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.

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.

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.

Review of 2025 reviews

Turning and turning in the widening gyre

The falcon cannot hear the falconer…

 

In our small, annual counterweight to Yeats’ “mere anarchy," Practical Fragments looks back on 2025.

 

This year marked the thousandth post on Practical Fragments, a milestone neither Teddy nor I imagined when the blog launched back in 2008. In terms of conferences, I wrote about CHI’s Drug Discovery Chemistry in San Diego here and Discovery on Target in Boston here.

 

For the past decade I’ve participated in annual J. Med. Chem. perspectives covering fragment-to-lead success stories, two of which published this year. The first, spearhead by Rhian Holvey at Astex, covers the year 2023, while Astex’s David Twigg took the lead on covering the year 2024. In addition to the tabular summaries for which these reviews are best known, both also include tables of “near misses,” none of which made the main tables because the starting points were (sometimes just slightly) too large. Four out of six of these heavyweights are covalent fragments, suggesting that the rule of three may need to be relaxed for these. The most recent paper also includes a table showcasing the eight approved FBLD-derived drugs.

 

Two more general publications are also of interest. In a brief (open-access) editorial in J. Med. Chem. Weijun Xu and Congbao Kang at A*STAR summarize fragment-finding methods and approved drugs and discuss future applications of FBLD in PROTACs and targeting RNA. And in Curr. Res. Pharm. Drug Discov., Geoffrey Wells, Exequiel Porta, and colleagues at University College London present a “graphical review” which covers library design, screening strategies, hit validation, fragment optimization, and a few case studies of approved drugs, along with current challenges.

 

In Drug Des. Devel. Ther., Bangjiang Fang and colleagues at Shanghai University of Traditional Chinese Medicine present an open-access bibliometrics analysis of 1301 fragment-based drug design papers published between 2015 and the end of 2024, which includes top ten lists of institutions, authors, and papers as well as keyword trends and analyses. Annual growth has averaged 1.4%, and the field is both global and collaborative, with 35% of publications involving more than one country.

 

Targets

Two reviews focus on oncology. The first, in Bioorg. Chem. by Milind Sindkhedkar and collaborators at Manipal College of Pharmaceutical Sciences and Lupin Ltd., briefly covers the history and practice of FBDD before providing short summaries of seven of the eight approved drugs to come from it. The second, published open-access in Chem. Rev. by Vanderbilt’s Steve Fesik, is a concise and highly readable introduction and account of the author’s groundbreaking work on BCL-2 family proteins, KRAS, and WDR5.

 

A much longer open-access review in Chem. Rev. by Paramjit Arora and colleagues at New York University covers protein-protein interactions (PPIs). Much of the focus is on larger molecules such as cyclic peptides, peptide mimetics, and other macrocycles, but there are summaries of fragment-based approaches against KRAS and 14-3-3 proteins.

 

The topic of 14-3-3 proteins is treated more fully in an open-access Acc. Chem. Res. paper by Michelle Arkin and colleagues at UCSF. While the focus of most efforts against PPIs is to find inhibitors, for 14-3-3 the goal is to find stabilizers, or molecular glues. The Arkin lab and others have been succeeding using various approaches, particularly disulfide tethering. We wrote about these efforts most recently in 2023, and the new review provides a nice update.

 

Fragment finding methods and libraries

Sahra St. John-Campbell and Gurdip Bhalay, both at The Institute of Cancer Research, published a massive open-access perspective on “target engagement assays in early drug discovery” in J. Med. Chem., covering a host of biochemical, biophysical, and cell-based assays. A table lists more than 50 different techniques, almost half of which are applicable to FBLD. Each row shows what characteristic(s) are measured as well as critical requirements for  protein, sample, and equipment. The paper is also beautifully illustrated with dozens of figures: one shows which techniques are most useful for different types of targets, and each method gets its own diagram.

 

A more focused open-access review is provided by Stefanie Freitag-Pohl and colleagues at Durham University in Biophys. Rev. After surveying various biophysical techniques, the researchers focus on spectral shift analysis, and in particular the Dianthus instrument from NanoTemper Instruments. This plate-based, high-throughput microfluidics-free instrument can detect changes in fluorescence caused by environment or temperature. Examples demonstrate affinity measurements across several orders of magnitude, up to double-digit millimolar, and a nice scheme shows use of the Dianthus in a fragment-screening workflow.

