Covalent allosteric inhibitors of NLRP3

NLRP3 has received considerable attention as an inflammatory disease target. The protein normally exists in an auto-inhibited conformation, but in response to various stimuli it changes shape to help form a multimeric complex called the inflammasome. This in turn activates various inflammatory pathways, including cell death. Several drugs targeting NLRP3 have gone into the clinic, but none have been approved. In a new open-access Br. J. Pharmacol. paper, Brian Cook, Gabriel Simon, and colleagues at Vividion describe a covalent inhibitor that binds to a previously unknown site.

 

As we wrote nearly a decade ago, Vividion pioneered the use of chemoproteomics to identify covalent binders in cells and cell lysates. While exploring the mechanism of the previously reported NLRP3 inhibitor MCC950, they found that cysteine 280 became resistant to modification by electrophiles in the presence of the ligand. MCC950 does not contain an electrophile, arguing against direct interaction with Cys280 and suggesting instead that binding causes a conformational change to the protein which makes Cys280 less accessible. In other words, protein inhibition correlated with Cys280 being less exposed.

 

A screen of Vividion’s library yielded no hits against Cys280 but more than 300 against cysteine 463. Interestingly, modification of C463 also caused a decrease in reactivity of Cys280, suggesting a conformational change similar to the one caused by MCC950. Thus, the researchers pursued Cys463 binders.

 

Two parameters were used to guide compound optimization, target engagement and promiscuity. The first number, TE₅₀, measures the extent of covalent engagement of a given cysteine after 1 hour in cell lysates. The second, P₅₀₀, measures the percentage of all measured cysteines in the sample labeled by at least 40% after incubation with 500 µM compound for 1 hour. One of the initial hits, VVD-124476, gave a low micromolar value for TE₅₀ but labeled some 20% of the measured proteome. Indeed, it was previously reported as a covalent ligand for a completely different protein. To be useful as a NLRP3 inhibitor, the researchers needed to reduce promiscuity, and the most straightforward approach is to reduce general reactivity.

 

To decrease reactivity, the researchers swapped the acrylamide warhead with a butynamide (VVD-142397). Butynamides are reported to be less reactive than acrylamides, a result corroborated at Vividion with chemoproteomic data on 400 matched molecular pairs. The acrylamide to butynamide swap improved selectivity some 20-fold, though it also decreased potency against NLRP3. Increasing the core ring size regained activity, and rigidifying the molecule led to further improvements. Further optimization ultimately led to VVD-338213 and several related molecules.

 

Although the researchers do not report k_inact/K_I values, a back of the envelope calculation puts it around 1200 M^-1s^-1 for VVD-338213, a potency where cell activity might be expected. Happily this turned out to be the case: the molecule blocked IL-1β secretion from human cells at lower concentrations than MCC950. Importantly, in cells where Cys463 had been mutated to alanine, MCC950 was still active while the covalent molecules were inactive.

 

In addition to promising cell activity, many of the covalent molecules had reasonable stability in whole blood, good permeability, and low efflux. (I do wish the ligand efficiency reactivities of the molecules were reported; after all Vividion, introduced this metric.) Some of the molecules also showed good brain penetration. In vivo pharmacodynamic studies are complicated by the fact that Cys463 is not conserved in mice, but fortunately the researchers could run studies on transgenic mice in which human NLRP3 had been introduced. In these animals, IL-1β was decreased by both VVD-338213 and by MCC950.

 

The researchers were also able to obtain a cryo-EM structure of one of their molecules bound to NLRP3, which confirmed covalent binding to Cys463. More importantly, it revealed that the binding site is in a cryptic pocket some 20 Å from where MCC950 binds. (One wonders if the computational technique we discussed last week would have been able to predict this pocket.) Similarly to the WRN inhibitor Vividion has taken into the clinic, the molecule appears to make only hydrophobic contacts with the protein, with no polar interactions. Mechanistically, the “Cys463 ligands act as molecular doorstops, stabilizing the inactive conformation and preventing the structural rearrangements necessary for transition to the active inflammasome disc.”

 

This is a lovely paper and a case study in transforming a highly reactive molecule into a selective molecule with in vivo activity. It is interesting that the starting molecule, with 28 non-hydrogen atoms, flagrantly violates the rule of three. Last year we asked whether covalent fragments need to be larger than their non-covalent counterparts, and this paper provides another example where the answer is yes. Whether or not this chemical series ultimately advances, it is nice to have another chemical probe for this target.

Computational prediction of cryptic pockets

Last week we highlighted how NMR-based fragment screening was able to identify cryptic pockets on a small protein. Experimental methods are (still) the gold standard, but they take considerable effort and resources. In a new open-access J. Am. Chem. Soc. paper, Neha Vithani, David LeBard, and collaborators at OpenEye, Bayer, Genentech, and Khartis provide a computational approach.

 

The researchers defined six categories of cryptic pockets: 1) side-chain motion, 2) loop motion, 3) secondary-structure motion, 4) secondary-structure change, 5) interdomain motion, and 6) pre-existing deep pockets with no access to the surface of the protein. They collected a set of 21 proteins with 26 cryptic pockets overall (some proteins contain more than one) whose apo (unbound) and holo (ligand-bound) structures were available in the Protein Data Bank. This “Encore Dataset” contains all six types of cryptic pockets, though some are represented by only two or three examples.

 

To computationally identify cryptic pockets, the researchers used weighted ensemble molecular dynamics (WEMD) simulations on the apo structures. The details are quite technical but involve focusing on “normal modes” of protein movement. To find pockets, xenon is included in the simulation; these large atoms will fill pockets that open up during the course of the simulation, so detecting bound xenon reveals pockets.

 

WEMD successfully identified 24 of the 26 cryptic pockets in the Encore Dataset. Interestingly, both failures were for the same protein, hepatitis C polymerase, one a preexisting deep pocket and the other a secondary-structure change from order to disorder. (This latter category is exemplified in one of the cryptic pockets we discussed last week, in which crystallography revealed that a helix became disordered upon ligand binding.) The researchers hypothesize that these types of order-to-disorder transitions may be particularly difficult to detect computationally because the models are designed to identify secondary structures, thus overstabilizing them.

 

So WEMD is able to correctly predict most cryptic pockets in the Encore Dataset, but does it also predict cryptic pockets that don’t exist? Paul Samulsen famously quipped that economists had “predicted nine out of the last five recessions,” which actually looks pretty good compared to WEMD, which predicted more than 30 potential cryptic pockets on the protein biotin carboxylase. For comparison, the software program fpocket, which we wrote about last year, predicts 13 for this protein.

 

To rank which pockets actually exist, the researchers turned to another computational technique called Target X, which was described in this Research Square article earlier this year. This proved reasonably successful at ranking experimentally observed pockets near the top, with the top two predicted biotin carboxylase pockets corresponding to known pockets.

