bioRxivPharmacology & Toxicology
Discovery of a Novel Chemotype as DYRK1A Inhibitors against Alzheimer's disease: Computational Modeling and Biological Evaluation
N. Qiu, C. Qian, T. Guo, Y. Wang, H. Jin, M. Yao, M. Li, T. Guo, Y. Lv, X. Si, S. Wu, H. Wang, X. ZhangiD, J. Xia
Abstract
Dual specificity tyrosine phosphorylation-regulated kinase 1A (DYRK1A) plays an essential role in tau and A{beta} pathology closely related to Alzheimers disease (AD). Accumulative evidence has demonstrated DYRK1A inhibition is able to reduce the pathological features of AD. Nevertheless, there is no approved DYRK1A inhibitors for clinical use as anti-AD drugs. This is somewhat the lack of effective and safe chemotypes of DYRK1A inhibitors. To address this issue, we carried out in silico screening, in vitro assays and in vivo efficacy evaluation with the aim to discover a new class of DYRK1A inhibitors for potential treatment of AD. By in silico screening, we selected and purchased 16 potential DYRK1A inhibitors from the Specs chemical library. Among them, compound Q17 (Specs ID: AO-476/40829177) potently inhibited DYRK1A. The hydrogen bonds between compound Q17 and each of three amino acid residues named GLU239, LEU241 and LYS188, were uncovered by molecular docking and molecular dynamics simulation. The cell-based assays showed that compound Q17 could protect SH-SY5Y cells from okadaic acid (OA)-induced injury by targeting DYRK1A. More importantly, compound Q17 significantly improved cognitive dysfunction in 3xTg-AD mice, ameliorated pathological changes, and reduced the expression of DYRK1A, GSK-3{beta} and GSK-3{beta} (pSer9), attenuated tau hyperphosphorylation and A{beta} deposition as well. In summary, our computational modeling strategy is effective to identify novel chemotypes of DYRK1A inhibitors with great potential to treat AD, and the identified compound Q17 in this study is worthy of further study. Graphic Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=182 SRC="FIGDIR/small/565431v1_ufig1.gif" ALT="Figure 1"> View larger version (38K): org.highwire.dtl.DTLVardef@75abecorg.highwire.dtl.DTLVardef@16a3611org.highwire.dtl.DTLVardef@39aeborg.highwire.dtl.DTLVardef@afc8ab_HPS_FORMAT_FIGEXP M_FIG C_FIG
- organism: mouse
- assay: proteomics
- research focus: drug response / drug discovery
- approach: deep learning
- approach: statistical model
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