Automating high-throughput NMR

NMR was the first practical method used to identify fragments, described thirty years ago in this famous SAR by NMR paper. That headstart made NMR the go-to method for finding fragments, and as recently as ten years ago it was still in the lead, according to our poll in 2016. But as other methods have become faster and more effective, the relative popularity of NMR has declined, to fourth place in our 2024 poll. Now, a new paper in J. Med. Chem. by Bradley Falk and colleagues at Bristol Myers Squibb reports improvements that might help it move up the ranks.

 

One of the challenges of NMR is that it requires so many steps. Protein-detected NMR requires isotopically labeled protein, but even ligand-detected NMR involves measuring spectra for small molecules in the absence and presence of protein. The specific NMR settings may need to be tweaked for any given molecule, and the spectra are then analyzed, usually manually, to decide whether the molecules are likely binders.

 

The new paper focuses on automating virtually everything in ligand-detected NMR, from sample preparation through NMR runs to analysis. It is meant to be more general than a fragment screen with a curated library where each member is already well understood. First, novel compounds are examined in a band-Selective Optimized Flip Angle Short Transient (SOFAST) NMR experiment, which is particularly sensitive (down to 20 µM ligand). Testing in both DMSO-d₆ and assay buffer provides solubility and quality control data. This is an essential step: as we noted some time ago more than a third of compounds in some vendors’ catalogs fail these quality checks. In addition, a CPMPG SOFAST experiment in assay buffer reveals whether the compounds aggregate, a hazard we wrote about here.

 

Next, an automated workflow funnels appropriate compounds to the next step, in which compounds are assessed for protein binding using CPMG or STD NMR. Four separate samples are tested: low or high concentrations of compound in the absence or presence of protein. Again, automated approaches are used to choose the specific NMR parameters and analyze the results. As the researchers note, “human input is reserved for evaluations that require integrating multiple lines of evidence.” The output places compounds into three bins, “advance to med chem,” “advance with orthogonal confirmation,” or the garbage bin.

 

The researchers describe an example using the model protein human carbonic anhydrase II (hCAII), which we’ve written about previously. The known ligand benzenesulfonamide was tested and passed into the “advance to med chem” bin, despite the fact that, with a K_D of 0.8 µM, it is more potent than most ligands studied by NMR.

 

One thing missing from the paper is a summary of overall time and throughput. The experimental section states that the CPMG-SOFAST experiment itself takes half an hour, and indeed the researchers note that the platform is “instrument-time limited rather than sample preparation- or data analysis-limited.” In terms of absolute throughput, it probably can’t compete with other biophysical methods such as SPR. The automation and automated decision-making is the real advance. In 2019 I satirically imagined a fully autonomous drug discovery engine. As artificial intelligence becomes ever more intelligent, that post may prove prescient. If so, it will feature automated tools such as those described in this paper.

Twentieth-Fourth Annual Discovery on Target Meeting

Cambridge Healthtech Institute held its annual Discovery on Target (DoT) meeting in Boston last week. Attendance may have set a record, with over 1000 participants. There were sessions on antibodies, conjugates, and radioligands, but I’ll just touch on some of the small molecule themes most relevant to this blog.

 

Covalent approaches

As with last year, covalent approaches to drug discovery were well-represented. Dhirendra Simanshu (Frederick National Laboratory) described several drugs birthed in the NCI RAS Initiative, including ones targeting the G12C mutant of KRAS and C242 on the RAS-binding domain of PI3Kα. He also described how various KRAS mutants cause resistance to drugs, finding that most of them affect the K_I term (binding) more than the k_inact term (bonding). Continuing on the KRAS theme, Jack Sloan discussed efforts at BMS to discover inhibitors targeting both the on- and off-states of KRAS G12C.