 

This is an interesting paper, but I am left with more questions. First, while the Encore Dataset is nice, why didn’t the researchers (also) use CryptoSite, a previously published set of 93 proteins with cryptic sites, which we discussed in 2024? More importantly, how can drug hunters decide whether to target a specific cryptic pocket? For instance, does WEMD and Target X allow one to assess whether cryptic pockets can support high-affinity ligands? These would be interesting questions for a follow-up paper.

Fragments find cryptic sites on ARID1B

We’ve written previously about the concept of synthetic lethality, in which changes to one protein make cancer cells more dependent on another. Inhibiting this second protein allows selective targeting of cancer cells while sparing normal cells. One such opportunity is the pair of proteins ARID1A and ARID1B, which assemble into a large complex that helps remodel chromatin to regulate transcription. ARID1A is mutated in some 9% of cancers, and ARID1B can partially compensate. CRISPR knockdown studies show that these cancer cells are dependent on ARID1B. ARID1B is huge (2236 amino acids!) and predicted to be mostly disordered – not the easiest target for small molecule drugs. However, a small 109-residue AT-Rich Interactive Domain (ARID), which binds to DNA, had previously been crystallized. This domain is the focus of a new Proc. Nat. Acad. Sci. USA paper by Michael Holliday and colleagues at Relay Therapeutics.

 

Computational assessment using Schrödinger’s SiteMap did not show any ligandable pockets, but molecular dynamics revealed that the N-terminal helix might open to reveal a shallow hydrophobic groove beneath it. Thus encouraged, the researchers performed a 2D NMR (¹⁵N-SOFAST-HMQC) screen of 480 fragments and obtained two hits, both with weak but measurable affinities. Interestingly though, the NMR signals are consistent with slow kinetics, suggesting that the molecules bind to rare conformations of the protein. Chemical shift perturbation (CSP) mapping showed that the two fragments bind near one another but not exactly at the same site.

 

Crystallography revealed that compound A-1 bound to a previously cryptic tunnel of ARID that forms by the displacement of a tryptophan side chain – a motion not seen in the molecular dynamics simulation. Preliminary medicinal chemistry improved the potency to mid-micromolar affinity.

 

Compound B-1 proved recalcitrant to crystallography, but cocrystallization with the more potent compound B-2 yielded a high resolution structure. This molecule did in fact bind where the N-terminal helix normally binds, as predicted by molecular dynamics; the helix itself became disordered.

 

But wait, there’s more! To find additional chemical matter, the researchers conducted a virtual screen of 150,000 compounds against ARID. Of the several dozen molecules tested by 2D-NMR, two showed CSPs similar to those caused by compound A-1, and their binding modes were confirmed by crystallography. But one of the molecules that showed CSPs similar to those caused by compound B-1 also caused CSPs in a different region of the protein, and crystallography confirmed binding to yet a third site.

 

In order for the compounds to be selective for cancer cells they need to be selective for ARID1B over ARID1A, which is 83% identical in the ARID domain. NMR experiments revealed that the molecule that binds to the third site binds with similar affinity to ARID1A, while compound A-2 binds about 5-fold less tightly. Happily, compound B-2 showed no binding to ARID1A even at 10 mM. Thus, the researchers have found three starting points for optimization, and evidence that selectivity is possible.

 

As the researchers acknowledge, the compounds will require “substantial additional optimization,” not least for affinity. Intriguingly though, compounds A-2 and B-2 do bind sufficiently close to one another that a fragment linking or merging approach may be warranted.

 

So in the end, ARID1B ARID presents two cryptic sites and a third more conventional (albeit challenging) site. The presence of multiple small-molecule binding sites is consistent with this paper from 2016, but still surprising in such a small protein. As we noted then, just because a site binds fragments does not mean it will support high-affinity ligands. Will ARID1B turn out to be like KRAS, which ended up being druggable, or like IL-1β, which has resisted high-affinity small molecules? The current paper provides good starting points toward answering this question.

Fragments vs DHX8: towards a chemical probe

RNA helicases present an intriguing but challenging opportunity for drug discovery. Their importance for cell growth makes them attractive cancer targets. But to function, they must undergo considerable conformational changes, and this dynamism makes it difficult to find high-affinity inhibitors. In a new open-access J. Med. Chem. paper, Gurdip Bhalay, Rob van Montfort, and collaborators at The Institute of Cancer Research (ICR), Merck KGaA, and Proteros describe their efforts against DHX8.

 

DHX8 is one of 14 or so DEAH-box helicases and helps release mRNA from spliceosomes. Previous research had found it to be important for the cellular stress response as well as malignant cell transformation and progression, and siRNA-knockdown experiments showed that cancer cells were particularly dependent on DHX8 activity. Thus, the researchers set out to find a chemical probe.

 

The 2000-fragment ICR library was screened (at 300 µM) against the helicase domain of DHX8 using both surface plasmon resonance (SPR) and thermal shift assay (TSA). In addition, the full-length yeast homolog of DHX8, Prp22, was also screened by SPR to look for fragments that might bind outside the helicase domain. The TSA screen yielded 18 hits, while the SPR screen yielded 91, of which 63 also bound Prp22, while another 17 fragments bound Prp22 more strongly than the helicase domain of DHX8.

 

Eight-point dose response curves were collected for the hits, and 10/18 TSA hits gave a concentration-dependent response on both DHX8 and Prp22, along with 42/108 SPR hits, while another 5 SPR hits only bound to Prp22. Surprisingly, there were only 2 hits in common between the TSA and SPR hits; the researchers note the importance of using multiple orthogonal screens.

 

Next, the confirmed hits were tested in three ligand-observed NMR assays: CPMG, WaterLOGSY, and STD. Reassuringly, 39 were active in all three assays. Competition experiments suggested that the fragments did not bind in the ATP-binding site, but some of them were competitive with RNA.

 

Crystallography was attempted on several of the fragments, and compound 1 yielded a structure revealing that it did in fact bind in the “RNA-binding tunnel” of the helicase domain, with the carboxylic acid making contacts similar to a backbone phosphate. Although the dissociation constant (assessed by SPR) was > 1 mM, compound 1 was active in an ATP-hydrolysis assay and an RNA fluorescence anisotropy (FA) binding assay with triple-digit micromolar activity (values for the FA assays are shown here).

 

Much of the paper is devoted to the optimization of this fragment, which, true to the reputation of the helicase family, turned out to be difficult. Testing related compounds led to compound 6, with low micromolar activity, but despite extensive efforts it was impossible to improve the affinity much further. Thus, the researchers reexamined their larger compound collection for other similar compounds and identified compound 24. Although weaker than compound 6, a crystal structure revealed that the phenyl ring binds deeper into the RNA-binding tunnel, providing new opportunities for fragment growing. Structure-based design led to compound 34, with low micromolar activity, and further optimization ultimately led to compound 53, with submicromolar activity.

