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Detection of protein-ligand binding sites using graph neural networks
Gamouh, Hamza ; Hoksza, David (advisor) ; Pilát, Martin (referee)
The function of most biological systems is realized by the interaction of proteins with other biological molecules. Protein-ligand binding is one of the most im- portant kind of interactions which, if studied well, can reveal a lot of hidden causal patterns behind biological functions, and can contribute to the rapid de- velopment of drug research. Protein-ligand binding sites detection is one aspect of the general study of protein-ligand interactions, where the goal is to develop computational methods that can use ligand as well as protein structure, possibly together with its sequence data, to predict regions of the protein that can bind to potential ligands. The significant growth of protein structure databases, such as, Protein Data Bank (PDB) and sequence databases like Universal Protein Re- source (UniProt), has led to the development of different machine learning and deep learning approaches that make use of this huge amount of biological data to solve this task. In this thesis, we examine a deep learning method based on a recent model architecture called Graph Convolutional Networks (GCN), which combines the traditional Convolutional Neural Network (CNN) architecture, and the more recent Graph Neural Network (GNN) architecture which has been suc- cessful in solving various chemoinformatics...
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