Abstract
Cyclophilin D (CypD) is a mitochondria-specific cyclophilin that is known to play a pivotal role in the formation of the mitochondrial permeability transition pore (mPTP).The formation and opening of the mPTP disrupt mitochondrial homeostasis, cause mitochondrial dysfunction and eventually lead to cell death. Several recent studies have found that CypD promotes the formation of the mPTP upon binding to β amyloid (Aβ) peptides inside brain mitochondria, suggesting that neuronal CypD has a potential to be a promising therapeutic target for Alzheimer’s disease (AD). In this study, we generated an energy-based pharmacophore model by using the crystal structure of CypD—cyclosporine A (CsA) complex and performed virtual screening of ChemDiv database, which yielded forty-five potential hit compounds with novel scaffolds. We further tested those compounds using mitochondrial functional assays in neuronal cells and identified fifteen compounds with excellent protective effects against Aβ-induced mitochondrial dysfunction. To validate whether these effects derived from binding to CypD, we performed surface plasmon resonance (SPR)—based direct binding assays with selected compounds and discovered compound 29 was found to have the equilibrium dissociation constants (KD) value of 88.2 nM. This binding affinity value and biological activity correspond well with our predicted binding mode. We believe that this study offers new insights into the rational design of small molecule CypD inhibitors, and provides a promising lead for future therapeutic development.
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Acknowledgements
This work was supported by KIST institutional program (2E26850) and the National Research Council of Science & Technology (NST) Grant by the Korea government (MSIP) (No. CRC-15-04-KIST). J. L. supported by the Sungshin Women’s University Research Grant of 2015-2-21-007.
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Park, I., Londhe, A.M., Lim, J.W. et al. Discovery of non-peptidic small molecule inhibitors of cyclophilin D as neuroprotective agents in Aβ-induced mitochondrial dysfunction. J Comput Aided Mol Des 31, 929–941 (2017). https://doi.org/10.1007/s10822-017-0067-9
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DOI: https://doi.org/10.1007/s10822-017-0067-9