Fragments vs DsbA: towards a chemical probe

Despite its ubiquitous use as a model organism, Escherichia coli causes nearly a million deaths each year worldwide. Antibiotics provoke rapid selection for resistance and are becoming ineffective. An interesting alternative is to inhibit virulence factors. Doing so won’t directly kill the bacteria but instead reduce its infectivity, a trait that might be subject to less evolutionary selection.

 

The oxidoreductase DsbA facilitates disulfide bond formation in other bacterial proteins and is a key regulator of resistance. Martin Scanlon’s lab at Monash University has been pursuing this enzyme for some two decades; we described some of their work in 2015. In two recent papers, he and his colleagues describe progress towards a chemical probe.

 

DsbA has more than 300 protein substrates that bind in a shallow, hydrophobic groove. The lack of deep pockets or specific recognition elements makes finding small molecule ligands particularly challenging. Three years ago we highlighted fragment screens that identified two dozen hits in this groove. Intriguingly, that screen also identified a couple fragments that bind in a cryptic pocket close to the groove. This pocket is the focus of a paper published in Angew. Chem. late last year by Martin and collaborators at Monash University, La Trobe University Bundoora, and Scripps.  

 

Crystallography revealed that compound 1 binds in a pocket that is completely enclosed by DsbA. Twenty commercial analogs were purchased and tested by protein-observed [¹⁵N,¹H]-HSQC NMR. Six bound to the protein, but NMR suggested all bound in the hydrophobic groove, not in the cryptic pocket. Undeterred, the researchers made and tested a few dozen analogs, some of which did indeed bind the cryptic pocket and also had slightly higher affinities as measured by NMR and SPR.

 

How do the fragments get inside a pocket with no apparent entrances? Computational, protein-observed NMR, SPR, and HDX experiments suggested that DsbA is dynamic and one region can open up to allow access of the fragments. Interestingly, the fragments bind preferentially to the oxidized (active) form of DsbA, a fact that makes sense given that this state is more dynamic, allowing readier access to the pocket.

 

Unfortunately, the affinity of the best fragments is only around 150 micromolar. The small size of the cryptic pocket makes further affinity improvements unlikely, so the researchers sought to break the bounds of this pocket to gain added affinity. This is the focus of a paper just published in J. Med. Chem. by Martin, Bradley Doak, and collaborators at Monash, Vernalis, University of Western Australia, and The University of Sydney.

 

The researchers first built a small set of compounds that would break out of the pocket. Compound 5 had slightly worse affinity, as measured by SPR, but crystallography confirmed that the alkyne does in fact protrude as designed. A small set of analogs led to compound 13, with mid micromolar affinity. This compound was nearly 30-fold more potent than its enantiomer, with the hydroxyl moiety displacing a conserved water to make hydrogen bond interactions with the protein.

 

To gain additional interactions in the hydrophobic groove, the researchers chose direct-to-biology, screening crude reaction mixtures without purification, an increasingly popular strategy as we noted last week. In this case the researchers used automated flow reactors, allowing air- and moisture-sensitive organometallic chemistry. A set of 92 compounds was made and tested by off-rate screening (ORS) SPR and affinity-selected mass spectrometry (ASMS). Four crude hits were remade, purified, and tested, and compound 17 came in as a low micromolar binder both by SPR and ITC. This molecule also inhibited the enzyme in a functional assay and even showed some activity in a bacterial swarming motility assay.

 

Further improvements in potency will be needed to obtain a chemical probe, let alone a drug, but these two papers describe meaningful progress. They also provide a useful reminder that proteins are far from static. Cryptic pockets are surprisingly common, and even if they are too small and enclosed to support high affinity binding, they can be used as footholds to build larger molecules.

Twenty-First Annual Fragment-Based Drug Discovery Meeting

Last week some 875 people attended the CHI Drug Discovery Chemistry (DDC) meeting in San Diego. I can’t do justice to the 40 or so presentations I attended over four days but can highlight some of the main themes.

 

Reversible fragments

Membrane targets such as G protein-coupled receptors (GPCRs) pose a challenge for biophysical methods, but three talks presented progress. Matthew Eddy (University of Florida Gainesville) described high-resolution magic angle spinning (HRMAS) NMR, which entails spinning isolated cellular membranes containing GPCRs at high speed (4 kHz!), which miraculously yields sharp NMR signals for bound ligands. Matthew demonstrated applications with the human adenosine A_2A receptor and weak (mM) ligands. He noted that the technique can work with native, poorly expressed proteins, though data collection times can be upwards of 30 minutes.

 

Kris Borzilleri described using ¹⁹F NMR to find ligands against an orphan GPCR at Pfizer. The 2287 fragments screened yielded 87 hits, of which 38 confirmed by SPR. SAR studies eventually yielded low micromolar ligands, but these were difficult to advance in the absence of structure (see here for a more successful example from Merck).

