Computational prediction of alanine scanning and ligand binding energetics in G-protein coupled receptors
2014 (English)In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 10, no 4, e1003585- p.Article in journal (Refereed) Published
Site-directed mutagenesis combined with binding affinity measurements is widely used to probe the nature of ligand interactions with GPCRs. Such experiments, as well as structure-activity relationships for series of ligands, are usually interpreted with computationally derived models of ligand binding modes. However, systematic approaches for accurate calculations of the corresponding binding free energies are still lacking. Here, we report a computational strategy to quantitatively predict the effects of alanine scanning and ligand modifications based on molecular dynamics free energy simulations. A smooth stepwise scheme for free energy perturbation calculations is derived and applied to a series of thirteen alanine mutations of the human neuropeptide Y1 receptor and series of eight analogous antagonists. The robustness and accuracy of the method enables univocal interpretation of existing mutagenesis and binding data. We show how these calculations can be used to validate structural models and demonstrate their ability to discriminate against suboptimal ones. Author Summary G-protein coupled receptors constitute a family of drug targets of outstanding interest, with more than 30% of the marketed drugs targeting a GPCR. The combination of site-directed mutagenesis, biochemical experiments and computationally generated 3D structural models has traditionally been used to investigate these receptors. The increasing number of GPCR crystal structures now paves the way for detailed characterization of receptor-ligand interactions and energetics using advanced computer simulations. Here, we present an accurate computational scheme to predict and interpret the effects of alanine scanning experiments, based on molecular dynamics free energy simulations. We apply the technique to antagonist binding to the neuropeptide Y receptor Y1, the structure of which is still unknown. A structural model of a Y1-antagonist complex was derived and used as starting point for computational characterization of the effects on binding of alanine substitutions at thirteen different receptor positions. Further, we used the model and computational scheme to predict the binding of a series of seven antagonist analogs. The results are in excellent agreement with available experimental data and provide validation of both the methodology and structural models of the complexes.
Place, publisher, year, edition, pages
2014. Vol. 10, no 4, e1003585- p.
Bioinformatics (Computational Biology) Biochemistry and Molecular Biology
IdentifiersURN: urn:nbn:se:uu:diva-212102DOI: 10.1371/journal.pcbi.1003585ISI: 000336507500014OAI: oai:DiVA.org:uu-212102DiVA: diva2:676224