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Workshop: Machine Learning in Structural Biology Workshop

Lightweight Equivariant Graph Representation Learning for Protein Engineering

Bingxin Zhou · · Kai Yi · Xinye Xiong · Pan Tan · Liang Hong · Yuguang Wang


This work tackles the issue of directed evolution in computational protein design that makes an accurate prediction for the function of a protein mutant. We design a lightweight pre-training graph neural network model for multi-task protein representation learning from its 3D structure. Rather than reconstructing and optimizing the protein structure, the trained model recovers the amino acid types and key properties of the central residues from a given noisy three-dimensional local environment. On the prediction task for the higher-order mutants, where many amino acid sites of the protein are mutated, the proposed training strategy achieves remarkably higher performance by 20% improvement at the cost of requiring less than 1% of computational resources that are required by popular transformer-based state-of-the-art deep learning models for protein design.

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