 

Vanessa Porkolab (Eurofins Cerep) described using the Nanotemper Spectral Shift technology to screen 826 fragments against the adenosine A_2A receptor at 300 µM, with a 9.2% hit rate. Many of these ligands stabilized the GPCR in a thermal shift assay and seven were even active (as antagonists) in a cellular assay.

 

Turning to soluble proteins, Paola Di Lello presented a case study from Genentech and Vernalis applying ligand-observed NMR to the protein phosphatase PTPN22. Subsequent protein-observed NMR revealed that most of the 16 validated hits bound to two pockets some distance from the active site. The fragments were optimized to mid-micromolar affinity but showed no functional activity.

 

And Charlotte Hodson presented the eIF4E story from Astex. As we discussed last year, this yielded a low nanomolar ligand that did not have the desired cellular effects. Charlotte noted that subsequent genetic experiments were consistent with the limited efficacy. Still, the target was sufficiently interesting that a chemical probe would have been pursued even knowing it would be high-risk.

 

Covalent ligands

Covalent approaches made appearances throughout the conference. Keriann Backus (UCLA) described chemoproteomic approaches to find cysteine-targeting ligands; she noted that gain of cysteine residues (such as G12C in KRAS) are the most common missense variants in cancer. Keriann also warned how covalent compounds can cause potentially misleading effects in cells, as she described in Nat. Chem. Biol. last year.

 

In 2021 we wrote about the SpotXplorer fragment library from György Keserű (Hungarian Research Centre for Natural Sciences). György has now prepared a PhotoXplorer library, which uses diazirine tags for photochemical screening, which we described here. The new library has produced high hit rates across a variety of targets. György also described a new sulfozone-based photoprobe that is easier to prepare than diazirines.

 

Kelly Craft recounted a DNA-encoded library (DEL) screen at AbbVie against the target BCL2A1, also known as BFL1. This produced an aldehyde-containing low micromolar binder that formed an imine with buried lysine 102. Uncomfortable progressing an aldehyde, the researchers sought to covalently engage cysteine 55, the same cysteine targeted by AstraZeneca, as we wrote about here. The progression included at least one dual-warhead molecule which was crystallographically confirmed to bind both the lysine and cysteine. The effort ultimately yielded cysteine-selective leads.

 

Earlier this year I described the dDRTC method we developed at Frontier Medicines for determining k_inact/K_I, and Svetlana Kholodar presented a nice overview of its scope and utility. My colleague Johannes Hermann spoke in more detail about our covalent technologies, particularly those using AI.

 

Chemical space and the exploration thereof

Brian Shoichet (UCSF) gave an entertaining and wide-ranging account of “directed and random walks in chemical space.” Brian has consistently been on the bleeding edge of high-throughput in silico screens, from 67,000 compounds in 2009 to 138 million molecules in 2019 to 4 billion molecules today. When docking artifacts are avoided (as we discussed here), bigger libraries consistently produce more potent hits for more targets – an observation strikingly consistent with Alex Shaginian’s in 2023 as HitGen expanded their DEL libraries from billions to more than a trillion molecules. Brian is developing methods to computationally screen the >4 trillion make-on-demand molecules now available from companies such as Enamine.

 

Direct-to-biology (DTB) approaches, which rely on microscale chemical reactions screened without purification, have become increasingly popular methods for exploring chemical space. Jack Sadowsky correctly stated that Carmot was the first company formed around this approach; we previously wrote about the role Chemotype Evolution played in the discovery of sotorasib. Jack described how Kimia, which spun out of Carmot, has continued to advance the technology, applying it to find inhibitors selective for single members of closely related kinase families.

 

Allan Jordan described how Sygnature Discovery is applying DTB in a variety of assays including microsome stability and crystallography. (We wrote about crude reaction screening by crystallography earlier this year.) Expanding beyond DTB, Allan called their platform direct-to-discovery, and discussed how it led to a preclinical candidate with STORM Therapeutics in just 18 months.

 

WuXi Apptec is also using DTB. Peichuan Zhang described starting with ligands derived from fragment and DEL screens against the E3 ligase GID4 to make PROTACs to degrade BRD4; DTB was used to explore a wide range of different linkers. And Daniel Blair (St. Jude) described using DTB and affinity selection-mass spectrometry (AS-MS) to find new molecular glues for the oncology target LCK.

 

Computers, DEL, and DTB are not the only way to explore chemical space. Last year we covered Tom Kodadek’s bead-based screening approach at University of Florida Scripps, and Tom presented two talks on the topic, one using macrocycles to find binders to difficult targets such as PTP1B and one using small molecules to find molecular glues.

