There are over 180,000 unique proteins with 3D structures determined, with tens of thousands new structures resolved every year. This is only a small fraction of the 200 million known proteins with distinctive sequences. Recent deep learning algorithms such as AlphaFold can accurately predict 3D structures of proteins using their sequences, which help scale the protein 3D structure data to the millions. Graph neural network (GNN) has emerged as an effective deep learning approach to extract information from protein structures, which can be represented by graphs of amino acid residues. Individual protein graphs usually contain a few hundred nodes, which is manageable in size. Tens of thousands of protein graphs can be easily stored in serialized data structures such as TFrecord for training GNNs. However, training GNN on millions of protein structures is challenging. Data serialization isn’t scalable to millions of protein structures because it requires loading the entire terabyte-scale

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