Fragments win in a virtual screen against Notum

Wnt proteins are implicated in a variety of diseases, from Alzheimer’s to colorectal cancer. The enzyme Notum shuts down signaling by removing a palmitoyl group from Wnt. Last year Practical Fragments highlighted several series of Notum inhibitors identified from biochemical and crystallographic fragment screens. The researchers behind those efforts, including Paul Fish and Fredrik Svensson (University College London), have now published a successful virtual screen against the enzyme in J. Med. Chem.

 

Starting with 1.5 million compounds available from ChemDiv, the researchers chose 534,804 based on a variety of computational filters including molecular weight (200-500 Da), number of hydrogen bond donors (<=2) and ClogD (-4 to 5). A virtual screen of these (using Glide) produced 1330 high-scoring hits, of which 1088 were chosen for purchase. Of these, 952 were available, a much higher percentage than the ZINC15-reliant paper we wrote about earlier this year.

 

All 952 compounds were tested in a biochemical assay, and the 44 that gave >50% inhibition at 1 µM were then tested in dose-response format. This yielded 31 compounds with IC₅₀ values < 500 nM. These could be subdivided into four structurally related clusters and eight singletons. Further triaging removed compounds likely to cause assay interference as well as those similar to known Notum inhibitors. This left two clusters and two singletons.

 

Compound 1f was the most potent member of a series of 9 related (and possibly covalent) inhibitors. Although these strongly inhibited the enzyme in the biochemical assay, they were essentially inactive in a cell-based assay. They were also highly insoluble and showed low cell permeability, and were thus dropped.

 

Compound 2a was one of two related molecules that were also quite potent when initially tested. Unfortunately, when the molecules were resynthesized they turned out to be significantly weaker and were also not very soluble, so this series was also halted.

 

The singleton compound 3 turned out to be a covalent inhibitor; the catalytic serine formed an ester with the molecule. The mechanism is more fully described in this open-access J. Med. Chem. paper.

 

That leaves the second singleton. Compound 4d was not just active in the biochemical assay, it also showed sub-micromolar cell activity. SAR, guided by crystallography, ultimately led to low nanomolar inhibitors. The pK_a of compound 4d was measured to be 7.9, which is less acidic than many previously reported Notum inhibitors and thus more likely to be cell permeable. This turned out to be the case experimentally, and the compound was also stable in mouse liver microsomes. Pharmacokinetics in mice were promising for several compounds, but unfortunately brain penetration – which the researchers were hoping for – was negligible. (This could be an advantage for peripheral diseases.)

 

This is a nice example of lead discovery in academia. Like last week’s post, it also illustrates that fragments themselves can be quite potent. Indeed, although the researchers were looking for molecules up to 500 Da in their virtual screen, all of the best hits were fragment-sized. Another illustration that small is beautiful.

Fragment docking: promiscuous but good enough?

Computational docking of fragments can be difficult, partly because the energetic differences between possible binding modes can be so small that it’s impossible to select the best one. In a recent paper in J. Med. Chem. Andrew Good and colleagues at Genzyme ask whether docking results are nonetheless good enough to act on.

The researchers were interested in the kinase Pim-1; we’ve previously highlighted their success using SPR-discovered fragments to generate nanomolar inhibitors of this target. In the current paper, they use the program Glide to virtually screen 13,888 fragment-sized molecules from their general collection against Pim-1. About 3% (462) of these were tested in a functional assay at 125 micromolar concentration, resulting in 46 fragments with IC50 better than 100 micromolar. Five diverse representatives of these fragments were then soaked into crystals of Pim-1, and their structures were compared with those from docking.

Overall, only two of the fragments showed a good correlation between the in silico and crystallographic models (rmsd 1.0 Å or better). However, two of the “failures” do make key hydrogen-bond interactions seen in the crystal structures, though some of the hydrophobic interactions are quite different.

As the researchers note:

Fragments that do not fill their binding pocket can exhibit promiscuous hydrophobic interactions due to the lack of steric constraints imposed on them by the boundaries of said pocket. As a result, docking modes that disagree with an observed crystal structure but maintain key crystallographically observed hydrogen bonds still have potential value in ligand design and optimization.

This seems reasonable, and is consistent with the notion that polar interactions are more directional – and thus perhaps easier to correctly dock – than more generic hydrophobic interactions.