 

Moving to specific techniques, Jia Gao, Ke Ruan, and colleagues at University of Science and Technology of China Hefei provide an open-access survey of “the rise of NMR-integrated fragment-based drug discovery in China” in Mag. Res. Lett. After a brief overview of NMR approaches, they cover case studies from China, most of which are focused on fragment screening rather than optimization.

 

A less common biophysical method is native mass spectrometry (nMS), the subject of an open-access opinion in RSC Med. Chem. by Louise Sternicki and Sally-Ann Poulsen at Griffith University. This is a good survey of the approach; we highlighted a more fragment-focused review by the same authors last year.

 

The most common fragment-finding approach, X-ray crystallography, is covered in two open-access reviews. The first, in Acta Cryst. F by Sarah Bowman and collaborators at University of Buffalo and Brookhaven National Laboratory, focuses on critical early stages, from protein characterization to sample preparation and various crystallization approaches. The second, in Curr. Opin. Strut. Biol. by Martin Noble and colleagues at Newcastle University, starts by briefly reviewing crystallographic fragment screening before turning to fragment libraries. The paper includes a nice table summarizing publicly available libraries at major synchrotrons, with the text describing these in more detail.

 

The provider of one of these libraries, EU-OPENSCREEN, is the subject of an open-access review in SLAS Discov. by Robert Harmel and collaborators at EU-OPENSCREEN ERIC and Fraunhover ITMP. As we wrote last year, EU-OPENSCREEN is a broad consortium whose mission is to advance early drug discovery by providing access to technology and expertise. The new paper summarizes the four compound collections, including the European Fragment Screening Library (EFSL), and surveys progress to date. It also lays out ambitious plans, including expanding to >30 sites in nine countries.

 

Computational approaches and cryptic sites

Despite the hype about artificial intelligence in the broader world, AI in fragment-based drug discovery has been less common. In Curr. Opin. Struct. Biol., Woong-Hee Shin and colleagues at Korea University College of Medicine summarize applications to fragment growing, merging, and linking. The open-access paper includes a handy table of 13 programs, and includes GitHub links where available.

 

Cryptic binding sites, defined by Ehmke Pohl and collaborators at Durham University and Cambridge Crystallographic Data Centre “as binding pockets that exist in the ligand-bound state of a protein but not in its apo form,” are the focus of an open-access review in Bioinform. Adv. The researchers cover earlier computational approaches for finding these, especially molecular dynamics (MD) and machine learning (ML). They note that a key challenge for ML is the limited quantity and quality of experimental data: undiscovered cryptic sites would be misclassified as non-binding sites.

 

Yowen Dong, Ge-Fei Hao, and colleagues at Guizhou University review “computational methods for identifying cryptic pockets” in Drug Discov. Today. As with the previous review, these are divided between molecular dynamics and AI-based techniques, which are discussed individually and then compared. The researchers apply six approaches to the model bacterial protein TEM-1 β-lactamase and find that, for this highly studied single protein, the AI-based methods are much faster (seconds instead of days) and just as accurate, though MD-based methods provide more insight into formation mechanisms of cryptic pockets.

 

Covalent ligands

Allosteric sites are an important sub-class of cryptic pockets, and in J. Med. Chem. Jianing Li and colleagues at Purdue University discuss covalent allosteric inhibitors. After briefly discussing advantages of covalent molecules, they review examples targeting protein phosphatases, kinases, and GTPases, such as KRAS.

 

Of course, covalent molecules are not limited to allosteric sites. An open-access review in Bioorg. Med. Chem. Lett. by Walaa Bedewy, John Mulawka, and Marc Adler at Toronto Metropolitan University summarizes published covalent protein ligands, grouping them by target site:  active sites, residues adjacent to an active site, protein-protein interfaces, cofactor binding sites, and allosteric sites.

 

Chem. Rev. published two massive reviews on covalent ligands, each with more than 300 references. The first, by Tomonori Tamura, Masaharu Kawano, and Itaru Hamachi at Kyoto University, covers a wide range of topics, from covalent drugs, to peptide- and protein-based covalent inhibitors, to chemical biology labeling and target engagement strategies, to covalent bifunctional molecules such as PROTACs and radionucleotide-based molecules, and even covalent modification of DNA and RNA. The paper includes 68 figures, many reproduced from the original publications.

