Fragment-based chemogenomics

An article with an intriguing title appeared recently in Drug Discovery Today: Small and colorful stones make beautiful mosaics: fragment-based chemogenomics. Iwan de Esch and colleagues at VU University Amsterdam and IOTA Pharmaceuticals define chemogenomics as:

The discovery of new connections between chemical and biological space leading to the discovery of new targets and biologically active molecules.

Thus, “fragment-based chemogenomics” is:

An approach to accurately characterize protein-ligand binding sites by interrogating protein families with libraries of small fragment-like molecules.

Like the “small, colorful stones” (or tesserae, though presumably not quantum) in a mosaic, fragments can be used to build up an understanding of protein-ligand interactions.

The authors start by constructing a fragment library consisting of 1010 compounds, most of which follow the rule of 3 (or, as Teddy would have it, the Voldemort Rule). An upper limit of 22 non-hydrogen atoms was used, and it looks like the lower limit was 7 atoms (vote on your own lower limit in the box at the right!), with a mean molecular weight of 211 Da. Most of the fragments were originally made as synthetic intermediates, but 117 were purchased specifically to add diversity to the library.

Having assembled the library, the authors then screened it against six targets: 4 GPCRs, 1 ion channel, and 1 kinase. Most of the assays involved displacement of a radioligand; hits were found against all of the targets, with hit rates ranging between 1 and 10%. Perhaps not surprisingly, different proteins preferred fragments with different physicochemical properties: histamine receptors selected polar, positively charged fragments (like histamine itself), while the kinase preferred rigid, hydrophobic, neutral fragments.

Although none of the fragments hit all six targets, a good proportion bound several (up to four). These tended to be larger than average, though no more hydrophobic, in contrast to results from other studies (see here and here).

Some of the most interesting results describe activity “cliffs,” ways to classify SAR observations for the GPCRs. An affinity cliff consists of two closely related fragments, one of which is active, the other of which is not, while a selectivity cliff consists of two fragments, one of which is active for a set of proteins, while the other is selective for one or more. The researchers show several examples where small changes – the introduction of a single heavy atom, the contraction of a ring, or the reversal of an amide bond – ablates activity.

Although crystallographically-enabled fragment optimization is now possible for GPCRs, the activity-guided SAR described here should be accessible to more researchers working on a wider range of proteins, and should prove powerful for tackling targets where structures are still elusive.

Nature recently declared that “‘omics bashing is in fashion,” but I do think there is something here. Whether or not it deserves its own ‘ome is open to debate, so feel free to weigh in!