In this project, we seek to continue our development of new innovative computational tools, apply them to model relevant protein targets of orphan neurodegenerative diseases, and build a publicly accessible web resource to host these tools and models. These goals will be achieved via two Specific Aims.
Aim -1 is to develop and validate two new structure-based computational tools. The first tool (Shape4) is a fast, structure-based virtual screening method designed to search large multi-conformer molecular databases for potential ligands for a protein target. It is based on this chief hypothesis: a ligand molecule's topographical shape and pharmacophore features should be complementary to those of its protein binding site. Novel computational geometry and shape modeling algorithms will be employed to fulfill the shape / pharmacophore matching tasks. The second tool (SB-PPK) generates structure-based descriptors for organic molecules. The descriptors so generated depend not only on the structure of an organic molecule, but also on the binding site features of the target protein. Thus, these new descriptors overcome the drawbacks of traditional molecular descriptors that depend only on the structures of organic molecules, regardless of what the target protein is. The new descriptors will be employed in conjunction with QSAR (quantitative structure activity relationship) modeling workflow to develop predictive models for selected protein targets (Aim-2a). Specifically, the two new methods developed in Aim-1 will be applied to build predictive models for targets from phosphodiesterase (PDE) and histone deacetylase (HDAC) families: PDE- 4, PDE-5, HDAC-7 and HDAC-8. We will then deploy the validated models via a web portal (Aim-2b) to benefit the research community of orphan neurodegenerative diseases.
We aim to develop and apply innovative computational tools to study the protein targets of orphan neurodegenerative diseases, and to establish an open-access informatics resource to support the drug discovery efforts in these disease areas. We will ultimately contribute to alleviating the pain of orphan neurodegenerative disease patients.
|Dong, Xialan; Hilliard, Solomon G; Zheng, Weifan (2011) Structure-based quantitative structure--activity relationship modeling of estrogen receptor ?-ligands. Future Med Chem 3:933-45|
|Ebalunode, Jerry O; Zheng, Weifan; Tropsha, Alexander (2011) Application of QSAR and shape pharmacophore modeling approaches for targeted chemical library design. Methods Mol Biol 685:111-33|
|Dong, Xialan; Ebalunode, Jerry O; Yang, Sheng-Yong et al. (2011) Receptor-based pharmacophore and pharmacophore key descriptors for virtual screening and QSAR modeling. Curr Comput Aided Drug Des 7:181-9|
|Dong, Xialan; Ebalunode, Jerry O; Cho, Sung Jin et al. (2010) A novel structure-based multimode QSAR method affords predictive models for phosphodiesterase inhibitors. J Chem Inf Model 50:240-50|
|Ebalunode, Jerry O; Zheng, Weifan (2010) Molecular shape technologies in drug discovery: methods and applications. Curr Top Med Chem 10:669-79|
|Adekoya, Adetokunbo; Dong, Xialan; Ebalunode, Jerry et al. (2009) Development of improved models for phosphodiesterase-4 inhibitors with a multi-conformational structure-based QSAR method. Curr Chem Genomics 3:54-61|