 

The second (open-access) Chem. Rev. paper, by Ku-Lung Hsu and colleagues at University of Texas at Austin, focuses on covalent ligands targeting protein residues other than cysteine, particularly lysine and tyrosine; we highlighted some of Hsu’s work recently. The paper also discusses naturally occurring molecules that bind to lysine, such as pyridoxal phosphate and aldose sugars.

 

Methods for finding covalent ligands are the focus on an open-access review in JACS Au by Mengke You, Hong Liu, and Chunpu Li at Shanghai Institute of Materia Medica. Specifically, they review disulfide tethering, activity-based protein profiling (ABPP), covalent DEL, phage and mRNA display, and sulfur(IV) fluoride exchange (SuFEx), with examples for each.

 

The last paper on this topic, in J. Med. Chem., offers a brief but important overview of all covalent FDA-approved small molecule drugs through 2023. Samuel Dalton and collaborators at Isomorphic Laboratories and Merck counted 128 covalent drugs, about 7% of all small molecule drugs. More than half are antibiotics, and more than 85% target serine or cysteine. Only 10% are reversible, but this number is rapidly increasing, with 11 of the 13 reversible covalent drugs approved since 2010. Importantly, the names, chemical structures, indication, target and target residue, warhead, and key references for all the drugs are provided in the supporting information.

 

Miscellaneous

Deconstruction of ligands to smaller fragments that are then “reconstructed” into new leads is a venerable approach in FBLD and the subject of an open-access perspective in J. Med. Chem. by J. Henry Blackwell, Iacovos Michaelidies, and Floriane Gibault at AstraZeneca. Multiple examples dating as far back as the late 1990s are provided, along with appropriate caveats about potential changes in fragment binding modes and protein conformations.

 

Finally, an open-access perspective in J. Med. Chem. by Dean Brown (Jnana Therapeutics) examines the 104 oral drugs approved from 2020 through 2024, including structures, dosing, pharmacokinetics, and safety. Roughly a third of these drugs are dosed more than once per day, and almost a quarter have a black box warning, while 42% have at least one contraindication. Dean warns that “overly prescriptive [development candidate] criteria may inadvertently stifle the development of innovative drugs,” and that it is difficult but important “to be the champion for a compound that others perceive as ‘un-drug like.’” The growing success of covalent drugs illustrates that some organizations are taking this to heart.

 

And that’s it for 2025. Thanks for reading and special thanks for commenting. And in 2026, may the best of us be filled with passionate intensity.

A sharp NMR trick for rapidly measuring affinities

As noted in our poll last year, ligand-detected NMR ranks among the most popular fragment-finding approaches. The various methods are able to detect even weak binders, so determining affinities is important to effectively prioritize hits. This, however, can be time-consuming. In a recent J. Am. Chem. Soc. paper, Ridvan Nepravishta, Dušan Uhrín, and collaborators at CRUK Scotland Institute, University of Edinburgh, and Universidad de Sevilla present a clever way to speed up the process.

 

Normally, NMR spectra of small molecules show multiple spectral lines, with each line corresponding to a different atom or atoms (typically protons). Indeed, depending on the details, the signal from a single proton might be split into multiple peaks. All these signals are great for understanding the details of individual atoms, but the more lines there are, the lower the signal to noise ratio. For maximum sensitivity it would be nice to combine all the lines from all the atoms in a given molecule into a single, intense singlet. This is exactly what the researchers have done.

 

The approach is called Sensitive, Homogeneous And Resolved PEaks in Real time, or SHARPER. For the NMR aficionados out there, “when placed before the acquisition of the NMR signal, a train of spin-echoes in the form of the Carr-Purcell-Meiboom-Gill (CPMG) pulse sequence suppresses evolution due to chemical shifts and J couplings…. All these attributes of the CPMG pulse sequence are maintained when the spin-echo train is employed during the acquisition of the NMR signal. However, this time, the outcome is not a regular spectrum, but under certain conditions, a single spectral line formed as a sum of Lorentzian lines of contributing spins.”

 

The researchers initially applied SHARPER to two commonly used ligand-detected methods: ¹H STD, which we wrote about here, and ¹H CPMG, which we wrote about here. The first test system was human serum albumin (HSA) binding to naproxen. Keeping protein concentration constant at 9 µM and varying ligand concentration gave similar K_D values (210-280 µM) for standard STD, STD SHARPER, and CPMG SHARPER (conventional CPMG failed due to insensitivity at lower ligand concentrations). These values are an order of magnitude higher than those reported using SPR and ITC (25 and 10 µM, respectively) because of the high protein and ligand concentrations needed for conventional NMR approaches; when the SHARPER experiments were rerun at 1 µM HSA, the K_D values were 39 µM. Several other HSA ligands also gave good agreement with the literature.

