Rough set-based proteochemometrics modeling of G-protein-coupled receptor-ligand
2006 (English)In: Proteins: Structure, Function, and Bioinformatics, ISSN 1097-0134, Vol. 63, no 1, 24-34 p.Article in journal (Refereed) Published
G-Protein-coupled receptors (GPCRs) are among the most important drug targets. Because of a shortage of 3D crystal structures, most of the drug design for GPCRs has been ligand-based. We propose a novel, rough set-based proteochemometric approach to the study of receptor and ligand recognition. The approach is validated on three datasets containing GPCRs. In proteochemometrics, properties of receptors and ligands are used in conjunction and modeled to predict binding affinity. The rough set (RS) rule-based models presented herein consist of minimal decision rules that associate properties of receptors and ligands with high or low binding affinity. The information provided by the rules is then used to develop a mechanistic interpretation of interactions between the ligands and receptors included in the datasets. The first two datasets contained descriptors of melanocortin receptors and peptide ligands. The third set contained descriptors of adrenergic receptors and ligands. All the rule models induced from these datasets have a high predictive quality. An example of a decision rule is If R1_ligand(Ethyl) and TM helix 2 position 27(Methionine) then Binding(High). The easily interpretable rule sets are able to identify determinative receptor and ligand parts. For instance, all three models suggest that transmembrane helix 2 is determinative for high and low binding affinity. RS models show that it is possible to use rule-based models to predict ligand-binding affinities. The models may be used to gain a deeper biological understanding of the combinatorial nature of receptor-ligand interactions.
Place, publisher, year, edition, pages
2006. Vol. 63, no 1, 24-34 p.
drug design, QSAR, GPCRs, machine learning, rough sets, partial least squares
IdentifiersURN: urn:nbn:se:uu:diva-79854DOI: 10.1002/prot.2077PubMedID: 16435365OAI: oai:DiVA.org:uu-79854DiVA: diva2:107768