 

The mechanism of DHX8 requires the RNA-binding tunnel to open and close right where compound 53 sits, so its inhibitory activity can be attributed to blocking this motion. In addition to biochemical activity, the molecule also showed low micromolar activity in a cellular target engagement assay. However, no other cellular data are reported.

 

This is a textbook example of fragment finding and growing against a difficult target. The fact that the researchers returned to fragments after the initial series reached an affinity plateau is a useful reminder that it is all too easy to get stuck in a local minimum, and you may need to take a step back to make progress. Although compound 53 is just on the edge of what may be considered a chemical probe in terms of potency, it is the first reported nanomolar inhibitor of DHX8. The researchers also mention a second series derived from a different fragment, and it will be interesting to see if this yields even better molecules.

Site-specific HDX-MS for increased resolution

Most methods to characterize protein-ligand binding interactions have drawbacks. Crystallography, obviously, requires your protein to crystallize, while NMR and cryo-EM both have size limitations and are resource-intensive. Hydrogen-deuterium exchange mass spectrometry (HDX-MS), which we wrote about earlier this year, is theoretically faster and can apply to more proteins, but the technique provides limited resolution. Now a new open-access paper in J. Am. Chem. Soc. by Derek Wilson and collaborators at York University, the Structural Genomics Consortium, and SCIEX have improved the resolution down to single amino acids.

 

As we wrote in March, HDX-MS relies on the exchange between deuterium and protons on proteins; slower exchange is associated with more stable secondary structures. The exchange is detected by digesting the protein with proteases and determining the mass of the resulting peptide fragments using MS; incorporation of deuterons increases the mass of the peptides. If, for example, a peptide has three deuterons incorporated, the mass would increase by 3. However, in conventional HDX-MS, one can’t tell exactly where the deuterons have exchanged.

 

Last year the researchers described a method in J. Am. Soc. Mass Spectrom. to do just this using some clever mass spectrometry experiments. I won’t go into details as the paper is open access, but suffice it to say that it involves electron-activated dissociation (EAD) to fragment the peptides while preventing the scrambling of the protons and deuterons that normally limits resolution.

 

In the new paper, the approach was applied to the oncology target WDR5, which we previously discussed here. The researchers applied site-specific HDX-MS to three small molecule and two peptide ligands. All five of these ligands had previously been characterized by conventional HDX-MS as well as surface plasmon resonance (SPR), and four had also been studied crystallographically.

 

Reassuringly, the results from site-specific HDX-MS were mostly consistent with conventional HDX-MS, but there were some surprises. For example, the site-specific approach revealed protection from exchange for one amino acid (which makes a hydrogen bond to the ligand) and enhanced exchange for the adjacent amino acid on the same peptide. In conventional HDX-MS, the “peptide contains offsetting uptake increases and decreases, resulting in a ‘net-zero’ signal.”

 

The researchers also found that, for another residue, the change in HDX-MS signal correlates with the affinity of the ligand, as assessed by SPR. The mechanism is unclear since the concentrations of ligands were far higher than their affinities, but the results are intriguing.

 

The new paper describes just a single, well-behaved target, but if site-specific HDX-MS applies more broadly, it would offer a significant advance in obtaining structural information. The researchers note that the data analysis is currently “manual and prohibitively cumbersome,” a problem that should be solvable with automation. I look forward to seeing it applied prospectively to more targets, particularly if it works for weak binders such as fragments.

Filtering fluorescent frauds from fragments for PTP1B

Protein tyrosine phosphatase 1B (PTP1B) is a long-standing target for diabetes and other diseases. But as we’ve written previously, it is challenging to drug due to its small and highly charged active site, which is conserved among phosphatases. In a new open-access Drug Design Dev. Ther. paper, Frank Boeckler and colleagues at Eberhard Karls Universität Tübingen screen fragments against PTP1B and demonstrate the importance of distinct, rigorous controls.

 

The Boeckler group previously developed a fragment library enriched in halogens (HEFLib, which we discussed here) and a separate covalent fragment library (CovLib). These, as well as two other small libraries, were screened against PTP1B using a fluorescence activity assay. Compounds were incubated with the enzyme for an hour at 2 mM in the presence of a substrate (DiFMUP, 6,8-difluoro-4-methylumbelliferyl phosphate) that releases a fluorescent reporter (DiFMU) when hydrolyzed by the enzyme. A molecule that inhibits the enzyme should lead to less fluorescence. Samples were run in triplicate, and detergent was included to avoid aggregation artifacts. A total of 515 fragments were tested, leading to 56 preliminary hits, defined as molecules that decreased fluorescence by more than 50%.

 

A major challenge with fluorescence assays is that the compounds themselves can interfere with the readout. To assess this, the researchers retested their hits both before and after incubation of PTP1B with substrate. A fluorescence artifact should give similar activity in both conditions, while a true inhibitor should only cause a decrease in fluorescence when added at the beginning of the assay, before the protein has had a chance to process substrate. Unfortunately, only 11 hits passed this test.

 

To further confirm mechanism, the researchers developed an HPLC-based assay that separates the substrate DiFMUP from the product DiFMU. Because this assay detects PTP1B activity using separation rather than fluorescence, it should be immune from fluorescence artifacts. Only three of the 56 preliminary hits confirmed in this assay. Dose-response experiments revealed them to be quite weak, with IC₅₀ values above 5 mM. All three are potentially covalent inhibitors, and two showed reactivity with glutathione. PTP1B has a reactive active-site cysteine so covalent inhibitors are not surprising, though it remains to be seen how specific they are.

 

This paper is a useful reminder of the importance of validation: an initial 11% hit rate dropped to just 0.6%. Full experimental details for all the assays are provided, as are the structures of the hits, while SMILES strings of all 515 compounds screened are listed in the supplementary material. If you’re running a fluorescent fragment screen for the first time, this paper is worth consulting.

Merck’s journey from flatland

Back in 2009 we highlighted a paper, “Escape from flatland,” suggesting that molecules with higher fractions of sp³-hybridized (ie, non-aromatic) carbons are more likely to become drugs. This hypothesis has been challenged on methodological grounds, and an analysis last year concluded that “Fsp³ may not have been a useful metric to optimize.” Now a new J. Med. Chem. paper by Olivia Garry and colleagues at Merck weighs in on the debate.