 

Speaking of PROTACs and glues, plenary keynote speaker Alessio Ciulli (University of Dundee) discussed the “evolution and future of targeted protein degradation.” Alessio noted that there are >25 PROTAC degraders and >10 glues in the clinic, though these collectively target only a small number of E3 ligands, so there is plenty of opportunity for the area to expand.

 

For many of us in industry, drugs represent the most privileged points in chemical space, and these often look quite different than we assume, as Dean Brown (Jnana) noted in his recent analysis of 104 oral small molecule drugs approved by the FDA from 2020 to 2024 (which we mentioned here). Some drugs contain eye-raising moieties such as acetylenes, styrenes, N-O bonds, and nitro groups. Indeed, it is worth remembering that venetoclax, arguably the most successful fragment-derived drug, sports a nitro group.

 

But before getting too complacent, Jonathan Baell (Manas) warned about frequent hitters in libraries of FDA-approved drugs. He notes in Eur. J. Med. Chem. earlier this year that many commercial libraries are actually enriched for molecules that cause spurious biological activity. Jonathan calls on library vendors to remove particularly egregious compounds, though I’d settle for world peace.

 

I’ll close on that pleasant thought, but please feel free to comment. I hope to see you in San Diego next year April 19-22 for the twenty-second iteration of DDC.

Fragments vs the E3 ligase KLHL12

Last week we highlighted work out of Steve Fesik’s lab at Vanderbilt University about PLPro. This week we’ll highlight another paper just published (open access) in J. Med. Chem. on a different subject from Steve, Alex Waterson, and colleagues.

 

Targeted protein degradation has been receiving increasing attention. The most common approach uses bivalent molecules called PROTACs. Imagine a molecular barbell, where the weight plate on each end is a different ligand, one targeting an E3 ligase and the other targeting a protein to degrade. As he discussed at the DDC meeting in 2023, Steve has long been pursuing ligands against previously unexplored E3 ligases with desirable properties, such as tissue-specific expression. A PROTAC using an E3 found predominantly in cancer cells could degrade essential proteins while sparing proteins in normal cells, and so yield safer drugs. The E3 ligase Kelch-like protein 12 (KLHL12) is overexpressed in many cancers but not expressed in heart tissue. The new paper describes fragment screening and optimization of ligands for this ligase.

 

The researchers started with a protein-detected ¹H-¹⁵N correlation NMR screen of 13,824 fragments in pools of 12, each at 0.8 mM. This yielded just 35 hits, of which 15 showed similar chemical shifts to those caused by a peptide substrate, suggesting that they bind in the same site. Dose-response experiments were used to determine affinities, with compound 1 being the best.

A crystal structure of this molecule bound to KLHL12 confirmed that it binds in the substrate-binding cleft, but the resolution was insufficient to determine the precise orientation. Nonetheless, SAR around the aniline moiety led to more potent molecules such as compound 7c, and exploration around the benzimidazole led to compound 7k, with sub-micromolar affinity as assessed both by a fluorescence polarization anisotropy (FPA) assay as well as SPR.

 

A crystal structure of 7k bound to KLHL12 was solved at high resolution, explaining the SAR and also revealing tempting space for further growing. Disappointingly though, most of the dozens of analogs had at best low micromolar affinity, and the few that had comparable activity to compound 7k had significantly worse ligand efficiencies. KLHL12 is homologous to the protein KEAP1, which as we noted last year has also proven challenging for conventionally drug-like ligands.

 

In addition to KEAP1, KLHL12 has more than 35% sequence identity to nine other proteins, so selectivity is a potential concern. Unfortunately, these proteins proved difficult to express. However, one compound tested was selective for KLHL12 over KEAP1.

 

Finally, a small set of compounds was tested for in-cell KLHL12 engagement using a nanoBRET assay. Happily, compound 7k proved active at sub-micromolar concentrations, suggesting that cell permeability would not be an issue.

 

This is a nice fragment to lead story. The relatively flat SAR for many of the compounds, while undoubtedly frustrating, should be useful for further understanding molecular recognition. It is not clear whether compound 7k is sufficiently potent to be useful, but as the researchers conclude, the work “provides a promising foundation for the future design and synthesis of KLHL12-based PROTACs.”

From noncovalent fragment to (non)covalent leads against PLPro

The most successful drug against COVID-19, nirmatrelvir, targets the main protease of SARS-CoV-2. As we discussed just last year, this protein has received considerable attention. But the genome for SARS-CoV-2 also encodes a second cysteine protease, papain-like protease, or PLPro. Despite this enzyme being essential for viral replication, the only previously disclosed chemical series targeting it dates from 2008 efforts against the original SARS. Two new papers in J. Med. Chem. from Stephen Fesik and colleagues at Vanderbilt University introduce new molecules, covalent and non-covalent.

