Diverse function – not structure – in fragment libraries

Successful fragment-based lead discovery typically starts with a good library. But what is “good”? Given that most fragment libraries are small, diversity is generally prized. The idea is to cover as much chemical space as possible with the fewest molecules. When most chemists hear the word diversity they think of structural diversity; tetrahydrofuran looks quite different from pyridine, for example. Functionally though, both contain a hydrogen bond acceptor. In a paper recently published (open access) in J. Med. Chem., Charlotte Deane and collaborators at University of Oxford and Diamond Light Source argue that functional diversity is more important.

 

Frank von Delft and his XChem colleagues at the Diamond Light Source have been screening dozens of targets crystallographically, many of them using the DSI-poised library, designed to enable rapid elaboration of hits. (We described it here). For the present analysis, the researchers considered ten diverse proteins (maximum pairwise sequence identity of 27%) that had all been screened against 520 fragments. Of these, 225 bound to at least one target.

 

The researchers considered what types of interactions the bound fragments made with the protein at either the residue or atomic level. For example, a fragment might serve as a hydrogen bond acceptor to the hydroxyl group of a serine residue. These interaction fingerprints, or IFPs, were calculated and compared.

 

Interestingly, there was no correlation between fragments that made similar IFPs and their structural similarity. In other words, “structurally dissimilar compounds can exploit the same interactions.” Moreover, many different fragments made similar or identical interactions: “structurally diverse fragments can be described as functionally redundant.”

 

In fact, just 135 fragments could make all the interactions observed for the 225 fragments. Some made more novel interactions than others, with “promiscuous” fragments that bound to multiple targets tending to be more informative.

 

The top 100 of these 135 functionally diverse fragments tended to have molecular weights between 175 and 240 Da and 12 to 16 non-hydrogen atoms, putting them comfortably within rule of three space. Interestingly, fragments that never hit any target skewed smaller, with many having molecular weights less than 175 Da and fewer than 12 non-hydrogen atoms; this is slightly at odds with work from Astex which found many tiny fragment hits.

 

The researchers considered sub-libraries consisting of either these functionally diverse fragments, randomly selected fragments, or structurally diverse fragments. The number of interactions discovered was significantly higher for the functionally diverse sets of fragments than for the other sets.

 

On one level the findings are not surprising: the whole concept of bioisosterism relies on the fact that different functional groups can make the same interactions, meaning that structurally disparate fragments can be functionally redundant. This suggests that libraries could be optimized to capture more information with fewer molecules. How to do so prospectively is not entirely clear, but laudably the researchers have provided chemical structures for all the fragment hits in the Supporting Information. It may be worth adding some of the functionally diverse fragments to your library; perhaps some enterprising vendor will start selling the top 100 as a set.

Fragments vs human Adensoine 2a Receptor using SPR

Last week we highlighted the use of surface plasmon resonance (SPR) to find ligands against RNA. Although RNA is not a typical protein target, it is at least normally free in solution. Targets such as GPCRs are more technically challenging because they are bound within membranes. Challenging, but not impossible, as illustrated by this post from 2012. A new ACS Med. Chem. Lett. paper by Reid Olsen, Iva Navratilova, and colleagues at Exscientia, University of Dundee, and AstraZeneca provides the latest example.

 

Navratilova and colleagues previously described using SPR to screen the β2 adrenergic receptor. In the new paper, the researchers studied the human adenosine 2a receptor (hA_2AR), a “rheostat for energy homeostasis” that also plays a role in cancer immunotherapy. hA_2AR is one member of a small family of adenosine receptors, and the researchers expressed all four of them, each with a polyhistidine tag that could be captured in the SPR instrument using a nickel-NTA sensor chip. Other labs (such as Heptares) have used mutant, stabilized forms of GPCRs, but here the researchers used native proteins and stabilized them by crosslinking them to the surface of the chip. They confirmed that these GPCRs bound known ligands with similar affinities to those reported in the literature.

 

Next the researchers screened a library of 656 fragments, each at 50 µM, against hA_2AR. This led to 72 potential hits taken into dose-response experiments, of which 17 confirmed with affinities ranging from 1.1 to 410 µM. All the sensorgrams are shown, as are the structures of the fragment hits. These confirmed hits were also screened against A₁, A_2B, and A₃; most of the fragments bound to all the receptors, though two were selective for hA_2AR.

 

To assess where the fragments bind, the researchers added a known high-affinity ligand; ten of the fragments could be competed, while seven showed less or no competition, suggesting that they may bind to an allosteric site.

