Structural Biology of Epigenetic Targets: Exploiting Complexity

Marek, Martin; Shaik, T.B.; Romier, Christophe

doi:10.1002/9783527809257.ch2

Cited by 2 publications

(1 citation statement)

References 108 publications

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“…Histone deacetylases (HDACs) form a protein family responsible for catalyzing the elimination of acetyl groups from lysine residue of histone proteins as well as other substrates [1]. The histone deacetylase family is classified into four main classes, three of which are constituted by eleven zinc-dependent HDACs, namely, class I (HDAC1, 2, 3 and 8), class IIa (HDAC4, 5, 7 and 9), class IIb (HDAC6 and 10), and class IV (HDAC11) [2].…”

Section: Introductionmentioning

confidence: 99%

Comparative Structure-Based Virtual Screening Utilizing Optimized AlphaFold Model Identifies Selective HDAC11 Inhibitor

Baselious,

Hilscher,

Robaa

et al. 2024

IJMS

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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.

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Section: Introductionmentioning

confidence: 99%

Comparative Structure-Based Virtual Screening Utilizing Optimized AlphaFold Model Identifies Selective HDAC11 Inhibitor

Baselious,

Hilscher,

Robaa

et al. 2024

IJMS

View full text Add to dashboard Cite

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Utilization of an optimized AlphaFold protein model for structure‐based design of a selective HDAC11 inhibitor with anti‐neuroblastoma activity

Baselious,

Hilscher,

Hagemann

et al. 2024

Archiv der Pharmazie

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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.

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Structural Biology of Epigenetic Targets: Exploiting Complexity

Cited by 2 publications

References 108 publications

Comparative Structure-Based Virtual Screening Utilizing Optimized AlphaFold Model Identifies Selective HDAC11 Inhibitor

Comparative Structure-Based Virtual Screening Utilizing Optimized AlphaFold Model Identifies Selective HDAC11 Inhibitor

Utilization of an optimized AlphaFold protein model for structure‐based design of a selective HDAC11 inhibitor with anti‐neuroblastoma activity

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