 

One reason progress has been slow against PLPro is that it is inherently challenging. It recognizes the sequence LXGG, where X = Arg, Lys, or Asn. The two glycine residues thread a narrow channel to access the catalytic cysteine, while leucine and the “X” residue bind in solvent-exposed subsites. In 2024 the Fesik group published an open-access paper in ACS Med. Chem. Lett. describing the results of a protein-observed NMR fragment screen. Out of 13,824 fragments screened in pools of 12 at 0.8 mM each, 77 confirmed when tested individually, and 22 had affinities better than 1 mM.

 

An attractive feature of protein-observed NMR is that it tells you where the fragments bind, and in this case there were two main binding sites. One set of fragments bound in the S3 and S4 pockets, where the leucine and lysine residues of the substrate would normally fit, while another group of fragments bound some distance from the active site. Representatives from both groups were tested for inhibition of enzymatic activity. Only those in the first group were active, so these were prioritized.

 

In the first open-access J. Med. Chem. paper this year, the researchers started with compound 11, which fulfills rule-of-three fragment criteria, binds in the S4 pocket, and inhibits the enzyme with high-micromolar activity. Adding a couple methyl groups (compound 15) improved the potency by roughly 10-fold, and building towards the S3 pocket yielded compound 37, with low micromolar activity. Addition of a basic nitrogen, as in compound 46, improved the potency to submicromolar activity, and crystallography revealed that the basic nitrogen interacts with a glutamic acid side chain. This molecule was active in a cellular assay at submicromolar concentration.

 

An increasingly popular strategy for addressing difficult targets is through covalent inhibitors, and this is the subject of the second open-access J. Med. Chem. paper this year. The researchers synthesized and tested 25 analogs based on molecules such as compound 37 in which various warheads were appended by flexible linkers. Although some of these were active at low micromolar concentrations, only a few showed time-dependent activity as would be expected for an irreversible covalent inhibitor.

 

These few were optimized with the aid of molecular dynamics and structure-based design to molecules such as compound 45. Interestingly, despite being nearly 10-fold more potent than the best non-covalent molecule, it was less active in cells; the researchers attribute lower-than-hoped activity to the two hydrogen-bond donors in the diacetylhydrzaine linker. Unfortunately, these turned out to be essential for covalent binding; crystallography revealed that the compound forms four hydrogen bonds in the glycine channel. Indeed, this particular linker-warhead combination had been previously reported, and the inability to improve on it emphasizes the restrictive requirements for this particular protein.

 

This is a nice series of papers that shows how a single fragment can lead to multiple leads. The last paper is also a useful reminder that adding a warhead to a high-affinity binder is not always easy, nor does it necessarily lead to superior molecules. Indeed, neither the covalent nor the noncovalent leads have any reported in vitro ADME or pharmacokinetic data. It would be fun to screen PLPro against a library of covalent fragments to look for even more starting points.

NICELE: everyone gets an A+!

Since the invention of ligand efficiency (LE) more than two decades ago, scholars have been debating its strengths and weaknesses. And alternatives; we've written about LLE, LLE_AT, LELP, %LE, WTF, and more. Just last week we discussed CRE.

 

But all these metrics (with perhaps one exception) have a problem: they sometimes tell you that your molecule is not as good as you hoped. And who wants to hear that? 

 

Maybe you used cutting-edge AI technology to design your ligand, and then moved heaven and earth to make it. When you finally test it the result comes back - meh. But that doesn't mean the haters should win! 

 

You can't (or really shouldn’t) change the assay results, but you can change the scoring system, and now researchers at the University of Durak have come up with a new metric called NICELE (No Insulting Critical Evaluation Ligand Efficiency). Unlike xLE, the calculation is simple:

NICELE = LE + F, where F = (1.0 - LE)

Let's say your fragment comes in with a LE of 0.18 kcal/mol per non-hydrogen atom - not so good. That's OK, the NICELE is a much more impressive 1.0!

 

As a bonus, because LE is removed from the final calculation, NICELE is independent of standard state assumptions, so Dr. Saysno should embrace it.

 

Of course, NICELE makes it harder for those molecules with truly impressive ligand efficiencies to stand out, but they're a bunch of elitists anyway.  

Ligand reactivity efficiency (LRE)

As covalent drug discovery continues to rise, the demand for metrics to help guide lead optimization is increasing. Last year we discussed covalent ligand efficiency (CLE). In an open-access paper just published in J. Med. Chem., Benjamin Horning, Brian Cook, and colleagues at Vividion Therapeutics describe ligand reactivity efficiency (LRE). (Benjamin presented LRE at the DDC meeting in 2024.)

 

A key challenge when developing covalent ligands is maximizing specific reactivity towards the target of interest while minimizing intrinsic reactivity towards other proteins; the two types of reactivity are not the same, as we wrote about last year. For molecules that target cysteine residues, intrinsic reactivity is usually determined by assessing reactivity against the small molecule glutathione, which is abundant in cells.

