Graph neural networks for prediction of protein isoelectric points

Graph neural networks were used to model protein isoelectric points. Predictions contained markedly fewer outliers (predicted with errors > 0.5 pH units) compared to tools published in the literature, despite slightly higher root-mean-squared errors. This result was reproduced for graph convolutional and graph isomorphism networks when node features used only one-hot encoding of amino acid sequences. Graph isomorphism networks could also produce similar predictive powers when employing physical descriptors of the amino acids, either alone or in addition to the one-hot encoded features.