences (modeling residue-residue epistasis).
  • Integrated global evolutionary context encoding semantic and structural features from the protein sequence universe.

Main Results:

  • ECNet accurately maps protein sequence to function.
  • ECNet generalizes from low-order to higher-order mutants.
  • ECNet outperformed existing algorithms on ~50 deep mutational scanning datasets.
  • ECNet successfully guided the engineering of TEM-1 β-lactamase for improved ampicillin resistance.

Conclusions:

  • ECNet represents a significant advancement in machine learning for protein engineering.
  • The integration of evolutionary contexts enhances predictive accuracy.
  • ECNet can effectively guide the design of proteins with desired functional improvements.