 

For lead optimization more generally, a common metric is lipophilic efficiency (LLE or LipE, see here and here), in which the logP of a molecule is subtracted from the negative log of the IC₅₀ (pIC₅₀). More lipophilic molecules have higher logP values, so maximizing LLE helps to minimize increases in lipophilicity.

 

By analogy, the researchers defined LRE to help minimize increases in intrinsic reactivity. However, distinguishing specific from intrinsic activity is not necessarily straightforward. As we previously discussed, IC₅₀ alone is an inappropriate measurement for covalent inhibitors; the incubation time before the IC₅₀ is measured is an essential variable. The most rigorous value is k_inact/K_I, and although this ratio has been historically time-consuming to determine, we described an easier method earlier this year. Yet an even simpler measurement is the TE_{50(target, 1h)}, the concentration of compound necessary to label 50% of a target after one hour, which is a function of k_inact/K_I. The researchers thus defined LRE as:

 

    LRE = pTE_{50(target, 1h)} – pTE_{50(GSH, 1h)}

 

The variable in the second term, pTE_{50(GSH, 1h)}, is calculated from the reaction rate of the ligand with glutathione; intrinsically reactive ligands have higher rates.

 

In the case of LLE, values above 5 or 6 are generally considered acceptable for advanced leads, and the same is true for LRE. For example, a molecule with TE_{50(target, 1h)} = 10 nM and a (low) GSH reactivity of 0.01 M^-1s^-1 would have an LRE = 6.3. Also analogous to LLE, one can generate plots with pTE_{50(GSH, 1h)} on the x-axis and pTE_{50(target, 1h)} on the y-axis to assess whether LRE values are improving during a lead optimization campaign.

 

In my view, LRE is superior to previously discussed CLE because it explicitly considers the time component. A one hour incubation is practical; a ligand with k_inact/K_I = 10,000 M^-1s^-1 would have TE_{50(target, 1h)} = 19 nM. Also, LRE is more intuitive for medicinal chemists than CLE due to its similarity to LLE.

 

On the minus side, the researchers note that some of the assumptions break down for ligands with high non-covalent affinity (low K_I). Also, some folks may take issue with metrics that take the logarithm of a measurement that has units.

 

The researchers note another alternative metric, the reactivity enhancement factor (REF), which I briefly discussed here. REF is simply the ratio of the specific reactivity to the intrinsic reactivity, which is conceptually simpler to me than LRE. Nonetheless, the researchers state that LRE is commonly used at Vividion, which has put several covalent drugs into the clinic, so clearly it can be useful. Whether REF, LRE, or CLE, ultimately the choice of metric is less important than the ultimate goal: maximizing specific reactivity while minimizing intrinsic reactivity.

Malicious metals muddy fragment-to-lead optimization

Despite the effectiveness of vaccines against SARS-CoV-2, COVID-19 continues to plague us. The handful of approved small molecule drugs target only two proteins and have much room for improvement. One interesting but underexplored target is the nonstructural protein 14 (NSP14), a 3’ to 5’ RNA exonuclease, which is important both for viral replication as well as immune escape. In a new open-access ACS Chem. Biol. paper, Jae Jung, Shaun Stauffer, and colleagues at the Cleveland Clinic describe how their efforts against NSP14 were thwarted.

 

The researchers started with the crystal structures of two fragments that had been identified in a high-throughput crystallographic screen at XChem. They reproduced these in-house, confirming the published structures, and also made and characterized a few analogs. Crystallography demonstrated these bind in a similar manner. Encouragingly, they also showed activity in a biochemical assay.

 

The two published fragments bind next to one another, presenting a good opportunity for fragment merging or linking. The researchers used the computational tool Fragmentstein, which we wrote about here, to design new molecules. Some of these molecules were active in the biochemical assay, and a crystal structure of a merged compound revealed that it bound as expected. Importantly, none of the molecules inhibited an unrelated endonuclease.

 

So far, so good, but the researchers were suspicious about the SAR. For example, changing an isopropyl to a cyclopropyl group weakened the activity  from 2.4 to 150 µM, despite the fact that the moiety is largely solvent exposed. After resynthesizing and more carefully purifying the molecules, the researchers found them to be completely inactive in the biochemical assay.

 

NSP14 contains two catalytic magnesium ions and three structural zinc ions, and the researchers considered the possibility that metal contaminants might have been responsible for the activity. Sure enough, when they screened the Metal Ion Interferences Set (MIIS), which we wrote about here, they found that half a dozen metal ions potently inhibited the assay. They tested whether any of the spuriously active compounds contained palladium and ruled this out, but did not test for other metals. Indeed, metals may not even be to blame: the active molecules all contain thiazoles, and as we discussed in 2022 these can sometimes interfere with assays. What is clear is that the exciting initial activity results were artifacts, and the researchers were sufficiently diligent to figure it out for themselves.

