C-P.10: LIGYSIS: a resource for predictor training, benchmarking and functional characterisation of ligand binding sites
Keywords
LIGYSIS, protein-ligand binding sites, function prediction, functional characterisation, ligand site prediction, benchmark.
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Reliable protein-ligand binding site definition and prediction underpin function annotation, variant interpretation and drug discovery, yet many commonly used datasets are built from single asymmetric units, leading to artificial crystal contacts, redundant interfaces, and incomplete site definitions. LIGYSIS is a comprehensive dataset that aggregates unique, biologically relevant protein-ligand interfaces across the biological assemblies of the multiple structures of a protein. The human LIGYSIS component comprises 3448 proteins, 8244 binding sites, and 65,116 ligands, and has been used as a reference set for the largest independent systematic comparative evaluation of ligand site prediction tools to date.
To make these data accessible, we developed LIGYSIS-web, an open resource hosting the entire LIGYSIS set, which includes 64,782 binding sites across 25,003 proteins. Users can query UniProt accession identifiers or upload their own structures for automated site definition, characterisation, interactive 3D visualisation, exploration, and download. Sites are characterised by evolutionary divergence, human missense variation, and solvent accessibility-derived functional scoring, enabling prioritisation of likely functional pockets and residues, as shown in our recent study.
Finally, LIGYSIS is increasingly being adopted to train and test models for the prediction of general binding, allosteric, and cryptic sites, as well as being integrated in new resources. In parallel, we are using the full LIGYSIS set to train a new ligand site predictor that uses protein language models, developing an integration of LIGYSIS-web with Jalview, and extending our work on ligand site prediction evaluation and good practices.
Co-authors: Stuart MacGowan, Diane Lee, Gopal Sapkota, Radoslav Krivá, and Geoff Barton
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