Fragments vs GPCRs – virtually vs experimentally

G protein-coupled receptors (GPCRs) are common drug targets that present challenges for fragment-based approaches. Biophysical studies of these membrane proteins are often difficult. Moreover, while many fragment-finding methods reveal binders, GPCR ligands can be agonists, inverse agonists, neutral antagonists, and more – and directing a search toward desired functionality can be tough (though see here). In a paper published earlier this year in Bioorg. Med. Chem. György Keserü and colleagues at Gedeon Richter and the Hungarian Academy of Sciences describe how they have tackled this problem.

The researchers were interested in the adrenergic α_2C receptor; agonists could be useful for a variety of indications, though selectivity is challenging. No crystal structure has been reported in the literature, so the researchers investigated a radioligand displacement assay as well as a cell-based functional assay (calcium mobilization) for agonists. A test set of 160 fragments from Maybridge was screened in both assays at 250 µM, giving 3 hits in the functional assay but a whopping 48 hits in the displacement assay. A 30% hit rate in an unbiased screen generally means something’s wrong, so the researchers chose to focus on the functional assay.

For the full screen, 3071 fragments having 9-22 non-heavy atoms were tested at 250 µM in the cell-based functional assay, resulting in 318 hits – a much higher rate than the initial set. However, when these were retested, only 86 reproduced, which the researchers attribute to variability in the cell-based assay. Many of the hits were also active against an unrelated GPCR; ultimately 16 were specific for the α_2C receptor and were also active in the radioligand displacement assay (as was one of the three original Maybridge hits). The chemical structures and activities of these molecules are shown in the paper; they are all quite potent with inhibition constants from 2-220 nM in the displacement assay, with correspondingly high ligand efficiency scores.

Despite the lack of a crystal structure, the researchers also performed a virtual screen of the same set of 3071 fragments using a homology model of the α_2C receptor. Two of the top 30 hits were fragments that had been discovered in the functional assay. Although this is not as impressive as another docking study on a different GPCR, it is certainly better than chance, and not too shabby considering the lack of an actual structure for the protein.

Next, the researchers attempted to find more potent analogs by testing compounds chemically related to their best hits. Some of these did show good potency in the radioligand displacement assay, but interestingly all of these were antagonists as opposed to the desired agonists. This is further evidence that gaining affinity may be easier than maintaining functionality.

As the authors concede (and we’ve noted elsewhere), the α_2C receptor has evolved to bind fragment-sized ligands. Still, the computational discovery of agonists is encouraging. It will be interesting to see whether such approaches will work against more difficult targets, such as peptidergic GPCRs.

Fragments vs DAPK3, computationally and experimentally

Computational approaches for discovering hits often involve sorting through many possibilities and examining a few closely. With luck, some of the predicted molecules will bind to the protein of interest. However, these don’t always bind for the “right” reason: sometimes a fragment predicted to bind one way will turn out to bind in quite a different manner. A recent Angew. Chem. Int. Ed. paper by Gisbert Schneider and colleagues at the ETH in Zürich and SARomics in Lund reports a possible example.

The researchers were interested in death-associated protein kinase 3 (DAPK3), which is implicated in several diseases. Previous work had shown that fasudil inhibits this kinase, though it hits others as well. Fasudil was used as a starting point for de novo fragment discovery using software called DOGS (Design of Genuine Structures). This is a scaffold-hopping approach in which virtual chemistry is used to generate readily accessible alternatives to a starting molecule. In this case, 347 of the 521 suggested inhibitors were fragment-sized. These were prioritized using in-house software, and compound 2 – one of the top hits – was chosen for synthesis and characterization.

Happily, compound 2 turned out to be fairly potent for its size, with impressive ligand efficiency. It is also quite different from fasudil (Tanimoto similarity = 0.16). Indeed, while fasudil is likely to be positively charged at physiological pH, compound 2 is likely to be negatively charged. Moreover, of 27 other kinases tested, compound 2 hit only one other with similar potency.

For those who have worked on kinases, compound 2 does appear unusual. A crystal structure of this molecule bound to DAPK3 revealed that it sits in the ATP-binding pocket but without making any conventional hydrogen bond interactions to the so-called hinge region of the kinase. Although no reported crystal structures show fasudil bound to DAPK3, structures with other kinases reveal the nitrogen of the isoquinoline moiety making a hydrogen bond to a backbone amide in this part of the protein.

The software used to prioritize compound 2 is based not on docking but on machine learning using the ChEMBL database, and the researchers were interested in what else this fragment might inhibit. Not surprisingly given the aryl sulfonamide moiety, several carbonic anhydrases came up, and two were confirmed experimentally.

Interestingly, the diuretic drug azosemide, whose physiological target is unknown, contains compound 2 as a substructure, and the researchers found that this molecule inhibits DAPK3 with low micromolar affinity. It also binds human carbonic anhydrase IX with similar affinity. The researchers suggest that these targets could at least partially explain the mechanism of the drug, as well as some of its side effects. It would be interesting to see cell data against these two targets, as well as the crystal structure of azosemide bound to DAPK3.

The ability to predict biological targets of molecules with the aid of machine learning would clearly be valuable (see also here). And of course new approaches for scaffold hopping are always valuable. In this case DOGS did retrieve an active (albeit odd) molecule when fed a conventional kinase inhibitor; it is as if you threw a ball and your dog fetched a slipper. I will be curious to see this applied to more systems.

Fragments vs PDE10A: growing potency and selectivity

People often wonder how selective fragments need to be. According to molecular complexity theory, the answer is “not very”. After all, it would be hard to get a decent hit rate with a library of just a few thousand fragments if they were too selective. In the case of kinases, experimental studies support this theory. Indeed, a single fragment has given rise to several drugs – one of which is approved. In a new paper in J. Med. Chem., William Shipe and colleagues at Merck demonstrate the utility of a non-selective fragment for another class of enzymes, phosphodiesterases (PDEs).

