Docking for finding and optimizing fragments

Docking can sometimes seem like the Rodney Dangerfield of FBDD: it don’t get no respect. In last year’s poll of fragment finding methods, computational approaches ranked in seventh place. This partly reflects the largely biophysical origins of FBDD, but it is also true that ranking low affinity fragments is inherently challenging. Still, the continuing rise in computational power means that methods are rapidly improving. A recent paper in J. Med. Chem. by Jens Carlsson and collaborators at Uppsala University, the Karolinska Institute, and Stockholm University illustrates just how far they can take you.

The researchers were interested in the enzyme MTH1, whose role in DNA repair makes it a potential anti-cancer target. The crystal structure of the protein bound to an inhibitor had previously been reported, and this was used for a virtual screen (using DOCK3.6) of 300,000 commercially available molecules, all with < 15 non-hydrogen atoms, from the ZINC database.

Finding fragments is one thing, but one really wants slightly larger, more potent compounds to begin lead optimization. Thus, the top 5000 fragments were analyzed to look for analogs with up to 6 additional non-hydrogen atoms among the 4.4 million commercial possibilities. This led to 118,421 compounds, each of which was then virtually screened against MTH1. Of the initial 5000 fragments, the top 1000 that had at least 5 analogs with (predicted) higher affinity were manually inspected. Of 22 fragments purchased and tested in an enzymatic assay, 12 showed some activity, with the 5 most active showing IC₅₀ values between 5.6 and 79 µM and good ligand efficiencies.

Since each of these fragments had commercially available larger analogs, the researchers purchased several to see if these did indeed have better affinities. Impressively, this turned out to be the case: both compounds 1a and 4a bound more than two orders of magnitude more tightly than their fragments. Interestingly, while the researchers were unable to obtain crystal structures of fragments 1 and 4 bound to MTH1, they were able to obtain crystal structures of 1a and a close analog of 4a, and these bound as predicted.

Of course, not everything worked: in the case of one fragment, among 19 commercial analogs purchased, the best was only 7-fold better. The crystal structure of this initial fragment bound to MTH1 was eventually solved, revealing that it bound in a different manner than predicted, thus explaining the modest results. In another case the most interesting commercial analogs turned out not to be available after all, but during the course of the study a different research group published a low nanomolar inhibitor with the same scaffold.

One notable aspect of this work is going from fragments to more potent leads without using experimentally determined structural information, something the majority of respondents in our poll earlier this year said they would not attempt. Although such advancement is not unprecedented, published examples are still rare.

In some ways this work is similar to the Fragment Network approach we highlighted last month, the key difference being that while Fragment Network was focused on looking for other fragments, this is focused on finding larger molecules. But how general is it? The researchers found that, while there are a median of just 3 commercial analogs in which a fragment is an exact substructure of a larger molecule, this increases to 700 when the criterion is relaxed to similarity (for example compound 1 and 1a). These numbers undoubtedly become even more favorable for organizations with large internal screening decks.

Eight years ago I ended a post about another successful computational screen with the statement that “the computational tools are ready, as long as they are applied to appropriate systems.” This new paper demonstrates that the tools have continued to improve. I expect we will see computational fragment finding and optimization methods move increasingly to the fore.