 

One of the most disturbing findings is that the crystal structures looked fine, despite the compounds having no measurable activity. As we’ve written previously, the lack of affinity information is the biggest drawback of fragment screening by crystallography. Perhaps NMR would have been able to invalidate the false-positives, though as we have written both protein-detected and ligand-detected methods can be fooled. As our 2024 poll emphasized, using multiple methods to validate fragment binding is important. And resynthesizing and carefully purifying compounds helps too.

 

These sorts of cautionary tales are not published as often as they should be. Kudos to this team for both warranted skepticism and providing a warning for others.

Selectivity in cells may vary

Last year we celebrated the ten-year anniversary of the Chemical Probes Portal. One of the key requirements for a chemical probe is selectivity, which was set to >30-fold vs related targets when the Portal launched in 2015. For enzymes such as kinases, selectivity is often measured in cell-free assays. A new open-access J. Med. Chem. paper by Matthew Robers, Alison Axtman, and collaborators at Promega and University of North Carolina at Chapel Hill suggests that such data don’t necessarily translate to cellular assays.

 

Kinases are one of the most heavily mined classes of targets this century; five of the eight FBLD-derived approved drugs target kinases. With more than 500 in the human proteome, selectivity has long been a focus. One common method for assessing selectivity in cell-free assays is the Eurofins DiscoverX panel, which currently includes more than 450 kinases. Each kinase has a DNA tag and is paired with a promiscuous high affinity binder attached to a solid support. Test compounds are added, and qPCR is used to assess and quantify which kinases are displaced. The competition assay allows determination of dissociation constants.

 

To measure the binding of compounds to kinases in living cells, the researchers turned to the NanoLuc-bioluminescence resonance energy transfer (NanoBRET) assay. This is also a displacement assay that relies on a bivalent molecule containing a kinase ligand and a fluorophore. Kinases are tagged with NanoLuc, which causes luminescence of the fluorophore when it is in close proximity (ie, bound to the kinase). Ligands that bind to the kinase displace the bivalent molecule, decreasing luminescence.

 

The researchers started with four promiscuous kinase inhibitors, two of which (dasatinib and sorafenib) are approved drugs. They ran these against 240 or 300 kinases in the NanoBRET assay and compared the values with published DiscoverX dissociation constants. Most of the compounds were more potent in the DiscoverX assay than in the cell-based assay, and the researchers suggest several possible reasons. First, the DiscoverX assay is run in the absence of the cofactor ATP, which can compete with ligands that bind to the active site. Second, cell (im)permeability could decrease cellular potency. Finally, most of the DiscoverX kinases are truncated, whereas the NanoBRET kinases are full length.

 

For these and other reasons, it is common for compounds to be less active in cell assays than biochemical or biophysical assays. Surprisingly though, for a few kinases the compounds were actually more potent in the cellular assay than they were in the DiscoverX assay.

 

To extend these findings, the researchers tested additional kinase inhibitors and found that three kinases were particularly susceptible to inhibition in cells. One of these kinases, PIP4K2C, was engaged at mid nanomolar potency by cabozantinib, and the researchers suggest this could be useful for immuno-oncology. More worrisome, several approved drugs bind to the tumor suppressor STK11 in cells, raising the potential that these compounds could inhibit exactly the wrong pathway.

 

On the bright side, the researchers find that some molecules that look moderately selective in the DiscoverX assay are actually quite selective in cell assays, and they propose new chemical probes for the little-studied kinases BRSK1/2 as well the kinases DDR1/2.

 

Kinases are certainly not the only class of targets for which compounds’ performance differs outside vs inside cells; we wrote about covalent WRN inhibitors here. This paper is a good reminder that as useful as cell-free assays are, things can go weird once you go into cells – for better or for worse.

Best practices for applying HDX-MS to FBLD

Among the many biophysical techniques presented at the recent Novalix meeting, hydrogen/deuterium exchange mass spectrometry (HDX-MS) was mentioned only a few times. Practical Fragments last covered it nearly four years ago in the context of a paper that described theoretical challenges and improvements to the method for assessing fragment binding modes. A new open-access Comm. Chem. paper from Tiago Bandeiras, Alessio Bortoluzzi, and collaborators at iBET-Instituto de Biologia Experimental e Tecnológica and Merck KGaA implements some of these suggestions.

 

The researchers find only two examples where HDX-MS had been applied to fragments, one of which we covered here. The new paper focuses mostly on Cyclophilin D (CypD), a mitochondrial protein implicated in several diseases. A screen we wrote about in 2020 described several fragment hits, some of which were crystallographically found to bind in three overlapping sites designated the proline pocket, the aniline pocket, and the pyrazolo pocket.  The researchers chose a binder from each pocket to study as well as two more fragments whose binding sites were not known. All five fragments are extremely weak hits, with at best 7 mM (yes, millimolar) affinity as assessed by SPR, making this a particularly challenging test case.

