The signaling pathway of dopamine D2 receptor (D2R) activation using normal mode analysis (NMA) and the construction of pharmacophore models for D2R ligands

Abstract

G-protein-coupled receptors (GPCRs) are targets of more than 30% of marketed drugs. Investigation on the GPCRs may shed light on upcoming drug design studies. In the present study, we performed a combination of receptor- and ligand-based analysis targeting the dopamine D2 receptor (D2R). The signaling pathway of D2R activation and the construction of universal pharmacophore models for D2R ligands were also studied. The key amino acids, which contributed to the regular activation of the D2R, were in detail investigated by means of normal mode analysis (NMA). A derived cross-correlation matrix provided us an understanding of the degree of pair residue correlations. Although negative correlations were not observed in the case of the inactive D2R state, a high degree of correlation appeared between the residues in the active state. NMA results showed that the cytoplasmic side of the TM5 plays a significant role in promoting of residue–residue correlations in the active state of D2R. Tracing motions of the amino acids Arg219, Arg220, Val223, Asn224, Lys226, and Ser228 in the position of the TM5 are found to be critical in signal transduction. Complementing the receptor-based modeling, ligand-based modeling was also performed using known D2R ligands. The top-scored pharmacophore models were found as 5-sited (AADPR.671, AADRR.1398, AAPRR.3900, and ADHRR.2864) hypotheses from PHASE modeling from a pool consisting of more than 100 initial candidates. The constructed models using 38 D2R ligands (in the training set) were validated with 15 additional test set compounds. The resulting model correctly predicted the pIC₅₀ values of an additional test set compounds as true unknowns.

Keywords:

• dopamine D2 receptor (D2R)
• protein engineering
• 3D-QSAR
• D2high and D2low states of dopamine
• signaling pathway
• normal mode analysis (NMA)

Acknowledgements

S.D. acknowledges support from Bilim Akademisi – The Science Academy, Turkey under the BAGEP program, and S.D., R.E.S. and M.Y. acknowledge TUBITAK ULAKBIM High Performance and Grid Computing Center (TR-Grid) as well as National Center for High Performance Computing (Ulusal Yüksek Başarımlı Hesaplama Merkezi- UHeM) for support of computational sources.