 

The researchers calculated Fsp³ (number of sp³-hybridized carbons divided by total carbons) for millions of small molecules (MW < 1200) synthesized at Merck from 2000 through 2024. Interestingly, mean Fsp³ values dropped from 0.34 to 0.31 between 2000 and 2006 or so, coinciding with the increasing popularity of metal-mediated cross coupling reactions during that period. Since then, Fsp³ has risen to 0.40 in 2024, which could be due to the focus on “escaping flatland” as well as the increasing availability of saturated building blocks and new synthetic methodologies.

 

Consistent with the original publication, the average Fsp³ increases as molecules progress through development, from 0.34 overall to 0.36 for those that go into rat pharmacokinetic studies and to 0.38 for preclinical candidates – close to the 0.39 found for drugs approved since 2009 according to the study last year.

 

A key correlation proposed in the original flatland publication was that solubility increased with Fsp³, and this was observed again here. Of 435,280 compounds having Fsp³ between 0.00-0.70 with measured solubility data, 55% of those with more aromatic character had low solubility, while only 15% of those with Fsp³ > 0.65 were poorly soluble. The correlations held for solubility at neutral pH as well as at pH 2. Molecules with lower Fsp³ also tended to have more aromatic rings, and this has previously been shown to lower solubility.

 

In contrast to solubility, lipophilicity (logD) did not correlate with Fsp³ among 503,780 compounds. Apparent permeability showed a complex and noisy correlation for 16,570 compounds, leading the researchers to conclude that “Fsp³ is not a good parameter to optimize P_app.”

 

Inhibition of three cytochrome P450 enzymes, CYP 3A4, 2C8, and 2C9, was examined for 51,760 compounds. Weak trends were observed for the latter two, with highly aromatic compounds being more likely to inhibit the enzymes, but no trend was seen for CYP 3A4.

 

Inhibition of human ether-a-go-go-related gene (hERG) is a major red flag for compound progression, and here there was a correlation, with higher Fsp³ compounds showing lower inhibition. This trend held for both aminergic and nonaminergic molecules, though as with solubility these trends could be driven by aromatic ring count, which has independently been correlated to hERG inhibition.

 

Since the 2009 flatland publication, many solvents have been spilled to replace phenyl rings with more shapely, non-aromatic moieties. The researchers looked at matched molecular pairs for internal compounds to see what effects these changes had on solubility. Simply saturating a phenyl to a cyclohexyl ring tended to decrease solubility, while the best solubility improvements resulted from cyclopropyl or isopropyl substitutions. But these swaps did little for shapeliness: of the eight substitutions for which sufficient data existed, cyclopropyl and isoprproyl had the lowest increase of average Fsp³. As the researchers note, “using Fsp³ without consideration of other properties is not a good strategy to optimize solubility.”

 

Overall this study suggests that leaving the comforts of flatland may be worthwhile. Molecules with higher Fsp³ values are sometimes more challenging to synthesize, but this itself can lead to new intellectual property space.

 

It’s worth emphasizing that the correlations between Fsp³ and solubility, CYP inhibition, and hERG inhibition are limited. In the end, the researchers recommend “considering Fsp³ in combination with other factors in medicinal chemistry optimization as its standalone effect on specific properties is modest.” Just as general risk factors won’t predict whether any given person will contract a disease, metrics won’t predict whether any given molecule will succeed (or fail) as a drug. 

Ligand affinities for isolated proteins vs in cells and cell lysates

Earlier this year we highlighted a study that compared the affinities of kinase inhibitors for isolated proteins vs those same proteins in cells and found differences – sometimes dramatic. A new open-access paper in J. Am. Chem. Soc. by Blake Peterson and colleagues at Ohio State University addresses the same question, and explores insights from cell lysates as well.

 

To measure affinity in cells, the earlier paper used NanoBRET, which relies on a fusion protein consisting of the kinase and a luminescent protein. It is possible that this artificial protein may behave differently from the native protein, so the Peterson group developed the Fluorescent Probe Cellular Binding Assay (FPCBA). This technique makes use of a fluorophore conjugated to a ligand for the protein of interest. The protein of interest is overexpressed, the binding of fluorescent probe causes cells to become fluorescent, and the level of fluorescence can be quantified using flow cytometry. Adding a (non-fluorescent) ligand displaces the fluorescent probe, which is rapidly effluxed, and the loss of fluorescence can be used to calculate the affinity of the ligand.

 

For the sake of brevity I’m skipping lots of important details, such as detailed characterization of efflux and use of a second fluorescent protein to correlate expression of the target, but these are described extensively in the paper. The researchers focus on a fluorescent probe based on dasatinib, an approved drug that binds to dozens of kinases.

 

The researchers compared the affinity of dasatinib and imatinib, another approved drug, to the kinase ABL1 using both FPCBA and NanoBRET. The results were reassuringly similar, though dasatinib bound 3 or 4-fold more tightly in the PBCBA assay than the NanoBRET assay while imatinib bound up to 2-fold less tightly, which might be due to changes in the regulatory mechanism of the ABL1 fusion protein required for NanoBRET.

 

Whole cell assays tend to be lower throughput, so cell lysates are often used instead. For kinases, a common assay makes use of “kinobeads.” Native kinases are captured from cell lysates on beads and quantified using mass spectrometry. An inhibitor blocks binding of the kinase to the bead, and this reduction in binding can be used to calculate the affinity. The researchers measured the affinity of dasatinib and imatinib against a score of kinases using both FPCBA and kinobeads.

 

Of 21 kinases, 14 showed similar affinities (within four-fold) for dasatinib by both FPCBA and kinobeads. Six of the remaining seven showed up to 13-fold tighter binding in FPCBA than kinobeads, which the researchers attribute to the kinases being in a more active state in cells than in lysates. (A similar phenomenon has been seen for the helicase WRN.)  In contrast, the kinase SRC showed 11-fold weaker activity in cells than lysates, which the researchers attribute to autoinhibitory regulation.

 

The situation becomes even more discordant when comparing affinities in cells to the affinities of isolated recombinant kinase domains using the KINOMEscan assay. Since a picture is worth several hundred words (the length of this blog post), I’ve graphed the results. While possible to see a correlation, it is also possible to see dragons in the shapes of clouds.

 

The affinity of dasatinib was generally higher (often by more than 10-fold) in the KINOMEscan than the FPCBA assay, which is not surprising given the lack of ATP in the biochemical assay. But there were exceptions, most notably EPHA4, which binds slightly more tightly to dasatinib in cells than in the KINOMEscan assay. Interestingly, the affinity in the cell lysate assay was lower; the researchers suggest that in cells the kinase is in a more active form than it is in lysates, perhaps due to membrane localization.

 

In the end, this paper is another useful reminder that assays with isolated proteins do not necessarily translate to cells. And of course, activity in cells does not necessarily translate to activity in vivo. Understanding why is part of what makes drug discovery both frustrating and exciting.