 

As a reminder, HDX-MS examines the exchange of deuterium from D₂O to protein backbone amides. Nearby ligands can slow this exchange, and mapping the locations and magnitude of these changes reveals where the ligand binds. Optimization experiments were initially run on a compound with a K_D of 44 mM for the aniline pocket. Three protein concentrations were tested. Since the highest (10 µM) yielded the highest number of detected peptides, it was used for subsequent experiments.

 

As suggested in the 2022 paper, compound was added both to the initial protein solution as well as to the deuterium exchange buffer. The fragment was tested at 2.5, 5, and 10 mM, and changes in deuterium uptake were found in all cases, which is remarkable given that the theoretical occupancies range from 5 to 18%. An experiment using substoichiometric concentrations of a high affinity ligand confirmed that 18% protein occupancy is sufficient to generate a reliable signal.

 

Still, given the low occupancy, the researchers used statistical methods to ensure that changes in signals were significant. Mapping those that were onto the structure of the protein confirmed that the fragment bound to the aniline pocket.

 

A second fragment was tested by HDX-MS, and the results confirmed that it binds in the pyrazolo pocket, as previously shown by crystallography. However, for a third fragment that had been shown to bind in the proline pocket, the results suggested instead that it binds in the aniline pocket. The proline pocket exhibited very low levels of deuterium exchange, but when the researchers increased the pH from 7.4 to 9 they did find evidence that the fragment binds here as well.

 

Next, the researchers turned to the two fragments with unknown binding sites. HDX-MS revealed that these bind to the aniline binding site, though with subtly different protection patterns suggesting that they bind in slightly different regions of the pocket.

 

Finally, the researchers used their optimized HDX-MS conditions on a previously identified fragment that binds the kinase FAK with low millimolar affinity. This showed that it binds in the so-called hinge region, in agreement with crystallography. This fragment was also used as a negative control for CypD, showing that it does not bind.

 

This paper is a nice resource for those hoping to apply HDX-MS to fragments. The fact that the binding sites of such weak binders can be determined is quite remarkable. That said, the resolution is not as good as crystallography or protein-detected NMR; for FAK in particular, the ligand reduces deuterium update across a large fraction of the protein surface. And the fact that a proline-pocket binder was initially mapped to the aniline pocket also gives one pause. Perhaps the binding mode in solution really is different from that found crystallographically. It would be interesting to see whether NMR can resolve the conundrum.

Twelfth Novalix Biophysics in Drug Discovery Conference

Last week the Twelfth Novalix Biophysics in Drug Discovery Conference was held for the first time in La Jolla, California. It’s been several years since I wrote about one of these, and I was happy to see that they’ve maintained their reputation for excellent science and convivial conversation. There’s no way to cover the two-dozen talks, but here are a few highlights.

 

One of the things I most enjoy about these meetings is learning about new and emerging methods, and these were well represented. Chris Brosey (AbbVie) discussed time-resolved high-throughput small-angle X-ray scattering (TR-HT-SAXS). As I discussed a couple years ago, the approach can be used to measure the kinetics of protein dimerization in response to fragment-sized ligands.

 

SAXS-based approaches typically require access to a synchrotron, but Takashi Sato (Rigaku) described a related approach, electron density tomography (EDT), using an in-house instrument. Using machine learning, EDT can provide more detailed structural information than standard SAXS, and Takashi provided examples for samples ranging in size from viruses to single-chain variable fragments (scFvs) smaller than 30 kD.

 

Another approach to examine protein complexes is microfluidic diffusional sizing (MDS), described by James Wilkinson of Fluidic Sciences. By assessing the amount of diffusion in disposable chip-based chambers, MDS can determine how the hydrodynamic radius changes in response to ligands. Each chip holds 24 samples, and data can be collected in less than an hour. The minimum observable size change is 5-10% so measuring small molecules directly is unlikely. Still, the technique is useful for observing induced proximity events such as those caused by molecular glues, and it is sufficiently robust that it can be run in pure serum.

 

Among solution-based methods, none have achieved such recent prominence as cryo-EM. Weiru Wang, my colleague at Frontier Medicines, described how this technique was used iteratively to design and characterize bivalent degrader molecules that covalently exploit the E3 ligase DCAF2. (We recently published this work in Structure.)

 

Cryo-EM has revolutionized the types of biological molecules that can be structurally characterized. According to Denis Zeyer (Novalix), the technique accounted for 40% of PDB entries last year. However, despite the “resolution revolution,” most structures are not as detailed as those from X-ray crystallography; Denis noted that only 20% of the new structures were solved to a resolution better than 3 Å, which may have negative implications for machine-learning methods trained on these lower resolution structures.