 

Next, the researchers applied STD SHARPER to the anti-cancer target fascin, which we wrote about in 2019. An examination of 11 ligands from that study gave good agreement with the published dissociation constants. Importantly, SHARPER was faster than conventional approaches, with 15 K_D determinations per day instead of four.

 

Not content with this four-fold improvement in throughput, the researchers developed a new experiment based on line broadening called ¹H LB SHARPER. This allows the determination of 48 dissociation constants per day, and the results for HSA and fascin agreed with the other methods.

 

One of the most time-consuming aspects of most NMR-based affinity measurements is preparing and analyzing samples at multiple ligand concentrations, so the researchers turned to machine learning to choose which ligand concentrations would be most informative and choose just two of them rather than the six or more commonly used. This worked too, thereby potentially increasing throughput to 144 dissociation constants per day.

 

The researchers suggest that SHARPER could also be applied to some of the other recent NMR techniques we’ve discussed, such as PEARLScreeen and photo-CIDNP. Although I always emphasize that I’m no NMR spectroscopist, this strikes me as a neat, practical approach. What do you think?

Fragment merging without crystallography for CGRP receptor antagonists

Migraines are the third leading cause of disability worldwide. Although the pathology is complex, blocking the interaction of calcitonin gene-related peptide (CGRP) with its receptor, thereby decreasing vasodilation, has proven successful in the clinic. However, some of the early small molecule antagonists were discontinued due to hepatotoxicity. In a recent J. Med. Chem. paper, Naohide Morita, Isao Azumaya, and collaborators at Kissei Pharmaceutical and Toho University describe a new class of inhibitors.

 

CGRP binds at the interface of a heterodimeric receptor comprised of the calcitonin receptor-like receptor (CLR) and receptor activity-modifying protein 1 (RAMP1). To find hits, the researchers screened a library of 2500 fragments (which could be up to 350 Da) at 500 µM against the extracellular CLR/RAMP1 domains using SPR. This yielded 565 hits, which were clustered based on similarity, and 250 were chosen for dose-response studies, leading to 38 confirmed hits. Competition studies with a known CGRP antagonist whittled this number down to just four, with compound 1 being chosen for further study due to ease of analog synthesis.

 

Compound 1 was confirmed as a binder using isothermal titration calorimetry (ITC). Unfortunately, co-crystallography with CLR/RAMP1 was unsuccessful, so the researchers turned to docking using information from known small molecule inhibitors. This work suggested that compound 1 binds to the CGRP receptor but does not interact with RAMP1, a conclusion further supported by mutagenesis studies.

 

To find fragments that bind RAMP1, the researchers performed a second fragment screen, again using SPR. This time the fragments were chosen from those in the first set that had not been tested in dose-response studies, supplemented with several hundred more selected based on structures of known CGRP antagonists. Of 784 fragments screened, 114 were taken into dose-response studies, leading to 8 hits. Compound 2 was the most potent, and mutagenesis studies suggested it interacted with RAMP1.

 

Crystallography of compound 2 was also unsuccessful, but docking, supported by NMR studies, suggested a possible binding mode. Compounds 1 and 2 were merged to yield compound 3, which had a satisfying 2000-fold improvement in potency compared to compound 1. Compound 3 also showed cell activity.

 

Compound 3 contains three stereocenters, so the researchers sought to simplify the molecule. They also needed to improve potency and metabolic stability. Multiparameter optimization ultimately led to compound 15, with picomolar(!) affinity for the receptor, subnanomolar activity in cells, and good pharmacokinetic properties. A standard model for migraine is inhibition of facial blood flow in marmosets, and compound 15 was active. The compound was also clean in tests for hepatotoxicity.

 

Although no further development of compound 15 is reported, this is a nice case study in fragment merging. As the researchers note, it is also one of just a handful of examples that succeeded in the absence of crystallographic data (we wrote about another one here). Hopefully this will further embolden researchers to pursue fragment merging and linking without direct structural information.