But there may also be something more fundamental going on: the assumption seems to be that the observed crystallographic structures are definitive, but is this an oversimplification? After all, a crystallographically-derived model seldom provides more than one binding mode (though a notable exception led to the first approved fragment-based drug). Perhaps the reason it’s so difficult to score docked fragments is that fragments really can assume multiple binding modes, and our insistence on one single best model is the problem. If this is true, docking models are telling us more about reality than we are giving them credit for. NMR-based models are often presented as an ensemble of structures; perhaps the same should be done for docking? At any rate, NMR studies on Pim-1 with these fragments could prove interesting.

18 PI3K fragments

As we’ve noted before, kinases are a fertile field for fragment finding, but most of the targets have been protein kinases. Lipid kinases such as the phosphatidylinostide 3-kinases (PI3Ks), which mediate signal transduction by transferring a phosphate group to lipids, are also popular targets for a variety of diseases, but less has been disclosed about their suitability for fragment-based lead discovery. A paper in a recent issue of Bioorg. Med. Chem. Lett. remedies that.

Fabrizio Giordanetto and colleagues at AstraZeneca started with a homology model of p110beta (no crystal structure of this enzyme has been reported). They then used commercial software to dock 183,330 fragments selected from their corporate collection. All fragments that made at least two hydrogen bonds with the protein were organized into clusters of similar molecules and representatives of each cluster were visually inspected. This led to the selection of 210 fragments to be screened against the protein, of which 18 showed measurable activity. Structures of these fragments are provided in the paper; they range from kinase workhorses such as compound 1 to known PI3K motifs such as compound 10 to more unusual molecules such as compound 18. These hits were also tested on other members of the PI3K family, and while most showed activity across the board, others (such as compound 18) showed some selectivity.

There are some interesting structures in here; if I were starting a PI3K program I would definitely take a close look at them. Although the researchers have likely developed some of these into attractive leads, one of the virtues of fragments is that they are often so protean that different teams can start with the same fragment and end up in very different places.

Fragments in silico find new sites in crystals

Last year we highlighted a study in which virtual screening identified a number of functionally active fragments and crystallography confirmed their binding modes. In a recent issue of Bioorg. Med. Chem. Lett. researchers from Sanofi-aventis report a more complicated case: fragments that bind not only in a manner different than predicted, but in a completely different site.

The team used the computational docking method Glide to select 200 compounds likely to bind in the active site of the cytokine MIF (migration inhibitory factor). Of these, 23 were tested in crystallographic soaking studies, resulting in 5 co-crystal structures. Three of these bound in the active site, but the other two bound in a hydrophobic “cryptic” site on the protein surface formed by the rotation of a tyrosine residue. Protein rearrangements are not uncommon; a similar example was reported last year in which fragments were found to bind differently than predicted due to unforeseen protein movements. The cryptic site does appear to be real: the authors crystallized a compound reported in the patent literature and found that it binds across both the active and cryptic sites.

This is the third in a recent series of papers featured on this site in which fragment approaches found new binding sites on proteins. However, like the HIV-protease example, there is no functional data presented; I’ll take this to mean that the compounds are probably weak, if they show any detectable activity. The question of what to do with a fragment remains challenging, though (to be somewhat self-promoting) we are working on practical solutions.

What to do with a fragment is also a theme of the upcoming FBLD 2010, so if you have a success story you can share, consider submitting an abstract.

Journal of Computer-Aided Molecular Design Special FBDD Issue

Our friends over at FBDD-Literature have already highlighted this, but it bears repeating that the entire August issue of J. Comp. Aid. Mol. Des. is devoted to FBDD. For aficionados of all things silicon, there are articles on computational chemistry applied to FBDD generally as well as on more specific topics such as MCSS, NovoBench, FTMap, and two papers on Glide (here and here).

But don’t be put off by the name of the journal: with 14 articles covering close to 200 pages, there is something here for almost everyone, even for those whose interest in computers ends at using them to read this blog! A brief editorial outlines the challenges of FBDD, and a longer introductory piece gives an overview of the field. Several articles focus largely on specific targets such as p38alpha, heparanase, and Eg5, while one is devoted to assessing druggability.

Finally, two articles address the important topic of designing fragment libraries, one from the perspective of big pharma (nicely summarized here), the other from biotech.