Differential scanning fluorimetry (DSF) for covalent ligands

Differential scanning fluorimetry (DSF), also known as a thermal shift assay, is one of the most common techniques for fragment screening according to the most recent methods poll on Practical Fragments. This popularity is in part due to the simplicity of the assay: just mix your protein with a dye such as SYPRO Orange, which binds to the hydrophobic core of unfolded proteins, heat the solution in a PCR thermocycler, and measure the change in fluorescence. Ligands that bind specifically to proteins often stabilize them, increasing the melting temperature. As we noted in 2017, some companies even use DSF to assess the ligandability of new targets.

 

Most of the focus on DSF has involved non-covalent ligands. But the approach also works for covalent ligands, according to a new open-access paper in the British Journal of Pharmacology by Nir London and colleagues at the Weizmann Institute of Science.

 

The researchers acknowledge that they are not the first to explore DSF on covalent ligands; last year we discussed a screen of 47 covalent fragments against 47 kinases, and just last month we highlighted a DSF screen that identified an unexpectedly covalent fragment hit.

 

The new paper starts by performing DSF on five drug targets (BTK, the G12C mutant of KRAS, SARS-CoV-2 MPro, Pin1, and Keap1), each with well-characterized covalent ligands (for example, ibrutinib, acalabrutinib, and evobrutinib for BTK, sotorasib and adagrasib for KRAS^G12C, etc.) For four of the proteins they also tested non-covalent or covalent reversible ligands. Almost all of the ligands increased thermal stability of the bound protein, and covalent ligands tended to have a greater effect.

 

In addition to testing specific ligands, the researchers also performed DSF experiments on the five proteins with three reactive, non-specific covalent ligands: iodoacetamide, ethyl 2-(bromomethyl)acrylate, and a chloroacetamide called RA13. Intact protein mass spectrometry confirmed that these molecules gave essentially complete modification of the five proteins, in some cases at multiple sites.

 

In contrast to the specific ligands, the reactive alkylators tended either to have marginal effects on the melting temperatures of the proteins or to actually destabilize the proteins, sometimes dramatically. That said, there were cases of stabilization. The researchers suggest that “reactive, non-specific, irreversible binders may act as destabilizers, since they form an irreversible covalent bond regardless of molecular recognition, which may result in deformation of the protein if the rest of the binder cannot be accommodated near the modified amino acid.”

 

To assess how well DSF works for covalent fragments more generally, the researchers acquired 2612 acrylamides from Enamine and screened them (each at 8 µM in pools of 5) against Keap1 for 24 hours at 4 ºC. Mass spectrometry showed that just over 100 gave at least 50% modification, which a back of the envelope calculation suggests a covalent efficiency roughly 1 M^-1s^-1. Next, 84 compounds with differing degrees of modification were tested by DSF, and, in contrast to the kinase paper we discussed last year, there was a correlation between extent of modification and either stabilization or destabilization of the protein. But consistent with the specific ligands discussed above, the more reactive fragments (as assessed by glutathione reactivity) were the only ones that caused destabilization.

 

The authors conclude by proposing “DSF as a fast and complementary follow-up approach for electrophilic fragment screening, to prioritize productive and selective covalent binders over promiscuous reactive fragments.” This seems reasonable – what do you think?

Fluorine NMR meets make-on-demand libraries

Last month Practical Fragments wrote about make-on-demand libraries, such as those offered by Enamine. We’ve also written about fluorine-detected NMR screening of fragments, which is both rapid and particularly sensitive for low affinity binders. A new paper in ChemMedChem by Patrick Penner, Anna Vulpetti, and colleagues at Novartis combines these two concepts.

 

The researchers had previously built a library of 5472 fluorinated fragments. These were compared with the 77 billion molecules then in Enamine REAL Space, which are based on a smaller set of building blocks that can be combined using validated chemistries. More than a third of the Novartis library members could be found in REAL Space, and nearly 80% had close analogs, supporting the notion that one could do rapid follow-up studies of fragment hits without requiring resource-intensive in-house chemistry.

 

To test whether this would work in practice, the researchers turned to embryonic ectoderm development protein (EED), an oncology target that Novartis has been pursuing for some time; here’s a 2017 post. An undisclosed number of known fragment ligands were screened against Enamine REAL Space using three computational methods: a search of the 180,000+ Enamine building blocks themselves, SpaceMACS to find close analogs, and FastROCS to find more distant analogs.

 

Of 150 compounds ordered across the three categories, 125 arrived: 66 building blocks, 30 close analogs, and 29 distant analogs. All of these were first tested by SPR, and nine (mostly close analogs) showed at least double-digit micromolar binding.

 

To assess whether ¹⁹F NMR could identify weaker binders, the remaining 116 compounds were screened in mixtures of nine each. This led to 43 additional hits, of which several were characterized in more detail by competing them in a dose-response format against a reporter molecule to calculate dissociation constants. One of these came in at sub-micromolar affinity, though it was a close analog of a known binder; the others were high micromolar.

 

Two of the more novel (and less potent) molecules were used as starting points to select new molecules from Enamine REAL space, and 74 of the 81 selected were delivered and tested by SPR or DSF. Two of these were more potent than the starting molecules, one of them by more than an order of magnitude.

 

In the end, although the results are modest, the paper provides a detailed framework for applying fluorine NMR to make-on-demand libraries. And with only four authors, it seems to be a low-effort approach. Although this work was done at Novartis, it should be suitable for smaller companies or academic labs that have access to an NMR.

Fragment events in 2026 and 2027

We're half-way through 2026, but there are still some good events ahead. And 2027 is already starting to take shape.

September 14-16: Fragments X, RSC-BMCS Tenth Fragment-based Drug Discovery Meeting, will be held in the original Cambridge (UK). You can read my impressions of the 2024 meeting, the 2013 meeting, and the 2009 meeting.

September 28 to October 1: CHI’s Twenty-Fourth Annual Discovery on Target will be held as always in brilliant Boston. As the name implies this event is more target-focused than chemistry-focused, but there are always plenty of FBDD-related talks. You can read my impressions of the 2025 meeting, the 2024 meeting, the 2023 meeting, the 2022 meeting, the 2021 meeting, the 2020 virtual meeting, the 2019 meeting, and the 2018 meeting.

 

November 10-12: CHI holds its third Drug Discovery Chemistry Europe in beautiful Barcelona. This will include tracks on lead generation, protein-protein interactions, degraders and glues, and machine learning, with multiple fragment talks throughout. 

2027

April 19-22: CHI’s Drug Discovery Chemistry returns as usual to sunny San Diego, and there are always plenty of fragment talks. You can read impressions of the 2026 meeting, the 2025 meeting, the 2024 meeting, the 2023 meeting, the 2022 meeting, the 2021 virtual meeting, the 2020 virtual meeting, the 2019 meeting, the 2018 meeting, the 2017 meeting, the 2016 meeting; the 2015 meeting here, here, and here; the 2014 meeting here and here; the 2013 meeting here and here; the 2012 meeting; the 2011 meeting; and the 2010 meeting. 

