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XENet: Using a new graph convolution to accelerate the timeline for protein design on quantum computers.

Jack B Maguire1, Daniele Grattarola2, Vikram Khipple Mulligan3

  • 1Menten AI, Inc., Palo Alto, California, United States of America.

Plos Computational Biology
|September 27, 2021
PubMed
Summary
This summary is machine-generated.

We developed XENet, a novel graph convolution method that improves protein sequence design by better representing protein environments. XENet significantly reduces rotamer counts, enhancing computational efficiency for both classical and quantum algorithms.

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Area of Science:

  • Computational biology
  • Machine learning
  • Protein engineering

Background:

  • Graph representations are standard for protein structures in sequence design when backbone conformation is known.
  • Existing graph convolution algorithms struggle with protein environments due to limited focus on edge attributes and shallow architectures.

Purpose of the Study:

  • To introduce an improved message-passing operation, XENet, for enhanced protein design.
  • To address limitations in current graph convolution methods for representing protein environments.

Main Methods:

  • Developed XENet, a novel message-passing operation emphasizing both incoming and outgoing edge attributes.
  • Compared XENet against existing graph convolutions for protein side-chain optimization and sequence design within Rosetta's protocol.
  • Evaluated XENet's performance in decreasing rotamer sample counts and its tolerance for deeper neural network architectures.

Main Results:

  • XENet decreased rotamer counts by 40% without compromising quality.
  • Reduced classical pre-computation memory consumption by over 3x.
  • Decreased quantum algorithm qubit consumption by 40% and solution space size by 165x.
  • Demonstrated superior performance and greater tolerance for deeper architectures compared to existing models.

Conclusions:

  • XENet offers a significant advancement in modeling local kinematics for protein design.
  • The method enhances computational efficiency for classical and quantum approaches to protein sequence design.
  • XENet's ability to handle deeper architectures opens possibilities for more complex protein design challenges.