Predicting protein variants with equivariant graph neural networks

• URL: http://arxiv.org/abs/2306.12231v2
• Date: Mon, 24 Jul 2023 09:36:05 GMT
• Title: Predicting protein variants with equivariant graph neural networks
• Authors: Antonia Boca, Simon Mathis
• Abstract summary: We compare the abilities of equivariant graph neural networks (EGNNs) and sequence-based approaches to identify promising amino-acid mutations. Our proposed structural approach achieves a competitive performance to sequence-based approaches while being trained on significantly fewer molecules.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Pre-trained models have been successful in many protein engineering tasks. Most notably, sequence-based models have achieved state-of-the-art performance on protein fitness prediction while structure-based models have been used experimentally to develop proteins with enhanced functions. However, there is a research gap in comparing structure- and sequence-based methods for predicting protein variants that are better than the wildtype protein. This paper aims to address this gap by conducting a comparative study between the abilities of equivariant graph neural networks (EGNNs) and sequence-based approaches to identify promising amino-acid mutations. The results show that our proposed structural approach achieves a competitive performance to sequence-based methods while being trained on significantly fewer molecules. Additionally, we find that combining assay labelled data with structure pre-trained models yields similar trends as with sequence pre-trained models. Our code and trained models can be found at: https://github.com/semiluna/partIII-amino-acid-prediction.

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