 

GPCRs biology is complicated, and just because a ligand binds does not mean it will have any effect on signaling. In cell experiments, none of the fragments behaved as agonists, but five fragments could act as antagonists of a known agonist. Another fragment seemed to increase the signal, suggesting it is an allosteric modulator. As the researchers conclude, “while SPR can screen fragment-like molecules that allow for extrapolation of extremely large and diverse chemical spaces, it cannot predict the biological activity of these binders."

 

Nonetheless, this paper provides a nice guide on how to use SPR, with its low protein requirements, to screen GPCRs. And the fragments disclosed could be interesting starting points for medicinal chemistry.

Fragments vs RNA with SPR: A guide

Fragment-based lead discovery on RNA has a long history: the first mention on Practical Fragments was in 2009. Most often, various NMR methods have been used (see this example from last year), though isothermal titration calorimetry (ITC) is also effective. However, both of these techniques generally require considerable amounts of RNA. In a recent Biochemistry paper, J. Winston Arney and Kevin Weeks describe using SPR, which could increase the speed and ease of screening RNA.

 

Non-specific binding is a significant problem in characterizing RNA ligands. RNA is negatively charged, and many ligands are positively charged, leading to non-specific interactions. In a typical SPR experiment, the target is bound to a surface and the analyte is allowed to flow over the immobilized target; binding causes a change in refractive index that can be detected. However, if the analyte interacts non-specifically with the target, this will also be detected. For high affinity ligands the non-specific interactions may be minimal at low concentrations, but for low-affinity ligands such as fragments, it can be difficult to differentiate specific from non-specific binding.

 

SPR experiments generally use a reference cell, in which the analyte is allowed to flow over the surface in the absence of target; this signal is then subtracted from the target channel. Arney and Weeks decided to use a reference cell containing mutant RNA not expected to bind to the ligand.

 

The researchers developed their approach using two different riboswitches, each with known high-nanomolar ligands. Immobilizing the riboswitches to the chip and flowing ligand led to non-specific binding at concentrations of 100 µM or so. However, when the reference cell contained a mutant riboswitch designed not to bind to the ligands, this non-specific binding could easily be subtracted, leading to simple single-site binding models.

 

Of course, creating a mutant RNA assumes you already know where your ligand binds, which is not true if you are looking for ligands to a new target. To increase the generality of their approach, the researchers used a different riboswitch or a completely arbitrary RNA for the reference. These also worked, though not quite as well as the targeted mutants.

 

Finally, the researchers tested a dozen RNA-ligand pairs that had previously been rigorously characterized. Importantly, these varied considerably in affinity, from 8 nM to 2 mM. Most of them were also fragment-sized, with molecular weights as low as 119 Da. The correlation between SPR dissociation constants and those reported in the literature was excellent.

 

The technique does have limitations. First, the RNA-bound surfaces do seem somewhat unstable over a period of days. Also, larger RNAs present technical challenges, though the researchers do state that they have been able to examine molecules as large as 300 nucleotides. Overall this looks like a nice approach for measuring RNA-ligand affinities.

Solving structures with selective labeling and NMR2

Protein-detected NMR first enabled fragment-based lead discovery way back in 1996, but improvements in crystallography have now allowed synchrotrons to surpass big magnets as preeminent tools to determine how fragments bind to proteins. One of the major challenges in NMR is assigning the chemical shift values of atoms in all the individual amino acid residues. A technique called NMR Molecular Replacement (NMR²) sidesteps the need for this tedious, time-consuming process. A refinement to this technique, making it more broadly applicable, has just been published (open-access) in Sci. Reports by Julien Orts (University of Vienna), Martin Scanlon (Monash University) and collaborators.

 

As we discussed previously, NMR² relies on intensive calculations using experimental intermolecular NOEs between a protein and a ligand to generate a model. Although the method does not require assignment of backbone or side chain chemical shifts, it does require high-quality spectra. For example, if the spectra of several amino acid residues overlap it is impossible to distinguish them (this applies to conventional NMR methods too). The researchers realized that one way to simplify the spectra is through selective labeling, in which the methyl groups of the amino acid residues alanine, isoleucine, leucine, valine, and threonine are isotopically labeled with ¹³C. Going one step further, the entire protein can be deuterated (rendering most of the protein invisible to NMR), while these methyl groups retain ordinary hydrogen atoms.

