You Probably Already Knew This...

Academics can spend time and resources doing, and publishing, things that people in the industry already "know".  This keeps the grants, the students, the invitations to speak rolling in.  It also allows you to cite their work when proposing something.  This is key for the FBHG community.  There are many luminaries in the FBHG field, and we highlight their work here all the time. Sometimes, they work together as a supergroup.  Sometimes, Cream is the result.

Brian Shoichet and Gregg Siegal/ZoBio have combined to work together.  In this work, they propose to combine empirical screening (TINS and SPR) with in silico screening against AmpC (a well studied target).  They ran a portion of the ZoBio 1281 fragment library against AmpC.  They got a 3.2% active rate, 41 fragments bound.  6 of these were competitive in the active site against a known inhibitor.  35 of 41 NMR actives were studied by NMR; 19 could have Kds determined (0.4 to 5.8 mM).  13 fragments had weak, but uncharacterizable binding; 3 were true non-binders. That's a 90% confirmation rate.  34 of 35 were then tested in a biochemical assay.  9 fragments had Ki below 10 mM.  Of the 25 with Ki > 10mM, one was found to bind to target by X-ray, but 25A from the active site.  They then did an in silico screen with 300,000 fragments and tested 18 of the top ranked ones in a biochemical assay.  

So, what did they find? 

"The correspondence of the ZoBio inhibitor structures with the predicted docking poses was spotty. "  and "There was better correspondence between the crystal structures of the docking-derived fragments and their predicted poses."

So, this isn't shocking, but it is good to know.  This is also consistent with this comment.  So, the take home from this paper is that in silico screening can help explore chemical space that the experimentally much smaller libraries miss.  To that end, the authors then do a a virtual experiment to determine how big a fragment library you would need to cover the "biorelevant" fragment space [I'll save my ranting on this for some other forum].  Their answer is here [Link currently not working, so the answer is 32,000.]

Picomolar beta-lactamase inhibitors with help from fragments

In 2009 Practical Fragments highlighted a computational fragment screen against AmpC beta lactamase, a challenging antibacterial target. That paper described several millimolar fragments, and at the time we noted, “it remains to be seen whether these fragments can be advanced to low nanomolar inhibitors, but at least fragment-screening has provided many new starting points.” In a new paper in PNAS, Brian Shoichet and colleagues at UCSF, along with collaborators in Italy and France, have used the fragments to discover not just nanomolar, but picomolar inhibitors.

The researchers had previously identified reversible covalent inhibitors containing boronic acids, some of which were quite potent. However, they had reached an affinity plateau, and the compounds had only modest activity against bacteria in vitro. By examining the crystal structures of some of these molecules bound to AmpC, and superimposing these on the crystal structures of some of the fragments bound to the protein, they were able to come up with new ideas. For example, adding the tetrazole of fragment F3 to their previously reported compound 9 led to a low nanomolar inhibitor, and adding a small hydrophobic substituent improved the potency to an impressive 50 picomolar. Similar strategies worked with other fragment-inhibitor combinations.

AmpC beta lactamase degrades cephalosporin antibiotics, and the researchers demonstrated that many of their new molecules were effective at restoring sensitivity to otherwise resistant bacterial strains in vitro and, in the one case tested, in a mouse survival model.

This paper also addresses Teddy’s recent question, what is FBDD? Although the fragments described in 2009 were not themselves advanced, the information they provided was essential to moving the project forward. As the researchers note:

Whereas fragments are widely used to nucleate early discovery, this study suggests that they also may be used to guide late-stage optimiziation into chemotypes and geometries that would be hard to systematically sample by other methods.

This is all the more impressive given the relatively low ligand efficiency of the initial fragments. So though purists may argue whether this work is technically FBDD, it is certainly a nice example of fragment-assisted drug discovery, in this case merging parts of fragments with other molecules.

More on DOCKing fragments and sampling chemical space

A few weeks ago, we highlighted a paper from Brian Shoichet’s group at UCSF demonstrating that computational screening could successfully identify fragments binding to a protein target, and that the binding modes predicted were actually observed experimentally. A companion paper just published online in PNAS now extends these results, and also beautifully illustrates that it is possible to cover much more chemical space with fragments than with lead-like molecules.

Denise Teotico, Shoichet, and colleagues used the program DOCK 3.5.54 to screen 137,639 fragments against AmpC beta-lactamase, a bacterial protein responsible for antibiotic resistance. The protein had previously been the target of HTS and computational screens of drug-like like molecules. The computational screens had modest success rates (2-7%), but the HTS screen was a total bust: of the more than 1200 hits from the 70,000+ compound screening collection, more than 95% of these turned out to be false positives, mostly aggregators, with just a few dozen true inhibitors, all of which turned out to be covalent (irreversible).

In contrast, of the 48 high-scoring fragments that were experimentally tested, 23 had Ki values better than 10 mM, for a hit rate of 48%. The authors also assessed potential for false negatives by choosing 20 random fragments and testing these for inhibition; only one showed inhibition (with a Ki value of 3.1 mM), and this molecule had scored in the top 5% of docked fragments.

The paper presents a fascinating empirical test of the Hannian chemical complexity hypothesis. Starting with the 23 active fragments, the researchers calculated how many lead-like molecules (up to 25 non-hydrogen atoms) could contain these fragments. Of the roughly 47,000,000,000 to 430,000,000,000 possible lead-like molecules, only 675 are commercially available. By repeating this analysis with fragment-sized molecules (up to 17 non-hydrogen atoms), the size of the haystack was reduced by six orders of magnitude: only about 10,000 possible molecules contain these fragments, of which 93 are commercially available. Moreover, many of the active fragments represent unique chemotypes not previously observed in AmpC inhibitors. As the authors note:

The chances of discovering interesting chemotypes for biological targets is many orders of magnitude higher when targeting molecules in the fragment weight range than even at slightly higher size ranges.

But, as the paper asks, “are the docking predictions right for the right reasons?” The researchers solved the crystal structures of 8 fragments bound to AmpC. Four of these reproduced the docking predictions well, two were somewhat different, and two were way off. In these last two cases, the protein itself adopted different conformations than had been used in the docking studies.

Protein conformational flexibility is remarkably common, and likely to be a persistent difficulty for computational methods. Clearly, current computational methods can’t identify all possibilities, particularly with fluxional proteins. Still, especially with relatively rigid proteins, computational fragment-screening may reveal chemotypes that HTS won’t.

A notable feature of the fragments is their relatively poor ligand efficiency: with one unusual exception (a phosphinate), all of the active fragments have ligand efficiencies less than 0.3 (kcal/mol)/atom. AmpC has a large, open active site, and the authors suggest that the failure of other hit-ID methods against this target may reflect issues such as solubility.

It remains to be seen whether these fragments can be advanced to low nanomolar inhibitors, but at least fragment-screening has provided many new starting points. And the paper demonstrates, once again, that triaging a fragment set computationally can be an effective means for concentrating the needles in a haystack.