12 January 2015

Choosing fragments and assays

One of the advantages of running lots of fragment screens is that it generates lots of data that you can mine for general trends and insights. Astex and Vernalis have both done this; in a paper just published online in J. Biomol. Screen. Peter Kutchukian and (former) colleagues at Novartis provide their own meta-analysis of 35 fragment screening campaigns.

The Novartis fragment library consists of 1400 fragments with molecular weights ranging from 102 to 306 Da and logP values from -2.19 to 3.9. This library has been screened against dozens of targets using a variety of different methods. The researchers looked at the hit rates and used Bayesian methods to try to answer three broad questions.

What makes a fragment amenable for fragment-based screening?
Many people have found that some fragments hit many targets while others hit none, and the results here are no different. Over a set of 20 targets, only 37% of fragments came up as a hit, as opposed to the 54% that would be expected if the odds of hitting a target were the same for all fragments (and using the hit rates actually observed). Correspondingly, some fragments hit more targets than expected. Indeed, 1.4% hit six or more, which is orders of magnitude more than would be expected by chance. Given justifiable concerns about artifacts, one might be tempted to dismiss these hits, but the researchers found that these frequent hitters turn out to be more likely to generate crystal structures than other active fragments. In other words, these are privileged fragments (think 7-azaindole).

Do these privileged fragments have anything in common? Previous work from Astex and Vernalis has suggested that fragment hits tend to be slightly more lipophilic than non-hits, and this trend is all the more apparent here. In fact, fragments that hit more than five targets had a median logP of 2.47 versus 1.45 for fragments that hit just a single target. Promiscuous fragments also tended to be slightly larger than other fragments, in contradiction to the molecular complexity hypothesis. They also tended to have more aromatic bonds, fewer rotatable bonds, and higher solubility.

How do hits from different fragment screening technologies and target classes compare with each other?
Do different target classes find different sets of hits? An analysis of substructures identified in hits against various target classes suggests the answer is yes. Certain substructures are preferred by kinases, for example, while other substructures are preferred by serine proteases. This suggests that building fragment libraries specific to a target class may be productive, though certainly not essential.

Regarding screening technologies, the researchers examined both biophysical (for example NMR, SPR, and DSF) as well as biochemical (such as fluorescence) assays. In general, the hit rates were similar for different technologies, with two exceptions. In SPR, a number of fragments nonspecifically interacted with the surface of the chip, giving a higher number of false positives. On the other hand, DSF gave an anomalously low hit rate, and on closer inspection the researchers found that about 1% of the fragment library appeared to denature proteins.

Interestingly, there was less overlap of hits between biophysical methods and biochemical methods than among biophysical methods or among biochemical methods. In other words, hits from an NMR (biophysical) screen were less likely to be found in a fluorescence (biochemical) screen than in an SPR (biophysical) screen. This is similar to the results of a previous study, though not stated explicitly there.

What is the best way to pair FBS assay technologies?
Given this finding, the researchers suggest that, to find the greatest number of hits, it is best to pair a biochemical method with a biophysical method. Of course, this assumes that the goal is to find as many hits as possible, but these may come at the expense of false positives. Still, if you’re going after a tough target, you want to find every possible hit you can. And if you are more interested in weeding out false positives than finding every viable hit, choosing fragments that hit in both a biochemical and a biophysical assay is probably a good starting point.

This is a fascinating paper and contains far more data than can be practically summarized here. It will be fun to see whether similar analyses, from different organizations, come to similar conclusions. 

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