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Protein Engineering with Lightweight Graph Denoising Neural Networks.

Bingxin Zhou1,2, Lirong Zheng1, Banghao Wu1,3

  • 1Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai 200240, China.

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This study introduces ProtLGN, a deep learning tool for protein engineering. It efficiently predicts protein fitness, designing mutants with improved properties and guiding deep mutations.

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Area of Science:

  • Biochemistry
  • Computational Biology
  • Protein Engineering

Background:

  • Protein engineering requires efficient methods to identify optimal mutants from vast candidate pools.
  • Current methods face challenges in predicting fitness and guiding mutations effectively.

Purpose of the Study:

  • To develop a data-efficient, deep-learning-based tool to steer protein engineering.
  • To predict protein fitness and guide the design of improved protein mutants.

Main Methods:

  • A lightweight graph neural network (GNN) scheme for protein structures was developed.
  • The GNN analyzes amino acid microenvironments to reconstruct sequences likely to pass natural selection.
  • The model guides protein scoring for arbitrary properties and mutation orders.

Main Results:

  • Extensive wet-lab validation across diverse properties (fluorescence, affinity, stability, cleavage activity) was performed.
  • Over 40% of designed single-site mutants outperformed wild-type proteins.
  • The model successfully designed deep mutants (up to seven sites) by bypassing negative epistasis, significantly improving physicochemical properties.

Conclusions:

  • The structure-based deep learning model (ProtLGN) is a versatile tool for protein engineering.
  • It demonstrates potential for guiding deep mutations and improving protein properties.
  • The approach benefits both computational and bioengineering fields.