Protein binding affinity prediction under multiple substitutions applying eGNNs on Residue and Atomic graphs combined with Language model information: eGRAL

• URL: http://arxiv.org/abs/2405.02374v1
• Date: Fri, 3 May 2024 10:33:19 GMT
• Title: Protein binding affinity prediction under multiple substitutions applying eGNNs on Residue and Atomic graphs combined with Language model information: eGRAL
• Authors: Arturo Fiorellini-Bernardis, Sebastien Boyer, Christoph Brunken, Bakary Diallo, Karim Beguir, Nicolas Lopez-Carranza, Oliver Bent,
• Abstract summary: Deep learning is increasingly recognized as a powerful tool capable of bridging the gap between in-silico predictions and in-vitro observations. We propose eGRAL, a novel graph neural network architecture designed for predicting binding affinity changes from amino acid substitutions in protein complexes. eGRAL leverages residue, atomic and evolutionary scales, thanks to features extracted from protein large language models.
• Score: 1.840390797252648
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Protein-protein interactions (PPIs) play a crucial role in numerous biological processes. Developing methods that predict binding affinity changes under substitution mutations is fundamental for modelling and re-engineering biological systems. Deep learning is increasingly recognized as a powerful tool capable of bridging the gap between in-silico predictions and in-vitro observations. With this contribution, we propose eGRAL, a novel SE(3) equivariant graph neural network (eGNN) architecture designed for predicting binding affinity changes from multiple amino acid substitutions in protein complexes. eGRAL leverages residue, atomic and evolutionary scales, thanks to features extracted from protein large language models. To address the limited availability of large-scale affinity assays with structural information, we generate a simulated dataset comprising approximately 500,000 data points. Our model is pre-trained on this dataset, then fine-tuned and tested on experimental data.

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