Graph neural networks have been introduced to deal with learning tasks where the input data are naturally represented as nodes connected by edges. Molecules lend themselves to such treatment, where nodes represent atoms and edges between these nodes represent chemical bonds. Employing a graph neural network with physical information on both atoms (node features) and bonds (edge features), MoleculeNet is widely considered the de facto benchmark for molecular property prediction. In the case of proteins, a simpler graph representation is possible, namely, the representation of amino acids as nodes, rendering the amide bonds between amino acids as generic edges. With this simpler approach, node features can be as simple as one-hot encoding (OHE) of the AAs. Physical AA descriptors, which are widely available , may be added. As proteins are not branched, all nodes have exactly two edges, apart from the first and last AAs of the sequence, with a single node each. Such a connectivity scheme is very simple, especially if edge features are assumed to be redundant. The goal of this investigation was to compare the ability of such a straightforward sequence-based graph neural network to predict pI values with the performance of published models. The code used in the calculations is made available on github, where an optimized model can be used to predict the pI of proteins based on AA sequences.