 

If cryo-EM is the new kid in town, NMR is the grizzled veteran. But proving that it is possible to find new applications for old methods, Matthew Eddy (University of Florida) described a clever ¹⁹F-labeling approach for GPCRs in nanodiscs to quantify the distribution of various states in response to anionic lipids and ligands. This has allowed him to distinguish between antagonists and inverse agonists, which can be difficult using cell-based assays.

 

Turning from solution-based to surface-based methods, SPR has moved into the number two slot for fragment-finding, as we noted in our recent poll. Just as there are new tricks for NMR, the same applies to SPR. Matthew Peterfreund (Bruker Biosensors) described switchSENSE, a fluorescence proximity assay built on an SPR chip that is useful for measuring the binding and kinetics of bifunctional ligands such as PROTACs to two or more proteins. He also introduced the Triceratops SPR#64 instrument, which as its name implies supports 64 sensor spots.

 

Kris Borzilleri (Pfizer) discussed SPR-microscopy (SPRm), which combines an optical microscope with an SPR instrument. This can be used to measure the affinities of ligands binding to receptors in cells grown on SPR chips, and Kris described applications to membrane proteins such as GPCRs and solute carriers. The technique is still quite slow though, at only 10-15 compounds in duplicate per week.

 

Another surface-based approach to screening cells was described by Volker Gatterdam of Lino Biotech. Focal molography relies on changes in diffraction from nanoengineered diffraction gratings, called molograms. Targets, which can include living cells, are immobilized to the molograms, and analyte is flowed over. The instrument contains 64 spots, and assays can be run in complex samples such as tissue lysates.

 

Covalent drug discovery also made an appearance, with talks by Landon Whitby (Lundbeck) and Ben Cravatt (Scripps). Landon provided an overview of chemoproteomics techniques to screen ligands in cells, such as those we wrote about in 2016. Ben continued the theme, including several success stories, and also discussed challenges for finding cryptic ligandable pockets. Despite impressive progress with machine learning, Ben noted that these methods often find only common solutions, while empirical chemoproteomics methods can find rare types of pockets.

 

Of course, as we’ve repeatedly emphasized, biophysics methods are best used in combination, as noted by Daniel Harki (University of Minnesota) and Ann Boriack-Sjodin (Takeda). Daniel presented a screen of 1056 fragments against the cancer target APOBEC3 using NMR and SPR. This yielded just a single validated hit, which interestingly turned out to be the same fragment found against KRAS in a paper we discussed in 2022. And Ann described how biophysics led to multiple clinical compounds against a variety of targets at Epizyme and Accent Therapeutics.

 

I’ll stop here, but please feel free to add your thoughts. And while the date for the next Novalix conference has not yet been scheduled, the location has, with a return to beautiful Strasbourg. Vive la biophysique!

3D fragments vs the histamine H1 receptor

Last month we highlighted a paper from Iwan de Esch and colleagues at Vrije Universiteit Amsterdam about assessing molecular shapeliness. In a new open-access RSC Med. Chem. paper, de Esch and colleagues use a small, shapely library to find and develop potent antagonists of the histamine H₁ receptor (H₁R).

 

The G protein-coupled receptor H₁R is one of four histamine receptors and the target for dozens of antihistamines used for allergic and inflammatory conditions. Thus, it’s well understood and a nice model system.

 

In 2013 we discussed how the de Esch group screened a library of 1010 fragments against several targets, including H₁R. In the new paper, the researchers built a smaller library of just 80 compounds designed to be more shapely, as assessed by Fsp³, plane of best fit (PBF), and principal moment of inertia (PMI). A screen against H₁R using a radioligand displacement assay at 10 and 30 µM yielded a single hit, compound 1a.

 

Compound 1a deviates slightly from the rule of three due to some unusual elements: an azide as a synthetic handle and a butyl moiety to make the compound less explosive. Trimming these features led to compound 3a, a rule-of-three compliant fragment that – while having lower affinity – mostly maintained ligand efficiency and improved lipophilic ligand efficiency (LLE). Fragment growing led to compounds 13a and 13k, and merging with previously reported molecules led to compound VUF26691, with low nanomolar affinity and picomolar antagonistic activity in a cell assay. Throughout the process the cis isomers generally had equal or better potency than the trans isomers, an empirical observation difficult to explain by modeling.

 

This is a nice fragment-to-lead story, and the researchers note that fewer than 40 compounds were synthesized in the journey from compound 3a to VUF26691. Interestingly though, compound 1a was the only hit from the 80 compounds tested, while the hit rate from the larger, flatter library screened in 2013 was nearly three-fold higher, at 3.6%. Although it’s difficult to extrapolate from n=1, the results are consistent with our post last year that more shapely libraries will likely have lower hit rates.

 

Still, this shapely hit makes for a neat scaffold, and if nothing else perhaps it will be easier to establish intellectual property.