Targeting SARS-CoV-2 RNA – but not specifically

Last week we highlighted work suggesting that small molecule binding sites in RNA are most likely to be found in complex structures. A new open-access paper in Angew. Chem. Int. Ed. by Harald Schwalbe and collaborators at Goethe University Frankfurt and elsewhere provides both a case in point and an illustration of how difficult it is to target RNA.

 

The researchers had previously screened 15 RNAs from the SARS-CoV-2 virus, an effort we highlighted in 2021. In the new paper, the researchers focus on a portion of the frameshift element, which is important for directing viral replication from either of two partially overlapping open reading frames. The core of this RNA element is a roughly 69-nucleotide-long structure called a pseudoknot. Like most RNA sequences, this one can form multiple structures, including dimers, and the researchers used NMR, small-angle X-ray scattering (SAXS), and native gel electrophoresis to confirm that the construct was behaving as a homogenous monomer, consistent with three previously determined structures.

 

Based on some of the initial fragment hits, the researchers selected 50 similar molecules, of which only 14 were sufficiently soluble for screening. One of the more potent compounds, D05, initially showed promising activity in a ligand-detected NMR assay but turned out to be completely inactive when retested from a fresh stock. It turns out that D05 decomposes to compound 2, which was confirmed as active. Further modification led to compound 4, the most potent compound described. (Dissociation constants were determined by NMR, fluorescence, or both, and the two methods were in good agreement.)

Two-dimensional NMR with isotopically labeled RNA was used to try to determine the location of the binding site(s). Even with access to a 1.2 GHz magnet, the NMR peaks were severely overlapped, so the researchers used segmental isotopic labeling, in which just half of the RNA was labeled at a time. This exercise revealed potentially three different binding sites for compound 2.

 

The researchers also used two different computational approaches, Vina and RLdock, to predict binding sites, each of which could find one or two of the binding sites identified by NMR.

 

Several compounds were tested to see if they could block frameshifting in cell-lysates, and compound 2 showed 40% inhibition at 145 µM.

 

So far so good. But consistent with best practices, the researchers tested compounds 2 and 4 against phenylalanine tRNA. Unfortunately, the two ligands exhibited similar affinities to this control RNA as they did to the SARS-CoV-2 pseudoknot, despite the lack of sequence similarity. This suggests that these ligands bind to RNA nonspecifically. Perhaps this is not surprising given the three binding sites observed in a single 69-mer.

 

In the end, this is a thorough but sobering paper. Despite an impressive screening campaign with multiple biophysical methods, the best ligands seem to have modest affinity and low specificity. Drugging RNA still appears much more difficult than drugging proteins. But for either sort of target, this sort of careful work will be essential to find promising leads.

From fragment to macrocyclic Ras inhibitors

At the Drug Discovery Chemistry meeting last month chemist John Taylor described efforts against the oncology target RAS. This story was recently published in J. Med. Chem. by John, Charles Parry, and a team of some three dozen collaborators at CRUK Scotland Institute, Novartis, and Frederick National Laboratory for Cancer Research.

 

Practical Fragments has highlighted multiple Ras efforts, including the development and approval of sotorasib, which inhibits the G12C mutant of KRAS. Sotorasib binds in the so-called switch II region, next to the site where the nucleotides GDP and GTP bind. Before the discovery of this site, researchers had identified fragments that bind to a different site, switch I-II. 

 

Most of the ligands that bind to either site only inhibit the off-form of Ras proteins, in which the proteins are bound to GDP. One mechanism of resistance for cancer cells is to increase the amount of protein in the active, or GTP-bound state. Thus, the researchers focused on the oncogenic G12D mutant of KRAS bound to a GTP analog and screened it against 656 fragments using SPR. Ligand-detected NMR confirmed five of the hits, including compound 5.

 

Two dimensional ¹H-¹⁵N HSQC NMR revealed that compound 5 binds in the switch I-II pocket; merging this with a literature fragment generated compound 6. SAR studies led to compound 11, which was characterized crystallographically bound to the protein. The structure suggested trying to make a salt bridge with an aspartic acid residue, leading to compound 13, with sub-micromolar affinity for the inactive form of the protein. A crystal structure of a related compound suggested the possibility of macrocylization, and this turned out to be successful, with compound 21 being the most potent. (All values shown here are determined by NMR or SPR on the G12D KRAS mutant bound to either GDP or the GTP analog GMPPMP.)

