LIGYSIS is a web server for the analysis of ligand binding sites. Ligands bind proteins at multiple locations, or sites. Some of these sites, are well characterised and have been shown to be biologically relevant, i.e., substrate, cofactor binding sites, or catalytic sites. However, many other sites are not well characterised and their biological relevance is unknown. The server is based on a dataset of ≈ 25,000 proteins that bind to biologically relevant ligands in accordance with BioLiP.
LIGYSIS groups ligands binding to a protein according to their protein interactions. The sites are then characterised using mainly three features:
- Relative solvent accessibility (RSA). Ranging from 0-100%, it measures the extent to which a residue is exposed to solvent. The higher the value, the more accessible to solvent the residue is. The analysis of RSA can provide insight into protein function: enzyme active sites, protein-protein interaction sites, as well as key positions for the structural integrity of the protein tend to be buried. Allosteric sites, signal motifs, or highly dynamic regions, however, tend to be more exposed. Almost always, buried residues are relevant for either function, or structure.
- Evolutionary divergence. The Shenkin divergence score is used to measure divergence in a multiple sequence alignment (MSA) obtained with jackHMMER using the target protein sequence as a query. The lower this score, the more conserved a site is. Low divergence, i.e., strong conservation is usually linked with functional importance.
- Enrichment in missense variation. Functionally relevant protein positions tend to be evolutionary constrained and present fewer genetic variants than average, i.e., are depleted. Making use of the gnomAD database, we calculate a Missense Enrichment Score (MES) indicative of the functional constraint upon the binding sites, and the residues forming them. MES is an odds ratio (OR): the smaller the score, the fewer variants, the more constrained, and likely functional the position is.
For more information on how Shenkin, and MES can provide insight on protein function, see references below:
If you use LIGYSIS web server or the LIGYSIS dataset, please cite the following references:
- Utgés et al. Classification of likely functional class for ligand binding sites identified from fragment screening. Commun Biol 7, 320 (2024). https://doi.org/10.1038/s42003-024-05970-8
- Utgés, J.S., Barton, G.J. Comparative evaluation of methods for the prediction of protein–ligand binding sites. J Cheminform 16, 126 (2024). https://doi.org/10.1186/s13321-024-00923-z
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