AlphaFold is an artificial intelligence approach for predicting the three-dimensional (3D) structures of proteins with atomic accuracy. One challenge that limits the use of AlphaFold models for drug discovery is the correct prediction of folding in the absence of ligands and cofactors, which compromises their direct use. We have previously described the optimization and use of the histone deacetylase 11 (HDAC11) AlphaFold model for the docking of selective inhibitors such as FT895 and SIS17. Based on the predicted binding mode of FT895 in the optimized HDAC11 AlphaFold model, a new scaffold for HDAC11 inhibitors was designed, and the resulting compounds were tested in vitro against various HDAC isoforms. Compound 5a proved to be the most active compound with an IC50 of 365 nM and was able to selectively inhibit HDAC11. Furthermore, docking of 5a showed a binding mode comparable to FT895 but could not adopt any reasonable poses in other HDAC isoforms. We further supported the docking results with molecular dynamics simulations that confirmed the predicted binding mode. 5a also showed promising activity with an EC50 of 3.6 μM on neuroblastoma cells.
- MeSH
- antitumorózní látky * farmakologie chemie chemická syntéza MeSH
- histondeacetylasy * metabolismus MeSH
- inhibitory histondeacetylas * farmakologie chemie chemická syntéza MeSH
- lidé MeSH
- molekulární struktura MeSH
- nádorové buněčné linie MeSH
- neuroblastom * farmakoterapie patologie MeSH
- racionální návrh léčiv * MeSH
- simulace molekulární dynamiky MeSH
- simulace molekulového dockingu MeSH
- umělá inteligence MeSH
- vztah mezi dávkou a účinkem léčiva MeSH
- vztahy mezi strukturou a aktivitou MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
HDAC11 is a class IV histone deacylase with no crystal structure reported so far. The catalytic domain of HDAC11 shares low sequence identity with other HDAC isoforms, which makes conventional homology modeling less reliable. AlphaFold is a machine learning approach that can predict the 3D structure of proteins with high accuracy even in absence of similar structures. However, the fact that AlphaFold models are predicted in the absence of small molecules and ions/cofactors complicates their utilization for drug design. Previously, we optimized an HDAC11 AlphaFold model by adding the catalytic zinc ion and minimization in the presence of reported HDAC11 inhibitors. In the current study, we implement a comparative structure-based virtual screening approach utilizing the previously optimized HDAC11 AlphaFold model to identify novel and selective HDAC11 inhibitors. The stepwise virtual screening approach was successful in identifying a hit that was subsequently tested using an in vitro enzymatic assay. The hit compound showed an IC50 value of 3.5 μM for HDAC11 and could selectively inhibit HDAC11 over other HDAC subtypes at 10 μM concentration. In addition, we carried out molecular dynamics simulations to further confirm the binding hypothesis obtained by the docking study. These results reinforce the previously presented AlphaFold optimization approach and confirm the applicability of AlphaFold models in the search for novel inhibitors for drug discovery.
Background: Alzheimer's disease (AD) drugs in therapy are limited to acetylcholine esterase inhibitors and memantine. Newly developed drugs against a single target structure have an insufficient effect on symptomatic AD patients. Results: Novel aromatically anellated pyridofuranes have been evaluated for inhibition of AD-relevant protein kinases cdk1, cdk2, gsk-3b and Fyn. Best activities have been found for naphthopyridofuranes with a hydroxyl function as part of the 5-substituent and a hydrogen or halogen substituent in the 8-position. Best results in nanomolar ranges were found for benzopyridofuranes with a 6-hydroxy and a 3-alkoxy substitution or an exclusive 6-alkoxy substituent. Conclusion: First lead compounds were identified inhibiting two to three kinases in nanomolar ranges to be qualified as an innovative approach for AD multitargeting.
