Fragments vs histone demethylases: docking and merging

Tweaking epigenetic machinery is increasingly popular as a therapeutic strategy. Epigenetics often involves modification to proteins – such as histones – that interact with DNA. One common type of modification is methylation of lysine or arginine residues. A couple months ago we highlighted how fragment-based approaches were used to discover inhibitors of a methyltransferase, one of many classes of protein-modifying enzymes that underlie epigenetics. Just as methyltransferases put methyl groups on, demethylases take methyl groups off. In a recent paper in J. Med. Chem., Udo Oppermann, Brian Shoichet, and Danica Fujimori and their collaborators at the University of Oxford and UCSF show that demethylases too can be successfully targeted with fragments. What’s more, the work exemplifies concrete contributions of computational approaches to both identify and advance fragments.

The demethylase KDM4C has been implicated in cancer. This enzyme uses iron, the cofactor α-ketoglutarate (α-KG), and oxygen as part of its mechanism. The researchers ran a computational screen (using DOCK 3.6) of more than 600,000 compounds in the ZINC fragment library. Top-scoring hits were triaged on the basis of novelty and good interactions with the iron atom, and 14 fragments were tested in a functional assay. Remarkably, all of them were active, with 7 showing IC₅₀ values < 200 µM!

Several of the top hits were 5-aminosalicylates such as compound 4. Testing 80 commercial analogs led to low micromolar inhibitors, but these could not be further optimized. Moreover, despite the small size and polarity of these compounds, many of them showed signs of aggregation – a reminder that this type of artifact must always be considered.

Unfortunately, crystallography was also not successful for any of the fragments or analogs. But the researchers noticed that, according to the docking results, fragments such as compound 4 could assume two different binding modes: in one, the carboxylate and phenol interacted with the iron atom, while in the other the carboxylate interacted with lysine and tyrosine residues in the protein. This inspired several ideas for fragment merging, leading to molecules such as compound 45. Additional variations led to mid-nanomolar inhibitors such as compound 35.

As expected, these molecules are competitive with the α-KG cofactor (which normally binds to the iron atom) but not with the peptide substrate. Many also showed encouraging selectivity profiles against other demethylases, though no cell data are reported. Finally, crystallography mostly confirmed the predicted binding models for several of the merged compounds, including compound 35.

This is a lovely example of using computational approaches not just for fragment-finding, but for fragment merging as well. As the authors point out, this was done not to showcase computational methods but because crystallography didn’t initially work. Even in the short lifetime of Practical Fragments, in silico methods have made remarkable progress, and this is another milestone. It will be fun to see further optimization of these molecules.