 

A number of different macrocycles were made and tested, and all of them were more potent against the inactive than the active form of KRAS. Crystal structures suggested that a glutamic acid side chain adopts a conformation in the the GTP-bound form of KRAS that impedes ligand interactions.

 

Interestingly though, building off the molecules in another direction led to the opening of a small subpocket that had not previously been reported in the literature. Exploiting this “interswitch” region led to compound 36, with a nearly 10-fold preference for the active form of KRAS.

 

Most of the macrocycles in both series were able to block nucleotide exchange in a biochemical assay, meaning they could prevent the exchange of GDP for GTP. A few of the compounds were tested in cell-based assays and could block binding between RAF and multiple Ras isoforms, including two mutants of KRAS as well as wild-type KRAS, HRAS, and NRAS.

 

Unfortunately, and not surprisingly given their high polar surface areas, the compounds had low permeability, high efflux, and high clearance in vitro. Mouse studies on one compound confirmed these liabilities in vivo.

 

Although the compounds could not be advanced, this is still a nice fragment to lead story. The fact that a new pocket could be identified despite so much previous effort on this target is a good reminder that no matter how much you know, there is always room for surprises.

Solving protein-ligand NMR structures without isotopic labeling

Last week we highlighted a protein-detected NMR method that does not require expensive and sometimes difficult isotopic labeling of proteins. However, while that approach is able to provide affinity information, it does not provide structural information. A new (open-access) paper in J. Am. Chem. Soc. by Roland Riek, Julien Orts, and collaborators at the Institute for Molecular Physical Science and the University of Vienna tackles this challenge.

 

The approach builds on NMR Molecular Replacement (NMR²), which we last wrote about here. In NMR², brute force calculations obviate the need for assigning individual NMR peaks to specific protein residues, thereby sidestepping considerable up-front effort. Most of the new paper focuses on applying NMR² to ligand discovery for the oncogenic G12V mutant of KRAS, which I’ll briefly summarize.

 

The researchers start by screening the 890-membered DSI-poised fragment library (in pools of six, with each fragment at 0.6 mM) against KRAS using ligand-detected STD NMR. This produced 133 hits, which were then retested at 1 mM each using [¹⁵N,¹H]-HSQC two-dimensional protein-observed NMR, invalidating about 30% of them. Dose-response titrations were performed on the top 13 hits; all of them were found to be weak binders, with at best low millimolar affinity. NMR² was then used to determine protein-ligand structures for some of these hits. That information guided the design of additional ligands, which had slightly higher affinities.

 

This thorough description of the NMR² workflow should be useful if you’re trying to do this at home. But what really caught my eye was a bit at the very end of the paper describing a new relaxation-filtered NOESY pulse sequence. Specifically, “an inversion recovery pulse block serves as a T₁ filter, followed by a perfect echo sequence and a CPMG without J-modulation, as a T₂ filter.” In essence, the experiment takes advantage of the fact that proteins relax more rapidly than small molecules, so NMR peaks coming from the protein are filtered out. But NMR peaks from protons in the ligand that are in close proximity to protons on methyl groups of the protein are observed, and the intensity of these peaks correlates with the distance between ligand and protein protons. Feeding these distance constraints into NMR² generates a three-dimensional structural model. The researchers compare models generated using NMR² on unlabeled KRAS to those generated using NMR² on labeled KRAS and show that they are roughly similar.

 

This is a neat approach, and it will be interesting to see whether it catches on. According to our poll last year ligand-detected NMR has fallen to fourth place among fragment-finding methods, and protein-detected NMR is in seventh place. Perhaps approaches like this and that described last week will usher in a new era of NMR for FBLD.

Protein-detected NMR without isotopic labeling

Protein-detected NMR was the first practical approach for finding fragments, and as we noted last week some still consider it the gold standard. As commonly practiced, it requires isotopically labeled protein: at a minimum ¹⁵N and sometimes ¹³C and even deuterium. Making large amounts of labeled protein can be both expensive and difficult. A new ACS Med. Chem. Lett. paper by Andrew Petros and collaborators at AbbVie describes a new approach that avoids this requirement. The method, called 1D-ECHOS, combines two previously described techniques.

 

The first technique addresses the fact that fragment screens typically use much higher ligand concentrations than protein concentrations, and thus the proton signals coming from the ligand can overwhelm those coming from the protein. “1D-diffusion filtered NMR” essentially removes signals coming from small molecules to focus on the protein.