 

Kasia Handing (Tango Therapeutics) presented both covalent and non-covalent approaches against VRK1, a synthetic lethal target for glioblastoma. A biochemical screen yielded covalent inhibitors that were characterized crystallographically, but they turned out to be too non-selective to progress. In the meantime, an in-cell DNA-encoded library (DEL) screen led to more advanceable molecules.

 

DEL was indeed a major topic of the conference, and here too covalent approaches are being explored. Elizabeth D’ambrosio described how GSK is identifying covalent binders from DEL screens, a process which includes testing for time-dependence as well as aggressive washing steps under denaturing conditions to quench unreacted molecules and wash away any sticky, non-covalently bound compounds. And Xiaojie Bruce Lu (Chinese Academy of Sciences) described applying DEL to a variety of targets, including some covalently targeting non-cysteine residues.

 

One of the advantages of DEL is being able to screen large libraries, and increasing throughput is also of interest to Harvard’s Steve Gygi, who is screening covalent fragments against cells and cell lysates. In 2024 we described how he was using tandem mass tag (TMT) multiplexing to analyze 18 samples in a single mass-spectrometry run, and he has now extended this to 35 samples. What’s more, Steve is combining 16 different fragments in each sample and introducing clever deconvolution approaches to further accelerate the process.

 

Not everyone is targeting cysteine residues. Mark Tye described the discovery of Revolution Medicines’ zoldonrasib, which uses an aziridine warhead to target the G12D mutant of KRAS. And Balyn Zaro (University of California San Francisco) described using ninhydrin derivatives to target arginine. Most organic chemists are familiar with ninhydrin’s reaction with primary amines such as the N-termini of peptides, but Balyn has been able to optimize selectivity for arginine and has used the resulting chemical probes to identify more than 7000 arginine residues in cells, about 5% of which are hyperreactive. Interestingly, some cell lines seem to have a greater proportion of reactive arginine residues than others.

 

Direct-to-Biology (D2B) approaches

Another recurring theme was direct-to-biology, in which crude reaction mixtures are screened directly in various assays without purification. Indeed, Nicholas Bland of Domainex said that after looking at the agenda of the meeting he considered skipping the introductory slides – and this was on the first official day of the conference. Nicholas described using D2B for optimizing PROTACs as well as for improving fragment hits against the GPCR A_2A receptor.

 

Jack Sadowsky, who also presented at DDC this past spring, described how he and his colleagues at Kimia are using D2B in combination with machine learning to develop selective inhibitors of kinases while also improving their in vitro ADME properties. This effort entailed making and testing tens of thousands of compounds from a virtual space of some 80 million molecules.

 

But even small-scale D2B can be valuable. Trevor Sherwood described how he and his colleagues at BMS used D2B to make and test 663 molecules to improve the potency of an EP4 agonist identified from a high-throughput screen. This involved multiple reaction types, including photoredox chemistry, for which they built a custom-designed reactor to evenly heat and illuminate the microtiter plates. The D2B work was done in parallel with medicinal chemistry, but interestingly the development candidate BMS-986526 came directly from the D2B work and has entered a phase 1/2a trial for rheumatoid arthritis.

 

There were other talks and posters on D2B that I missed, but I did attend two breakfast roundtables on the topic that were quite lively despite happening at 7:30 – a testament to the interest in the topic.

 

“Traditional” fragment-based drug discovery

At a plenary panel moderated by Drug Hunter’s Dennis Hu on drugging difficult targets, Biogen’s Klaus Urbahns asked the other panelists if they were still doing fragment-based lead discovery. John Tallarico of Novartis and Ian Storer of AstraZeneca immediately said yes. And in a separate presentation, Kris Borzilleri described how she and her Pfizer colleagues used fluorine-detected NMR and fragment optimization to identify ligands for an orphan GPCR.

 

Debanu Das described using high-throughput crystallography screening at XPose Therapeutics, now part of Mid-Atlantic BioTherapeutics, to identify inhibitors of DNA damage response pathway proteins. Although the initial hits had no detectable activity, SAR by catalog was able to get to nanomolar inhibitors of the protein APE1, which is overexpressed in ovarian cancer, in a matter of weeks.

