Abstract
With the development of sequencing techniques, the discovery of new proteins significantly exceeds the human capacity and resources for experimentally characterising protein functions. LEGO-CSM is a comprehensive web-based resource that fills this gap by leveraging the well-established and robust graph-based signatures to supervised learning models using both protein sequence and structure information to accurately model protein function in terms of Subcellular Localisation, Enzyme Commission (EC) numbers and Gene Ontology (GO) terms. We show our models perform as well as or better than alternative approaches, achieving Area Under the Receiver Operating Characteristic Curve (ROC AUC) of up to 0.93 for subcellular localisation, up to 0.93 for EC and up to 0.81 for GO terms on independent blind tests. LEGO-CSM's web server is freely available at https://biosig.lab.uq.edu.au/lego_csm. In addition, all datasets used to train and test LEGO-CSM's models can be downloaded at https://biosig.lab.uq.edu.au/lego_csm/data. Supplementary data are available at Bioinformatics online.
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