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Antibiotics are traditionally used to treat bacterial infections. However, bacteria can develop immunity to drugs, rendering them ineffective and thus posing a serious threat to global health. Identifying and classifying the genes responsible for this resistance is critical for the prevention, diagnosis, and treatment of infections as well as the understanding of its mechanisms. Previous methods developed for this purpose have mostly been sequence-based, relying on the comparison to existing databases or machine learning models trained on sequence features. However, genes with comparable functions may not always have similar sequences. Consequently, in this paper, we develop a deep learning model that uses the protein structure as a complement to the sequence to classify novel Antibiotic Resistant Genes (ARGs), which we expect to provide more useful information than the sequence alone. The proposed approach consists of two steps. First, we capitalize on the much-celebrated AlphaFold model to predict the 3D structure of a protein from its amino acid sequence. Then, we process the sequence using a transformer-based protein language model and apply a graph neural network to the graph extracted from the structure. We evaluate the proposed architecture on a standard benchmark dataset on which we find it to outperform state-of-the-art methods.

In many domains data is currently represented as graphs and therefore, the graph representation of this data becomes increasingly important in machine learning. Network data is, implicitly or explicitly, always represented using a graph shift operator (GSO) with the most common choices being the adjacency, Laplacian matrices and their normalisations. In this paper, a novel parametrised GSO (PGSO) is proposed, where specific parameter values result in the most commonly used GSOs and message-passing operators in graph neural network (GNN) frameworks. The PGSO is suggested as a replacement of the standard GSOs that are used in state-of-the-art GNN architectures and the optimisation of the PGSO parameters is seamlessly included in the model training. It is proved that the PGSO has real eigenvalues and a set of real eigenvectors independent of the parameter values and spectral bounds on the PGSO are derived. PGSO parameters are shown to adapt to the sparsity of the graph structure in a study on stochastic blockmodel networks, where they are found to automatically replicate the GSO regularisation found in the literature. On several real-world datasets the accuracy of state-of-theart GNN architectures is improved by the inclusion of the PGSO in both nodeand graph-classification tasks.

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