The human genome contains more than 50 different PDEs, which cleave phosphodiester bonds. PDE10A hydrolyzes cyclic guanosine monophosphate (cGMP) and cyclic adenosine monophosphate (cAMP) and is a potential target for schizophrenia. It has been pursued extensively, both with fragments (see for example here and here) as well as more traditional approaches.

The researchers started with a biochemical assay that screened each fragment at 200 µM; 60 of the 1600 tested gave > 80% inhibition. Nine of these were soaked into PDE10A crystals, producing seven structures, including compound 5, with impressive potency and ligand efficiency. Initial SAR by catalog led to the even more potent compound 6, which revealed that an amino group was tolerated and pointed nicely towards another pocket, offering a way for further elaboration.

Fragment growing from the amino group was accomplished through several rounds of parallel synthesis, with crystallography used to understand and optimize the binding interactions. Compound 9s showed particularly impressive low nanomolar potency, as well as at least 80-fold selectivity against nine other PDEs. In contrast, the initial fragment 5 was at most only 11-fold selective against any of the other PDEs.

Previous work with PDE10A had revealed another “selectivity pocket” nearby,  and the researchers further grew their molecule towards this, leading ultimately to compound 15h, with low picomolar affinity and at least >5900-fold selectivity against nine other PDEs. The compound also showed functional activity in a rat model, though it suffered from suboptimal pharmacokinetic properties.

This is a beautiful illustration of the power of combining fragment screening, structure-based drug design, and parallel synthesis. The researchers were able to gain more than a million-fold improvement in potency and take a marginally selective fragment to a highly selective lead. Of course, there is still plenty of work to do, and it will be fun to watch this story unfold.

Group efficiency

Ligand efficiency (LE) is one of the more controversial topics we cover at Practical Fragments. One critic asserted – incorrectly – that it is mathematically invalid. Another has stated that it is “not even wrong,” because the metric is predicated on standard state conditions and thus "arbitrary". (As he acknowledges, this also applies to the value and even the sign of the Gibbs free energy for a reaction.) A related metric that has received less attention is group efficiency (GE). In a paper just published in ChemMedChem, Chris Abell and colleagues at the University of Cambridge use this to help them optimize pantothenate synthetase (Pts) inhibitors.

Ligand efficiency is defined simply as the free energy of binding divided by the number of non-hydrogen, or “heavy” atoms (often abbreviated as HAC for heavy atom count) in the ligand. (Geek notes: although the binding energy is negative, LE is expressed as a positive number, so LE = - ΔG / HAC. Also, on Practical Fragments, units are assumed to be kcal mol^-1 per heavy atom unless otherwise stated.)

Instead of focusing on a single ligand, group efficiency compares two ligands that differ by the presence or absence of a given group of atoms. To calculate GE, you simply subtract the ΔG values for the two ligands and divide by the number of heavy atoms in the group. For example, if you add a methyl group to your molecule and are lucky enough to get a 100-fold pop in potency, the methyl group has a group efficiency of 2.7 kcal mol^-1 per heavy atom.

The current paper chronicles lead discovery for Pts, a potential target for tuberculosis. Previous screening efforts followed by fragment growing and fragment linking had generated low micromolar and high nanomolar inhibitors. The researchers turned to group efficiency to improve their molecules further.

As expected from ligand deconstruction studies (see for example here, here, and here), different portions of a molecule are likely to have vastly different group efficiencies. Indeed, this turned out to be the case here: the acetate moiety had high group efficiency, whereas the pyridyl moiety had lower group efficiency. Thus, the researchers set out to replace the pyridyl with ten diverse substituents. Happily, one of these improved the dissociation constant to 200 nM as assessed by isothermal titration calorimetry of the fully elaborated molecule. Compound 11 also showed reasonable enzyme inhibition in a functional assay.

One potential problem with group efficiency is that it assumes the molecules being compared bind in a similar fashion, which is not always a safe assumption. In this case, the researchers obtained a crystal structure of compound 11 bound to the enzyme, which not only revealed that it binds similarly to compound 5, but also suggested that inserting a methylene may improve binding. The resulting compound 20 showed better activity in the inhibition assay, as well as activity against M. tuberculosis in a cell assay (though unfortunately the dissociation constant was not reported).

This paper offers a clear illustration of how group efficiency can be useful for prioritizing which portions of a molecule to change. In some cases, such as the example here, it makes sense to try to replace groups with low group efficiency. On the other hand, the core fragment may bind in a hot spot, and so just a slight tweak can dramatically boost potency. As with lead optimization in general, there are many paths – both to enlightenment and to perdition.

NMR poll results

The results of our latest poll are in – thanks to all who participated! Of the 119 people who responded to the first question, 87% said they use NMR for finding or validating fragments. Even if we assume that responses were biased towards NMR aficionados, big magnets are clearly popular.

The second question asked about specific NMR techniques. If everyone who said they used NMR in the first question also answered the second, this means the average user applies more than 3 different techniques; I’ll let Teddy weigh in to see whether this matches his experience.

One surprise for me was that, although many techniques are widely used, none are nearly universal; even the most popular methods seem to be used by just over half of respondents.

Among ligand-detected methods (blue in the figure), STD ranks at the top, with line-broadening, WaterLOGSY, and fluorine-based techniques all tied for second place.

Protein-detected methods (red in the figure) also appear quite healthy, with nearly as many respondents using ¹⁵N-HSQC/HMQC as STD.

Finally, 11 of you said you use "other" techniques. We didn't include TINS, even though it seems quite useful, because it is only available through the services of ZoBio. But what else is out there?