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A new structure-based QSAR method affords both descriptive and predictive models for phosphodiesterase-4 inhibitors.

Dong X, Zheng W - Curr Chem Genomics (2008)

Bottom Line: Our method can also identify critical pharmacophore features that are responsible for the inhibitory potency of the small molecules.Thus, this structure-based QSAR method affords both descriptive and predictive models for phosphodiesterase-4 inhibitors.The success of this study has also laid a solid foundation for systematic QSAR modeling of the PDE family of enzymes, which will ultimately contribute to chemical genomics research and drug discovery targeting the PDE enzymes.

View Article: PubMed Central - PubMed

Affiliation: Department of Pharmaceutical Sciences, BRITE Institute, North Carolina Central, University, 1801 Fayetteville Street, Durham, NC 27707, USA.

ABSTRACT
We describe the application of a new QSAR (quantitative structure-activity relationship) formalism to the analysis and modeling of PDE-4 inhibitors. This new method takes advantage of the X-ray structural information of the PDE-4 enzyme to characterize the small molecule inhibitors. It calculates molecular descriptors based on the matching of their pharmacophore feature pairs with those (the reference) of the target binding pocket. Since the reference is derived from the X-ray crystal structures of the target under study, these descriptors are target-specific and easy to interpret. We have analyzed 35 indole derivative-based PDE-4 inhibitors where Partial Least Square (PLS) analysis has been employed to obtain the predictive models. Compared to traditional QSAR methods such as CoMFA and CoMSIA, our models are more robust and predictive measured by statistics for both the training and test sets of molecules. Our method can also identify critical pharmacophore features that are responsible for the inhibitory potency of the small molecules. Thus, this structure-based QSAR method affords both descriptive and predictive models for phosphodiesterase-4 inhibitors. The success of this study has also laid a solid foundation for systematic QSAR modeling of the PDE family of enzymes, which will ultimately contribute to chemical genomics research and drug discovery targeting the PDE enzymes.

No MeSH data available.


a. Deriving the pharmacophore feature pairs (the reference) from the receptor-ligand complex. b. Deriving the pharmacophore feature pairs for inhibitor molecules. c. Generating structure-based pharmacophore key (SB-PPK) descriptors.
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Figure 3: a. Deriving the pharmacophore feature pairs (the reference) from the receptor-ligand complex. b. Deriving the pharmacophore feature pairs for inhibitor molecules. c. Generating structure-based pharmacophore key (SB-PPK) descriptors.

Mentions: The LigandScout program [25] was first employed to derive structure-based pharmacophore centers (Fig. 3a). The advanced 3D pharmacophore model option of the LigandScout program was used, which allows multiple pharmacophore feature types on the same heavy atom. The pharmacophore feature types used in this study include hydrogen bond donor (D), hydrogen bond acceptor (A), positively charged (P), negatively charged (N), and lipophilic (L) centers. Pharmacophore feature pairs are then generated by making all possible pair-wise combinations of the above pharmacophore centers. A specific pharmacophore feature pair is determined by both the feature types involved and the distance between the two centers. For example, AL in Fig. (3a) indicates a hydrogen bond acceptor (A) and lipophilic (L) feature pair; and the distance between them is 4.5 A. Another example, AP, means a hydrogen bond acceptor (A) and a positive (P) feature pair and their inter-feature distance is 3.8 A. All possible feature pairs are generated, and this recorded information reflects the pharmacophoric characteristics of the target binding site, and will be used as the reference for generating the SB-PPK descriptors of the inhibitor molecules. To avoid confusion with traditional pharmacophore fingerprints, we note that we do not need “distance binning” to capture different possible distance ranges because the pharmacophore features pairs are derived from the 3D structure of the target protein. Thus, all the feature pairs have defined distances.


A new structure-based QSAR method affords both descriptive and predictive models for phosphodiesterase-4 inhibitors.

Dong X, Zheng W - Curr Chem Genomics (2008)

a. Deriving the pharmacophore feature pairs (the reference) from the receptor-ligand complex. b. Deriving the pharmacophore feature pairs for inhibitor molecules. c. Generating structure-based pharmacophore key (SB-PPK) descriptors.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2803435&req=5

Figure 3: a. Deriving the pharmacophore feature pairs (the reference) from the receptor-ligand complex. b. Deriving the pharmacophore feature pairs for inhibitor molecules. c. Generating structure-based pharmacophore key (SB-PPK) descriptors.
Mentions: The LigandScout program [25] was first employed to derive structure-based pharmacophore centers (Fig. 3a). The advanced 3D pharmacophore model option of the LigandScout program was used, which allows multiple pharmacophore feature types on the same heavy atom. The pharmacophore feature types used in this study include hydrogen bond donor (D), hydrogen bond acceptor (A), positively charged (P), negatively charged (N), and lipophilic (L) centers. Pharmacophore feature pairs are then generated by making all possible pair-wise combinations of the above pharmacophore centers. A specific pharmacophore feature pair is determined by both the feature types involved and the distance between the two centers. For example, AL in Fig. (3a) indicates a hydrogen bond acceptor (A) and lipophilic (L) feature pair; and the distance between them is 4.5 A. Another example, AP, means a hydrogen bond acceptor (A) and a positive (P) feature pair and their inter-feature distance is 3.8 A. All possible feature pairs are generated, and this recorded information reflects the pharmacophoric characteristics of the target binding site, and will be used as the reference for generating the SB-PPK descriptors of the inhibitor molecules. To avoid confusion with traditional pharmacophore fingerprints, we note that we do not need “distance binning” to capture different possible distance ranges because the pharmacophore features pairs are derived from the 3D structure of the target protein. Thus, all the feature pairs have defined distances.

Bottom Line: Our method can also identify critical pharmacophore features that are responsible for the inhibitory potency of the small molecules.Thus, this structure-based QSAR method affords both descriptive and predictive models for phosphodiesterase-4 inhibitors.The success of this study has also laid a solid foundation for systematic QSAR modeling of the PDE family of enzymes, which will ultimately contribute to chemical genomics research and drug discovery targeting the PDE enzymes.

View Article: PubMed Central - PubMed

Affiliation: Department of Pharmaceutical Sciences, BRITE Institute, North Carolina Central, University, 1801 Fayetteville Street, Durham, NC 27707, USA.

ABSTRACT
We describe the application of a new QSAR (quantitative structure-activity relationship) formalism to the analysis and modeling of PDE-4 inhibitors. This new method takes advantage of the X-ray structural information of the PDE-4 enzyme to characterize the small molecule inhibitors. It calculates molecular descriptors based on the matching of their pharmacophore feature pairs with those (the reference) of the target binding pocket. Since the reference is derived from the X-ray crystal structures of the target under study, these descriptors are target-specific and easy to interpret. We have analyzed 35 indole derivative-based PDE-4 inhibitors where Partial Least Square (PLS) analysis has been employed to obtain the predictive models. Compared to traditional QSAR methods such as CoMFA and CoMSIA, our models are more robust and predictive measured by statistics for both the training and test sets of molecules. Our method can also identify critical pharmacophore features that are responsible for the inhibitory potency of the small molecules. Thus, this structure-based QSAR method affords both descriptive and predictive models for phosphodiesterase-4 inhibitors. The success of this study has also laid a solid foundation for systematic QSAR modeling of the PDE family of enzymes, which will ultimately contribute to chemical genomics research and drug discovery targeting the PDE enzymes.

No MeSH data available.