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Studies of H4R antagonists using 3D-QSAR, molecular docking and molecular dynamics

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Abstract

Three-dimensional quantitative structure–activity relationship studies were performed on a series of 88 histamine receptor 4 (H4R) antagonists in an attempt to elucidate the 3D structural features required for activity. Several in silico modeling approaches, including comparative molecular field analysis (CoMFA), comparative similarity indices analysis (CoMSIA), molecular docking, and molecular dynamics (MD), were carried out. The results show that both the ligand-based CoMFA model (Q 2 = 0.548, R 2ncv  = 0.870, R 2pre  = 0.879, SEE = 0.410, SEP = 0.386) and the CoMSIA model (Q 2 = 0.526, R 2ncv =0.866, R 2pre  = 0.848, SEE = 0.416, SEP = 0.413) are acceptable, as they show good predictive capabilities. Furthermore, a combined analysis incorporating CoMFA, CoMSIA contour maps and MD results shows that (1) compounds with bulky or hydrophobic substituents at positions 4–6 in ring A (R2 substituent), positively charged or hydrogen-bonding (HB) donor groups in the R1 substituent, and hydrophilic or HB acceptor groups in ring C show enhanced biological activities, and (2) the key amino acids in the binding pocket are TRP67, LEU71, ASP94, TYR95, PHE263 and GLN266. To our best knowledge, this work is the first to report the 3D-QSAR modeling of these H4R antagonists. The conclusions of this work may lead to a better understanding of the mechanism of antagonism and aid in the design of new, more potent H4R antagonists.

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Abbreviations

QSAR:

Quantitative structure–activity relationship

3D-QSAR:

Three-dimensional quantitative structure–activity relationship

HR:

Histamine receptors

H4R:

Histamine receptor 4

GPCR:

G-protein-coupled receptor

H4R:

Histamine receptor 4

CADD:

Computer-aided drug design

CoMFA:

Comparative molecular field analysis

CoMSIA:

Comparative similarity index analysis

MD:

Molecular dynamics

HB:

Hydrogen bond

RMSD:

Root mean square deviation

SEE:

Standard error of estimates

SEP:

Standard error of prediction

Q 2 :

Cross-validated correlation coefficient after the leave-one-out procedure

R 2ncv :

Non-cross-validated correlation coefficient

F ratio of R 2ncv :

Explained to unexplained R 2ncv ratio = R 2ncv /(1 − R 2ncv )

R 2pre :

Predicted correlation coefficient for the test set of compounds

OPN:

Optimal number of principal components

PLS:

Partial least squares

PCs:

Principal components

LOO:

Leave-one-out

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Acknowledgments

We gratefully acknowledge Armin Buschauer and his colleagues for providing us with the homology model of the H4R protein structure. This work is supported by the National Natural Science Foundation of China (grant no. 10801025).

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Authors and Affiliations

  1. Department of Materials Science and Chemical Engineering, Dalian University of Technology, Dalian, 116012, Liaoning, China

    Jing Liu, Yan Li, Hui-Xiao Zhang & Shu-Wei Zhang

  2. Lab of Pharmaceutical Resource Discovery, Dalian Institute of Chemical Physics, Graduate School of the Chinese Academy of Sciences, Dalian, 116023, Liaoning, China

    Ling Yang

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Correspondence to Yan Li.

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Liu, J., Li, Y., Zhang, HX. et al. Studies of H4R antagonists using 3D-QSAR, molecular docking and molecular dynamics. J Mol Model 18, 991–1001 (2012). https://doi.org/10.1007/s00894-011-1137-x

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