September 19-22: FBLD 2027 will be held for the first time in lovely Lund. This will mark the tenth in an illustrious series of conferences organized by scientists for scientists. You can read impressions of FBLD 2024, FBLD 2018, FBLD 2016, FBLD 2014, FBLD 2012, FBLD 2010, and FBLD 2009. 

Know of anything else? Please leave a comment or drop me a note.

Covalent fragment linking on the SARS-CoV-2 Main Protease

Fragment linking is a compelling approach to advancing fragments, but, as our poll from several years ago showed, it often works better in theory than practice. Also, most examples of fragment linking involve non-covalent molecules. In a new open-access ChemMedChem paper, György Keserű and collaborators at HUN-REN Research Centre for Natural Sciences, University of Ljubljana, and Diamond Light Source apply linking to a covalent fragment.

 

The main protease (3CL^pro or MPro) of SARS-CoV-2 was the first target pursued in the fight against COVID-19. A crystallographic fragment screen was conducted just weeks into the pandemic, yielding dozens of hits. Some of these, such as compound 1, formed covalent bonds to the active-site cysteine. Compound 2 bound noncovalently nearby. In the new paper the researchers used the program DeLinker, a “graph-based deep generative method,” to try to link them.

 

Just three linked molecules were made and tested, each with a different amide connectivity. While two were quite weak, compound 5 showed low micromolar activity. This is especially impressive given that both compounds 1 and 2  themselves were inactive. SAR studies revealed that the nitrile warhead is essential for activity, and a crystal structure of compound 5 bound to 3CL^pro confirmed covalent binding. Surprisingly though, the structure showed that the moiety derived from compound 2 binds in a “flipped” position relative to the non-covalent complex. Further fragment-growing led to compound 28, the most potent compound described in the paper.

 

The researchers also tried warhead swapping, replacing the reversible nitrile warhead with a chloride for irreversible aromatic nucleophilic substitution. Although some of these molecules showed activity, the best was about 30-fold less potent. This observation makes sense, as the cysteine sulfur atom must take different trajectories to react with the different warheads.

 

As the researchers point out, more potent inhibitors of 3CL^pro have been reported; the approved drug nirmatrelvir targets this enzyme. However, the paper is still a rare example of fragment linking starting from a covalent fragment; see here for fragment merging on a different target.

 

Personally what I find most interesting is the fact that it took just three molecules to go from inactive fragments to a low micromolar inhibitor. But given that this  molecule binds in a somewhat different manner than predicted, perhaps a high-throughput approach, such as direct-to-biology, would generate even more potent molecules.

Vast fields of biologically active chemical space

Exactly 17 years ago, I highlighted a paper from Brian Shoichet’s lab in which he hypothesized that only tiny regions of chemical space are biologically relevant. If so, Practical Fragments quoted, “a major reason why the screening of synthetic compounds ever finds notable hits is that our libraries are biased toward the sort of molecules that proteins have evolved to recognize.” The best scientists continually question their assumptions, and this is exactly what Brian, Bryan Roth, and collaborators at University of California San Francisco and University of North Carolina at Chapel Hill have done in a new open-access J. Med. Chem. paper.

 

The idea that molecules more closely resembling metabolites and natural products would be more likely to be biologically active is reasonable given the promiscuity of many naturally occurring molecules. As the researchers point out, dopamine signals through 5 receptors, while serotonin binds to 14. Nature is the ultimate recycler, constantly reusing chemical motifs.

 

But since 2009 there has been a massive growth of make-on-demand libraries, and these have increasingly diverged from known molecules. Brian has been among the most prolific explorers of this new chemical space, and as he noted in his keynote at DDC 2026, these gargantuan libraries are yielding more hits against more targets.

 

Are these hits more selective? In other words, are molecules that are less “bio-like” likely to bind to fewer targets? This is the question the new paper addresses, focusing on the 5-HT_2A serotonin receptor (5-HT_2AR), the target for a variety of drugs including psychedelics. The researchers have extensively studied this GPCR; we wrote about a successful computational screen here. In the new paper, the researchers compared hits from a previous “make-on-demand” (MoD) library screen of 1.6 billion molecules with a new screen of 3.5 million “in-stock” molecules. As predicted, the in-stock compounds were much more bio-like than the MoD compounds as assessed by several metrics (Tanimoto similarity, Avalon fingerprints, and RDK7 fingerprints, for the cheminformatics aficionados among you).

 

Both libraries were computationally screened using DOCK3.8 against 5-HT_2AR. Of the top scoring hits, 85 molecules from the in-stock library and 384 molecules from the MoD library were tested in a radio-ligand displacement assay. Hit rates were nearly identical at around 24% each. Functional assays revealed the hits to be active, with some acting as agonists while others were antagonists.

 

When tested against related GPCRs, specifically 5-HT_2BR and 5-HT_2CR, the in-stock molecules were more promiscuous than the MoD molecules. But when tested against a panel of 318 GPCRs there were no differences: the in-stock molecules bound on average 1.7% of the receptors while the MoD molecules bound on average 2.1%. The similar promiscuity persisted even when focusing on the 55 aminergic GPCRs (5-HT_2AR is an aminergic GPCR). In fact, the most promiscuous compound of all came from the MoD set and is distinctly not bio-like.

 

Of course, this is just one study, but I suspect it will generalize. In 2009 I questioned whether biologically relevant chemical space is really so sparse. As I wrote then, “consider a vast field of some crop that can only be harvested by night. There are lights scattered haphazardly throughout the field. One might expect that the crops immediately under the lamp posts would be harvested more intensively than crops in darker parts of the field, even if other areas are equally productive. In this scenario, the lamp posts reveal natural products and similar molecules, but much – or even most – of (unlit) chemical space may also be biologically active, it just hasn’t been sampled yet.”

 

There is grandeur in this view of chemical space, which is supported by the new paper. Imagine yourself in the midst of the field on a moonless night. Turn on your headlamp and step forward in any direction. Drug leads glimmer as far as your light can reach.

An unexpected lysine-reactive covalent fragment against a Pleckstrin Homology domain

Last week we highlighted a study in which an attempt to optimize a covalent fragment led to a non-covalent fragment that bound in a different location. A new open-access paper in J. Med. Chem. by David Spring, Marko Hyvönen and colleagues at University of Cambridge is almost the inverse – a surprisingly covalent binder from a conventional fragment screen.