 

But perhaps the most robust validation of FBDD was provided by Steve Fesik of Vanderbilt, who described a decades-long effort to drug the MYC pathway, which is probably dysregulated in almost all cancers. We described some of his published work on WDR5 here, but he is now making progress against MYC itself. This has not been easy: MYC is intrinsically disordered, and SAR-by-NMR screens against both the transactivation domain and the DNA-binding domain failed. However, MYC forms a structured leucine-zipper heterodimer with the protein MAX, and screening this complex yielded hits at the DNA binding interface. (Earlier this year we described work from Novartis on a different leucine zipper.) The hits have been optimized to low nanomolar binders that disrupt DNA binding, unlike a slide full of other (alleged) MYC inhibitors, including high-profile commercially available compounds such as MYCi361 and MYCi975.

 

I’ll close – hopefully before you stop reading. If you attended, what talks or posters stood out? And if you wish you had attended, mark your calendar for Oct. 18-21 next year, when DoT returns to Boston in time for peak leaf-peeping season.

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.

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.

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.

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.

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.

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!

Fragment merging – and flipping – on the leucine zipper of MITF

Transcription factors can be difficult drug targets, particularly those whose primary structure is a “leucine zipper” in which two α-helices gently coil around each other. Their three-dimensional structure provides few pockets suitable for binding small molecules. In a new (open-access) paper in Nat. Commun., Deborah Castelletti, Wolfgang Jahnke, and a large group of multinational collaborators at Novartis and elsewhere present progress toward one of these, microphthalmia-associated transcription factor (MITF), which has been implicated in melanoma.

 

Most of MITF is believed to be disordered, but the DNA-binding domain (DBD) homodimerizes as a basic helix-loop-helix leucine zipper. Unlike related transcription factors, the helices in MITF contain a small kink that keeps them from heterodimerizing and also creates a small “kink pocket.”

 

The researchers expressed the DNA-binding domain of MITF and screened it using ¹⁹F NMR against the LEF4000 library, which we described here. This yielded just 9 hits that confirmed in protein-observed NMR, a hit rate the researchers note “is amongst the lowest that we have observed across multiple FBS campaigns,” consistent with expectations for a difficult target. Two chemical series, represented by compounds 1 and 2, were prioritized, and analogs from the Novartis compound collection were screened to find more-potent compounds 3 and 4.

 

Crystallography revealed that compounds 3 and 4 both bound in the kink pocket. Excitingly, the binding modes are similar and overlapping, inviting fragment merging. This proved successful, yielding a compound that bound 100-fold more tightly than either fragment. Further optimization ultimately led to compounds 7 and 8, with low or sub-micromolar affinity as assessed by isothermal titration calorimetry (ITC).

 

The bound structures of compounds 7 and 8 were determined by crystallography. Compound 7 (gray, left) superimposes nicely onto compounds 3 (cyan) and 4 (magenta), showing successful fragment merging. Compound 8 (green, right), however, is flipped 180 degrees compared to compound 7, despite having similar structure and affinity. Although surprising, this is not too uncommon; we’ve written about previous flippers here, here, and here.

The MITF homodimer is asymmetric, with one helix kinked and the other straight. NMR experiments and molecular dynamics show that both compounds 7 and 8 slow the interconversion between kinked and straight forms, though it is unclear whether this has functional implications. The compounds do not seem to affect DNA binding, and with at best high nanomolar affinity towards MITF no cell data are reported with the molecules.

 

Nonetheless, the successful identification of ligands against a leucine zipper is exciting. The binding pocket is small; as shown in the figure above, the best compounds already stick out on either side of the helices. Further affinity improvements may be difficult, though perhaps covalent approaches could help. Alternatively, perhaps these molecules could be starting points for induced proximity strategies such as PROTACs. It will be fun to watch this story develop.