It has been more than a year since
we devoted a post solely to fragment library synthesis (though see here for an example
describing library synthesis and screening). Since you can’t screen fragments without
a library, Practical Fragments will spend the next two posts focusing on
recent library design papers.
The first, from David Spring (University
of Cambridge) and collaborators at the Technical University of Denmark, California
State Polytechnic University Pomona, and University of Leeds, was published
earlier this year in Chem. Commun. David Spring has long been
interested in fragments that resemble natural products (NPs), such as those with
multiple sp3 stereocenters.
The researchers focus on
3-hydroxy-2,2-disubstituted-cyclopentan-1-ones, which are found in natural
products and derived drugs. The two building blocks syn-1 and anti-1
were elaborated in fewer than six synthetic steps into a total of 38 small
molecules in 20 scaffolds, a few of which are shown.
Another paper from David Spring’s lab was published last year in Eur. J. Org. Chem. In it, the researchers describe the synthesis of nine heterocyclic spirocycles, a couple of which are shown here.
As with the newer paper, the physicochemical properties conform to the rule of three, and the molecules are quite shapely as assessed by their Fsp3 values.
Wrapping up this week’s
installment is a paper in Chem. Eur. J. from Richard Bayliss, Stuart
Warriner, Adam Nelson (all at University of Leeds) along with collaborators at
University of Leicester, Diamond Light Source, University of Oxford, and
University of Johannesburg. The researchers set out to assemble a diverse set of
80 shapely fragments for general use. Several rounds of computational pruning
arrived at 60 commercial compounds and 20 that were synthesized de novo. Both
approaches ran into problems: some “commercially available” compounds proved “difficult
to obtain in practice,” while several synthetic approaches that looked good on
paper turned out to be anything but. The final library is quite shapely though:
all the synthesized compounds have at least one stereocenter, and only two
fragments in the entire set are “close to the rod-disk axis” of a PMI plot.
Usefully, this paper presents screening data, in this case a high-concentration (80-200 mM)
crystallographic screen against Aurora A kinase. This yielded just four hits, a
5% hit rate much lower than some other crystallographic screens. Interestingly none
of these bound at the kinase hinge region where fragments often bind but instead
were found at an allosteric site. The authors do not speculate on the low hit
rate, which could be due either to the shapeliness of the fragments or their
portliness, with 18-22 heavy atoms, considerably above the optimum suggested by Astex. The
fragments are available for screening at Diamond’s XChem, though they don’t
seem to have been used in the recent SARS-CoV-2 main protease screen.
We’ll cover three more papers next
week. In the meantime, stay safe and please leave comments!
Posts
5 comments:
worth noting that the screen was carried out with ADP blocking the hinge site
Thanks - missed this detail - that could definitely explain the lack of hits in the hinge region!
Hi Dan,
I think the main problem with fragments like these is excessive molecular complexity. A fragment with multiple HB acceptors and donors in its molecular structure can present these to water in a nearly optimal manner (although “frustration” of hydration of adjacent HB acceptors/donors can be factor). For efficient binding to target, the fragment needs to present these HB acceptors/donors to the target in an as optimal manner as it does to water. Molecular complexity is likely to be an even greater issue when the HB acceptors/donors are clamped together in a relatively rigid molecular framework (e.g. structure 3).
Even the humble hydroxyl group comes with molecular complexity baggage. Deploying the HB donor of a hydroxyl group makes it a better HB (and vice versa) and this cooperativity would be expected to lead to stronger interactions with solvent. If the hydroxyl substituent can’t deploy both its HB acceptor and donor when binding then its inclusion in the ligand structure is unlikely to be beneficial for affinity.
Personally, I would not include any of the fragments from the blog post in a generic screening library (unless it was a very large screening library). If designing (or selecting) fragments to hit a specific target then these ‘levels’ of molecular complexity might be appropriate.
Hi Pete,
You bring up a good point with regards to molecular complexity (which roughly correlates with size and shapeliness). The problem with molecular complexity is how to precisely define it; do you have a favorite method?
But even qualitatively these fragments are far from the "minimal pharmacophore" ideal espoused by Astex.
When I first got into selecting fragments at Zeneca, I used a selection tool named filter (described in this article ) that I’d built with the Daylight programming toolkits. The compounds in the core of the library were selected using with very tight substructural constraints (like the German legal system, everything was forbidden unless it was specifically permitted). The layers were added to the core by progressively relaxing the substructural constraints (I even used this strategy to design a library for a phenotypic screen that we ran around 2003). Some of the power of SMARTS notation lies in being able to impose views of chemistry on sets of chemical structures in an objective and transparent manner.
The molecular complexity model introduced by Mike Hann and colleagues is a great way to think about molecular recognition in the context of drug design (I am a big fan and have cited it on a number of occasions in my published work) but I have never found it remotely useful when selecting compounds for screening. It is actually very difficult to define molecular complexity metrics that are relevant to library design in an objective manner. I have used extent of substitution (basically number of substituents defined using SMARTS) to control molecular complexity when making compound selections. This way of looking at molecular complexity is closer to the concept of needle screening than to Mike’s model.
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