 

The researchers were interested in Pleckstrin Homology (PH) domains. The 250 or so PH domains in humans bind to the intracellular side of the plasma membrane by interacting with phosphoinositides such as PIP3. Blocking membrane recruitment could be useful for multiple diseases, but the highly charged nature of the interaction (the “P3” in PIP3 means three phosphate groups) makes drugging PH domains difficult, a perfect challenge for fragments.

 

The study focuses on the PH domain from Bruton’s Tyrosine Kinase (BTK), an important oncology and immunology target with several approved drugs, all of which bind to BTK’s kinase domain. Its PH domain was screened against 720 fragments using differential scanning fluorimetry (DSF). Seven fragments stabilized the melting temperature by a whopping 5 degrees C or more. Crystallography was successful for compound 1, which surprisingly revealed that the ketone reacts with the terminal amine of lysine 12, which normally makes electrostatic interactions with two phosphates in phosphoinositides.

 

The crystal structure showed unoccupied space nearby; subsequent fragment growing and optimization led to compound 24, with low micromolar affinity as assessed by DSF. The researchers obtained some two dozen crystal structures, which they were able to correlate with structure-activity relationships (SAR). All of the molecules formed the imine with K12; reducing the ketone to an alcohol abolished stabilization in DSF.

 

Intact protein mass spectrometry was used for assessing SAR and confirming that the covalent bond formation is reversible: adding two fragments with different affinities led to the same distribution of products regardless of the order of addition of the fragments. Not surprisingly, the reaction was faster at higher pH, but nearly complete covalent modification still took place within an hour at pH 7.4.

 

Even at high compound to protein ratios the molecules largely gave single protein modification, and two separate computational studies of the pK_a values of the 15 lysine residues in the BTK PH domain revealed that K12 is an outlier, with a calculated pK_a of 7.1 or 8.8 compared to an average of 10.5 or 10.8 for the others. This means that K12 is largely unprotonated at physiological pH, increasing its reactivity. A similar analysis of four related PH domains suggested that the equivalent lysine residues also have anomalously low pK_a values.

 

The fact that K12 is conserved across related PH domains does raise the question as to whether selectivity will be possible with this type of ligand. Also, no in vitro ADME properties are provided, so it is not clear whether this warhead is advanceable. I wish the researchers had explored other heterocycle alternatives to the furan moiety to assess the tunability of the reactivity; perhaps those will come later. Overall though, this paper is a nice case study where following up on unexpected observations identified a new approach for covalently targeting lysine residues. As Isaac Asimov famously said, “The most exciting phrase to hear in science … is not ‘Eureka!’ but ‘That’s funny.’”

From covalent to noncovalent 14-3-3 modulator, unintentionally

The seven members of the 14-3-3 family act as “hub proteins” that bind to other proteins, bringing them together or affecting their subcellular localization. Some of the client proteins, such as estrogen receptor alpha (ERα), are associated with diseases such as cancer, and stabilizing the interaction with 14-3-3 could thus be useful therapeutically. The idea is that a small “molecular glue” could bind at the interface between 14-3-3 and a client protein, enhancing the interaction. We’ve written here, here, and here about successful examples, both covalent and noncovalent. An open-access paper in ACS Med. Chem. Lett. earlier this year by Richard Doveston and colleagues at University of Leiscester starts with a similar strategy, but ends up somewhere else entirely.

 

The researchers had previously found that WR-1065, the active metabolite of the approved drug amifostine, can covalently bind to C38 of 14-3-3σ. (This is the same cysteine residue that had been targeted by Michelle Arkin and coworkers in 2019 using tethering.) Disulfide bond formation between WR-1065 and 14-3-3σ enhances the affinity of ERα for 14-3-3σ by a modest 2.8-fold.

 

In the new paper, the Doveston group made a small set of analogs in which the thiol was replaced by other warheads. Compound 7, containing an acrylamide, improved the affinity of ERα for 14-3-3σ by nearly two orders of magnitude, from 206 to 2.8 nM. But surprisingly, mass spectrometry revealed no covalent modification. Indeed, compound 7 was also active at low micromolar concentrations against the C38A mutant form of 14-3-3σ, while WR-1065 was inactive.

An examination of the structure-activity relationships (SAR) revealed that the acrylamide was essential; reducing the double bond abolished activity, while replacing the amide nitrogen with an oxygen made it significantly less active. Removing the primary amine or replacing it with a hydroxyl or carboxylic acid was also not tolerated.

 

If compound 7 does not bind covalently to C38, how does it work? Surprisingly, the molecule does not even bind at the interface between 14-3-3σ and ERα. Indeed, it shows additivity with a well-characterized molecular glue (fusicoccin A) known to bind at the interface. Where exactly compound 7 does bind is uncertain. A series of biophysical experiments suggests that it may stabilize the dimeric form of 14-3-3σ, though the mechanism remains to be determined.

 

This paper is a useful reminder that, as the authors conclude, “even rational design approaches can lead to unexpected outcomes.” Sometimes a warhead does not behave as one.

Fragments in the clinic: VVD-214

Just over two years ago we highlighted a new clinical candidate targeting WRN, a covalent inhibitor then called VVD-133214. An open-access paper published near the end of last year in J. Med. Chem. from Shota Kikuchi, David Weinstein, and colleagues at Vividion describes in detail the optimization of the covalent fragment hit to the clinical compound. (Shota presented some of this work at the 2024 DDC meeting.) This paper is also an interesting contrast to non-covalent fragment-finding approaches against this target we wrote about earlier this month.

 

The 2024 post described the chemoproteomic screening that identified compound 1a, which covalently binds to C727 in WRN. An early observation was that some molecules were cooperative with ATP while others were competitive. Given the high concentration of ATP in cells, the researchers prioritized the former category, which led to compound 1f. (Note that while I’m showing only the k_inact/K_I values, the researchers used biochemical and cell-based assays to drive SAR).

 

The vinyl sulfone warhead is unusual amongst covalent clinical compounds, so the researchers sought to characterize it. The rate of reaction with glutathione for compound 1f is comparable to the approved drug osimertinib: reactive, though acceptable. To try to lower the reactivity and also prevent isomerization of the double bond, the researchers introduced a methyl group. Compound 2a not only showed increased stability but also improved activity against WRN and sub-micromolar activity in a cell-based assay. A crystal structure of a later molecule revealed interactions with a hydrophobic patch on the protein, explaining the improvement in potency. Importantly, the other enantiomer was much less potent.

 

Other rings were tried, unsuccessfully, to replace the pyrimidine and the phenyl moieties. However, changing the cyclopentyl ring to a tert-butyl moiety (compound 5d) further improved the potency to the point where the compound could be tested in vivo, where it proved to be active in a mouse xenograft study. Mass spectrometry experiments revealed prolonged occupancy of C727 out to 24 hours after compound dosing even though the compound itself had been cleared, consistent with a long half-life of the WRN protein. The researchers note that high target engagement (TE) at 24 hours was predictive of tumor growth inhibition, which streamlines optimization since it is easier to run a one-day TE experiment than a multi-week efficacy study.