 

When a ligand binds to a protein, the chemical shifts of nearby residues on the protein change, and these peak shifts are most easily observed in two-dimensional (2D) NMR spectra, where each dimension typically corresponds to the signal from a different nucleus, such as ¹H or ¹⁵N. Without isotopic labeling, only protons can be observed, and only in one dimension, so the resulting spectra look like mountain ranges, with overlapping peaks. To facilitate comparison between the two samples (protein with or without ligand), the researchers use a second technique, called Easy Comparison of Higher Order Structure (ECHOS). This allows differences to be expressed as a single “R-score”, where larger numbers indicate more deviation between the two spectra.

 

So, how well does it work? The researchers started by examining a set of 13 hits from a DNA-encoded library against an unnamed 36 kDa protein. Four of these had previously been confirmed to bind using 2D-NMR, and all of these had positive R-scores, while the non-binders had R-scores close to zero. The approach was also faster than a standard HSQC with labeled protein, requiring just 10 minutes rather than 35 minutes.

 

As the researchers note, DEL hits are typically larger and more potent than fragment hits, so they next turned to 11 confirmed MCL-1 binders from a fragment screen we wrote about here. These were tested at 62.5 µM, and the R-scores roughly correlated with their previously measured affinities, which ranged from 20 µM to 500 µM.

 

To try to get more quantitative information, the researchers performed dose-response experiments and plotted the R-score as a function of ligand concentration. This allowed them to extract dissociation constants, which were in good agreement with the known values. For ligands containing tert-butyl groups the 1D-diffusion filter was not fully capable of masking the signal, but this peak could be manually removed from the analysis. The researchers also applied the approach to two additional targets, BRD4 BDII and TNFα, and found good agreement with known ligand affinities. Of course, unlike 2D NMR, 1D-ECHOS does not provide information on where the fragments bind.

 

1D-ECHOS appears to be a practical approach for validating and characterizing fragment binding, but I’m no NMR spectroscopist, so I’ll be interested to hear what experts think.

Fragments vs eIF4E: a chemical probe

Cancer cells are known for growing and multiplying quickly, and to do so they need to produce large amounts of protein. The rate determining step in protein translation happens early, when ribosomes are recruited to the 5’-end of mRNA by the eukaryotic initiation factor 4F (eIF4F) complex. This complex has long been a target for drug discovery, and in a recent open-access Nat. Comm. paper Paul Clarke, Andrew Woodhead, Caroline Richardson, and collaborators at Institute of Cancer Research and Astex describe a chemical probe. (Andrew spoke about this program last year at FBLD 2024.)

 

The eIF4F complex includes three core proteins, confusingly named eIF4E, eIF4G, and eIF4A. eIF4E binds to the 5’cap of mRNA and recruits eIF4G. Blocking the interaction of eIF4E either with mRNA or eIF4G could in principle shut down protein synthesis, but intensive efforts by multiple groups have struggled: the mRNA binding site is very polar, and disrupting protein-protein interactions is tough. Thus, the researchers took a fragment approach.

 

Developing a form of eIF4E suitable for fragment screening was itself a challenge because the protein mostly exists as part of a complex in cells and the native monomer is unstable. After making more than two dozen different constructs, the researchers developed a stable, soluble form that could be crystallized. This construct was screened against a library of 1371 fragments in pools of four, each at 500 µM, using CPMG NMR followed by crystallography, leading to 50 hits. A few bound at the mRNA cap-binding site but most bound to a previously unreported “site 2,” which is near where eIF4G binds.

 

One of these, compound 1, has a reasonable ligand efficiency despite its low affinity as assessed by ITC. The phenol appeared to be making no interactions and so was removed. Adding a fluorine usefully enforced the twisted biaryl conformation and filled a small dimple; fragment growing then led to mid micromolar compound 3. Further growing to pick up additional lipophilic and polar contacts eventually led to compound 4, with low nanomolar affinity. Understanding the importance of negative controls for chemical probes, the researchers also switched the stereochemistry at the benzylic carbon to produce compound 5, which has >30-fold lower affinity for eIF4E. 

 

Crystallography revealed that binding of compound 4 to eIF4E causes conformational changes that should impair binding of the protein to eIF4G. Experiments in cell lysates bore out this hypothesis. Moreover, compound 4 also inhibited protein translation in cell lysates at low micromolar concentrations, while compound 5 did not.

