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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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Exploring "dark-matter" protein folds using deep learning.

Zander Harteveld1, Alexandra Van Hall-Beauvais1, Irina Morozova2

  • 1École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland.

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Summary
This summary is machine-generated.

We developed Genesis, a convolutional variational autoencoder, to design novel proteins. This AI model efficiently creates stable, native-like protein structures, opening new frontiers in protein engineering.

Keywords:
computational protein designdark-matter foldsdata-driven protein designde novo designdeep learning for protein designhigh-throughput screeningmachine learning for molecular generation

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Area of Science:

  • Computational biology
  • Protein engineering
  • Artificial intelligence in science

Background:

  • De novo protein design aims to create novel proteins beyond evolutionary sampling.
  • A key challenge is developing "designable" structural templates for sequence generation.
  • Existing methods often struggle with exploring diverse protein topologies.

Purpose of the Study:

  • To introduce Genesis, a convolutional variational autoencoder for learning protein structural patterns.
  • To demonstrate Genesis's capability in designing novel protein sequences for target structures.
  • To address the backbone designability challenge in de novo protein design.

Main Methods:

  • Developed Genesis, a convolutional variational autoencoder, to learn protein structure patterns.
  • Coupled Genesis with trRosetta for sequence design targeting specific protein folds.
  • Utilized a high-throughput protease resistance assay to assess protein stability.

Main Results:

  • Genesis successfully reconstructed native-like distance and angle distributions for five native and three novel protein folds.
  • The AI model demonstrated generalizability across diverse and previously unexplored protein topologies.
  • Designed proteins showed encouraging stability, with high success rates in folding.

Conclusions:

  • Genesis enables rapid exploration of protein fold space, significantly accelerating de novo protein design.
  • Small neural networks like Genesis can efficiently learn complex structural patterns in proteins.
  • This approach advances the ability to design functional novel proteins for various applications.