 

Further optimization of ADME properties ultimately led to VVD-214, which was active in a mouse xenograft study and showed good oral bioavailability and pharmacokinetics in mouse, rat, dog, and monkey. This compound was also profiled in a chemoproteomic assay and found to be quite selective for the C727 of WRN.

 

There are several important lessons in this paper. First, the initial fragment is larger than prescribed by the rule of three, consistent with an analysis of covalent inhibitors last year. Second, much of the SAR was empirical; crystallography was not used until relatively late in the campaign. When a crystal structure was finally solved of VVD-214 bound to WRN it revealed no polar contacts between the ligand and the protein, only hydrophobic interactions, which is rare for fragments, let alone drugs. Perhaps because of the lack of polar interactions, it was impossible to measure the inhibition constant (K_I), and saturating the warhead to make it unreactive completely abolished activity. In other words, the binding is largely driven by reactivity, but specific reactivity for WRN rather than generic chemical reactivity.

 

In 2024 just two WRN inhibitors had entered the clinic, the other being a noncovalent molecule called HRO761. We quoted the Vividion team as saying that “this presents a rare opportunity to compare two small molecule oncology drugs targeting the same protein by different mechanisms.” Since then, HRO761 has been quietly discontinued, as has another noncovalent drug, IDE275. Meanwhile, development of VVD-214 is ongoing, and another covalent compound, MOMA-341, which we mentioned here, has also begun human testing. (To be fair, so has yet another non-covalent molecule NDI-219216. And the binding mechanism of a sixth WRN drug, EIK1005, is undisclosed.) While it’s still early in the match, covalent drugs seem to be punching above their weight. May the best drug(s) prevail - Practical Fragments is rooting for them all.

From noncovalent fragments to covalent WRN inhibitors

Last week we described the discovery and early optimization of noncovalent inhibitors against the the DNA helicase WRN, an interesting oncology target both for its conformational flexibility as well as a ‘synthetic lethal’ approach to cancer drugs. Today we continue the theme with a Bioorg. Med. Chem. Lett. paper from Geoffrey Smith and colleagues at CHARM Therapeutics.

 

At the time the project began, no specific WRN inhibitors had been reported, but a crystal structure of the ADP-bound form of the protein had been published. The computational tool Fpocket, which we wrote about here, was used to identify several ligandable pockets.

 

To find actual ligands, the researchers crystallographically screened a library of 860 "poised" fragments from Enamine and used PanDDA (see here) to identify binders, even those with low occupancies. Several ligands occupied pockets that had been identified by Fpocket. More interestingly, five fragments bound in a previously cryptic site that had not been predicted. This pocket was formed by rotation of two phenylalanine residues as well as peptide backbone movements, consistent with sites able to support high affinity ligands, as we discussed in 2024. Thus, the focus turned to ligands that bind here.

 

Because of its low occupancy in the crystal, the orientation of compound 3 was ambiguous, so the researchers turned to a machine-learning-based protein-ligand co-folding algorithm called DragonFold. This revealed that the fragment binds in close proximity to a cysteine residue, C727, known to be reactive. Scaffold hopping and addition of a covalent warhead led to compound 4d. While the initial compound 3 showed no activity in a WRN helicase assay, compound 4d showed micromolar activity. Moreover, a crystal structure revealed binding to C727. Further SAR led to molecules such as compound 9b, the most potent WRN inhibitor reported in the paper.

 

Compounds were also tested against the closely related helicase Bloom syndrome protein, or BLM, and most of them were active, though the SAR varied between WRN and BLM. The activity against BLM is odd given that the residue corresponding to C727 is a serine, but the researchers note that the molecules might bind to other cysteine residues in BLM. Although no chemical reactivity data are provided for the ligands, I suspect they are somewhat reactive.

 

Several important lessons can be drawn from this brief paper. The fact that an experimental screen was able to identify cryptic pockets missed in a computational screen justifies empirical approaches. The identification of these pockets is all the more impressive given that pre-formed crystals were sufficiently flexible to undergo significant conformational changes. But computational approaches did prove their utility in refining the binding mode of the ligand. And finally, this is another example of appending a covalent warhead onto a non-covalent ligand.

 

Next week we’ll conclude this WRN trilogy with a covalent-first example.

Noncovalent fragments vs WRN

Werner syndrome helicase, or WRN, is an interesting target both for its biological mechanism and its flexible structure. Two years ago we highlighted work out of Vividion describing the discovery of a clinical-stage covalent WRN inhibitor. In a Nat. Commun. paper published earlier this year, Sandra Gabelli, Daniel Wyss, and collaborators at Merck and Proteros describe their noncovalent efforts against this target.

 

Inhibiting WRN kills cancer cells that are already defective for certain DNA repair pathways. It is an example of a “synthetic lethal” approach to drug discovery that expands the number of cancer targets by focusing on oncogenic cells rendered vulnerable by pre-existing mutations. As an ATP-dependent helicase, WRN acts as a molecular machine to unwind DNA. This requires the multidomain protein to undergo dramatic conformational changes, which makes finding ligands challenging: how do you know which conformation(s) to target? Moreover, WRN enzymatic assays are particularly prone to false positives; a paper published in 2024 demonstrated that some previously disclosed inhibitors are at best nonspecific, and at worst downright artifacts. Thus, the researchers chose to use biophysics to identify fragments.

 

A library of 1020 fluorine-containing fragments was screened in pools of up to 21 compounds using ¹⁹F NMR T₂ CPMG experiments. The 31 primary hits were re-screened as pure compounds in this assay as well as three more ligand-detected NMR assays, leading to seven hits taken into crystallography, of which three yielded structures. A separate SPR screen of 500 non-fluorinated fragments followed by confirmation by NMR led to three additional fragments characterized crystallographically. None of the validated fragments from either screen showed functional activity in an enzymatic assay.

 

The fragments bound in three different sites on the protein, which itself underwent significant conformational changes to accommodate the fragments. Fragments 1 and 2 bound in the same site and could be partially superimposed on one another, and these were used to generate a virtual library, of which 17 compounds were made and tested. Compound 4 had the best affinity as assessed by SPR and was also active in a functional assay.

Crystal structures of some of the other compounds bound to WRN were also determined, and these showed significant protein domain rearrangements, even when the compounds themselves were structurally similar. The researchers include a nice movie (link to download here) and suggest that “these structures capture only a few states of WRN as it translocates along the DNA and conducts its helicase and exonuclease functions.”

 

This paper nicely illustrates the challenges of finding ligands, particularly noncovalent ones, against conformationally flexible proteins. We’ll revisit this topic next week.