 

Unfortunately, these observations did not extend to intact cells. A cellular thermal shift assay (CETSA) demonstrated that compound 4 did stabilize eIF4E in cells with an EC₅₀ = 2 µM, consistent with binding. But it was much less effective at blocking the interaction with eIF4G in cells, even at high concentrations, and showed no inhibition of protein translation.

 

To understand why, the researchers conducted a series of targeted protein degradation and genetic rescue experiments that are beyond the scope of this blog post. The upshot is that eIF4G binds to several regions of eIF4E, and that while compound 4 disrupts binding to the “non-canonical binding site”, it does not block binding to the “canonical binding site,” and thereby does not block overall complex formation. Why there should be a difference between intact cells and cell lysates is not obvious to me, but perhaps the more dilute conditions of cell lysates play a role, as seen for a paper we discussed last year.

 

One interesting feature of this story is that the initial fragment makes no polar interactions with the protein; all of the polar interactions in compound 4 were added during optimization. This is quite the opposite of ASTX660, where all the polar interactions in the final clinical compound came from the initial fragment. Indeed, a 2021 analysis of fragment to lead successes found that fewer than one in ten retained no polar interaction from the initial fragment.

 

This paper also illustrates the gap that can occur between research and publication; a couple of the authors listed as affiliated with Astex left in 2017. But better late than never, and this study nicely integrates fragment-based lead discovery with elegant biology. Compound 4 should be a useful tool for further exploring the nuances of eIF4E.

Fishing for pearls more efficiently with a new NMR method

NMR is the most venerable approach for finding fragments, and ligand-detected NMR is still among the more popular methods. But the amount of protein required for a full fragment library screen can be a limitation, particularly for more challenging targets. A new paper in Angew. Chem. Int. Ed. by Alvar Gossert and collaborators at ETH Zürich, Bruker, and Karlsruhe Institute of Technology provides a new, less protein-intensive approach.

 

I’ll preface the next paragraph by admitting that not only am I no spectroscopist, I don’t even play one on TV. So, spectroscopy-savvy readers, please feel free to provide more details in the comments, especially if I get something wrong. For fellow non-spectroscopists, the takeaway is that clever NMR tricks increase sensitivity.

 

PEARLScreen, short for Perfect Echo for Advanced Relaxation-based Ligand Screen, is related to the classic Carr-Purcell-Meiboom-Gill (CPMG, or T_1ρ) method, which we wrote about most recently here. As in that older method, PEARLScreen relies on the decrease in signal intensity of a ligand that binds to a protein. This is due to slower tumbling of the bound ligand, resulting in faster relaxation of excited protons (see here). Lengthening the time between excitation and measurement should in theory boost contrast between bound and free ligands, but various technical challenges impede this in practice. PEARLScreen overcomes these challenges using “a perfect echo pulse train with water suppression by excitation sculpting.” In addition to lengthening the relaxation delay, PEARLScreen also allows exchange broadening to occur between the ligand and protein, further increasing sensitivity.

 

The researchers simulated multiple conditions to optimize various parameters, and then experimentally tested PEARLScreen on four different proteins with three types of NMR instruments, starting with a standard high-end 600 MHz.

 

The first protein-ligand pair was trypsin binding to a known benzamidine fragment. This interaction was detectable using a standard T_1ρ experiment with 200 µM ligand and 20 µM protein. Using PEARLScreen, the researchers could reduce the protein concentration to 1 µM while maintaining similar signal to noise .

 

Next, they screened 94 fragments in pools of 8 against three different proteins: PPAT, Abl, and FKBP. In all cases PEARLScreen was more sensitive than T_1ρ, allowing screening at 2.5 µM rather than 20 µM protein. PEARLScreen was also more sensitive than the two other most common ligand-detected NMR methods, STD and WaterLOGSY.

 

We wrote recently about benchtop NMR, and the researchers found that PEARLScreen was also more sensitive than a T_1ρ experiment on an 80 MHz instrument, though the difference was not as dramatic as on the 600 MHz machine. On the other hand, on a 1.2 GHz instrument PEARLScreen was so sensitive that the researchers could screen mixtures of 16 fragments with just 1 µM protein.

 

This is a neat paper, which confidently concludes that “due to the superior sensitivity of the PEARLScreen compared to all established screening experiments at standard fields, we expect it to become the standard experiment for ¹H-detected ligand screening.” We look forward to hearing how it performs for others.