Crystallography first in fragment optimization: Binding-Site Purification of Actives (B-SPA)

At FBLD 2024, Frank von Delft (Diamond Light Source) announced the ambitious goal of taking a 100 µM binder to a 10 nM lead in less than a week for less than £1000. Fragment to lead optimization usually takes longer, as dozens or even hundreds of compounds need to be synthesized and tested. One way to speed things up is through “crude reaction screening,” otherwise known as “direct to biology,” in which unpurified reaction mixtures are tested directly. In a new (open-access) Angew. Chem. Int. Ed. paper, Frank, John Spencer, and collaborators at University of Oxford, University of Sussex, and Creoptix apply this approach to crystallographic screening.

 

The researchers were interested in the second bromodomain of Pleckstrin Homology Domain-Interacting Protein, or PHIP(2), an oncology target. As we discussed in 2016, they had previously run a crystallographic screen and identified multiple hits, including F709, which, despite having no measurable affinity, had good electron density and multiple vectors for optimization. Six separate libraries based on this fragment were constructed, with between 58 and 1024 targeted small-molecule products per library and up to four steps done without purification.

 

One challenge for crude reaction screening is assessing whether or not a reaction has actually generated product. Typically this is done by analytical liquid chromatography mass spectrometry (LCMS), but analyzing results manually is tedious. Fortunately academics have graduate students and postdocs, and it was presumably these intrepid souls who spent 17 days analyzing the 1876 small-molecule products attempted.

 

I can say from personal experience that spending hours perusing LCMS chromatograms is not enjoyable, so the researchers built an automated tool called MSCheck, which appears to be freely available here. This showed 83% agreement with the manually curated data, and even identified additional true positives that had been missed. All together 1077 of the reaction mixtures had the desired product, with success rates for the various libraries ranging from 39% to 97%.

 

The successful reactions were soaked into crystals and screened, and nearly 90% of these generated usable data. A total of 29 crystals had interpretable density in the ligand binding site: 7 were starting materials and 22 were desired products. Of the products, 19 bound with the piperazine core in a similar position as the initial fragment, while three bound in an alternate manner.

 

Of course, the whole point of this exercise is to find improved binders, so the researchers tested pure versions of each of the 22 crystallographic hits in two different assays. Only compound PHIP-Am1-20 had measurable affinity, with modest ligand efficiency.

This is not the first example of crude reaction screening by crystallography; we wrote about REFiL_x and a related technique in 2020. In one of those papers, the crude reaction mixtures were assessed by SPR as well as crystallography, which revealed that the crystallographic screen missed some binders, and there is no reason to think the same did not happen here. Indeed, molecules that bind tightly in a different conformation may be more likely to shatter the crystal lattice and thus go undetected.

 

The researchers state that for non-crystallographic crude reaction screening “only strong assay readouts are informative.” But is this bug, or a feature? A 2019 publication that used crude reaction screening to identify KRAS ligands (which I wrote about here) used an assay cascade to quickly select the most potent hits. Even the fastest crystallographic screens can’t compete with plate-based assays in terms of speed.

 

Perhaps PHIP(2) is a particularly challenging test case. As we discussed in 2022, multiple computational screens performed poorly in predicting crystallographic binding modes of ligands for this protein. But as I wrote at the time, it may be that many crystallographic ligands are just too weak to be useful.

 

Although there is a strong case for using crystallography first for finding fragments, I am not yet convinced the same applies for optimizing fragments.