 

For the present study, the researchers focused on the protein EcDsbA, an antibacterial target we’ve written about previously. They selectively labeled methyl groups so that, in isoleucine, leucine, and valine, only one of the two methyl groups was labeled. That reduced the total number of protons to just 6% of the unlabeled protein.

 

The researchers then solved the structure of EcDsbA with a previously identified ligand. At 23 heavy atoms the ligand is on the large side, though with an affinity of just 0.9 mM it presents a difficult test case. A total of twelve intermolecular NOEs were used in NMR² to build a model of the complex. One challenge with NMR² is that there may not be a single solution. For example, if two methionine methyl groups are both near a ligand, it may be impossible to determine a unique binding mode. This turned out to be the case, and the top two structures had different positions for a carboxylic acid group and a phenyl in the ligand.

 

To benchmark NMR², the protein-ligand complex was also determined using conventional two-dimensional techniques (HADDOCK and CYANA, which made use of assigned chemical shifts) as well as X-ray crystallography. These all agreed with the NMR² model in placing a phenylpropyl moiety from the ligand in a hydrophobic groove, but they differed in the placement of the carboxylic acid and the other phenyl moiety: the top scoring NMR² model agreed with the crystal structure and the CYANA NMR structure but differed from the HADDOCK structure, which was similar to the second-best NMR² model. Before assuming that the crystallographic structure is correct, though, it is worth noting that the ligand makes crystal contacts with a neighboring protein, and the electron density around the ambiguous phenyl is weak.

 

This is a nice demonstration of the utility of NMR². It seems to provide similar information as classic NMR methods, but the time taken is “orders of magnitude” less. And selective labeling should make NMR² applicable to even larger proteins. I look forward to seeing more people use this strategy.

What rings are found in drugs?

Recently we highlighted the “Ring Replacement Recommender,” which provides suggestions for how to improve affinity by replacing one ring with another. The recommendations are based on an analysis of hundreds of thousands of molecules. But what about the rings found in actual drugs? This is the focus of a J. Med. Chem. paper by Richard Taylor and collaborators at UCB and Bohicket Pharma Consulting.

 

The researchers examined FDA-approved and investigational drugs with disclosed structures as of January 2020. These were fragmented into component “ring systems” for analysis. (Ring systems include not just monocycles but fused rings, such as purine. For example, sotorasib consists of four ring systems: benzene, pyridine, piperazine, and pyrido[2,3-d]pyrimidin-2-one.) More than 90% of drugs contain at least one ring.

 

Approved drugs have just 378 unique ring systems in total – a small increase from when the researchers examined approved drugs in 2014. The phenyl ring is found 727 times, with pyridyl (86 examples) a distant second, followed by piperidine (76 examples) piperazine (65 examples) and cyclohexane (47 examples). After that the numbers drop off sharply, with pyrazine in 50^th place with just six examples and fluorene in 100^th place with three examples.

 

Investigational drugs at first appear to be more diverse, with 450 unique ring systems, 280 of which are not found in approved drugs. Of these 280, pyridazine is the most common, with nine examples, followed by oxetane, with seven, but things quickly become less common from there, with 271 of the ring systems found just once. In contrast, ring systems found in drugs are found in multiple compounds, and in fact two thirds of investigational drugs only contain previously used ring systems.

 

Many of the new ring systems are closely related to those found in approved drugs, with nearly half differing by at most two atoms. Perhaps because of this the overall properties of the ring systems are similar between approved and investigational drugs, with no significant differences in heteroatom ratio, percentage of sp³ centers, or number of rings per system.

 

What new opportunities exist? The researchers identified nearly half a million synthetically accessible ring systems and winnowed these down to 3902 ring systems that have similar heteroatom ratios to those found in drugs and differ by at most two atoms. This attempt to explore new chemical space is similar to earlier work from the same group (here) as well as that from others (here, here and here).

 

The researchers also examined growth vectors and combinations of rings, the latter by using graph theory. These analyses suggest that investigational drugs do have greater variety. In other words, even if the component rings are shared with approved drugs, they might be combined in new ways.

 

Whether certain ring systems are more likely to fail in the clinic was intentionally not addressed, due to the difficulty of assessing why the failures occurred. For example, drugs can fail for commercial reasons; a company may choose to drop a drug against a particular target rather than be tenth to market. And even when the failure is due to the science, it might not be an indictment of the drug itself. Verubecestat did lower β-amyloid levels in people as designed, but had no effect on Alzheimer’s disease.

 

This paper is a fun read, and it will likely provide ideas for scaffold hopping and library design. It is also a reminder of how much chemical space remains to be explored.