Filtering fluorescent frauds from fragments for PTP1B

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

 

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

 

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

 

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

 

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

Differential scanning fluorimetry (DSF) for covalent ligands

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

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

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

 

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

 

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

 

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

 

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

 

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

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

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

 

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

 

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

 

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

 

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

 

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

 

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

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

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

 

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

 

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

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

 

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

 

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

Fragments in the clinic: VVD-214

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

 

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

 

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

 

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

 

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

 

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

 

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

From noncovalent fragments to covalent WRN inhibitors

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

 

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

 

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

 

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

 

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

 

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

 

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

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.

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.

Ligand reactivity efficiency (LRE)

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

A new tool for covalent ligands: kinact/KI made easy with dDRTC

As covalent drug discovery becomes increasingly common, researchers are becoming more rigorous in how they characterize their molecules. The simple IC₅₀ values used for reversible inhibitors are meaningless for irreversible ligands unless the incubation times are also disclosed. And as molecules become more potent during the course of optimization, the incubation time may need to be shortened. An early hit might require treatment overnight to give 50% protein modification, while a potent lead might completely modify the protein in seconds. How do you quantitatively compare these?

 

The most rigorous parameter to characterize irreversible ligands is k_inact/K_I, sometimes called covalent efficiency, which we recently discussed here and here. Unfortunately, determining k_inact/K_I is a pain: it requires running multiple dose-response studies at multiple time points, and is thus typically only done for key compounds. In a new (open-access) Nat. Commun. paper, Robert Everley and colleagues at Frontier Medicines (including yours truly) provide a shortcut.

 

The new method relies on the fact that, especially for low-affinity fragments, much of the data collected in a conventional dose-response time course (DRTC) is redundant, providing little additional value. For example, if a compound at one concentration gives virtually no modification after 8 hours, it also won’t modify after 1 hour. The trick is to collect just the most informative data in a “diagonal” dose-response time course, or dDRTC.

 

I won’t go into the mathematics and full implementation details since the paper is open-access, but suffice it to say that dDRTC lowers the number of required data points by a factor of eight, thus saving both time and reagents – including precious protein.

 

The paper appropriately notes limitations, such as the fact that for compounds with better affinities (K_I < 50 µM), the values derived from dDRTC can underestimate the true k_inact/K_I. However, this situation is uncommon for fragments, and indeed the potencies for even some clinical compounds such as sotorasib and VVD-133214 are largely driven by (specific) k_inact rather than K_I. The paper shows good agreement for k_inact/K_I values determined using dDRTC with those determined using the conventional approach for compounds having k_inact/K_I from 1 to 2000 M^-1s^-1.

 

Perhaps most relevant for this blog, dDRTC is a practical solution for collecting important data. The next time you’re running a covalent program, give it a try! 

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.

Surprise – a covalent histidine-targeting PDE3B inhibitor

Earlier this year I wrote about archiving crystallographic fragment data, and indeed a meeting is planned for early next year to establish guidelines. A new paper in J. Med. Chem. by Samuel Eaton and David Christianson at University of Pennsylvania illustrates why this is important.

 

The story starts with a paper published in 2024, also in J. Med. Chem., by Ann Rowley, Gang Yao, and collaborators at GSK and 23andMe. They were interested in finding inhibitors of PDE3B, a cyclic nucleotide phosphodiesterase that has been implicated in metabolic disease. However, this enzyme has a closely related counterpart significantly expressed in cardiac tissue: PDE3A, with 95% amino acid identity near the active site. So the researchers sought an inhibitor highly selective for PDE3B over PDE3A.

 

A DNA-encoded library (DEL) screen of 1.9 trillion(!) molecules was screened against both PDE3B and PDE3A. Hits were resynthesized without the DNA and tested in activity assays, leading to several chemical series, only one of which was selective for PDE3B. A key feature of this series was a boronic acid moiety, which was essential for activity. Optimization led to compounds such as GSK4394835A, with high nanomolar activity against PDE3B and >20-fold selectivity against PDE3A. The GSK researchers deposited a crystal structure of this molecule in the protein data bank (PDB), along with the structure factor amplitudes. It showed the boronic acid making non-covalent interactions with side-chain residues as well as the catalytic magnesium atoms and water molecules.

 

Further optimization at GSK led to compounds with as much as 300-fold selectivity for PDE3B, but like GSK4394835A, these were only high nanomolar inhibitors. The researchers could further improve potency, but this came at the expense of selectivity. Cell activity was modest at best, and the researchers noted that “the boronic acid is, in general, a challenge for development of an orally bioavailable drug.”

 

This is where the University of Pennsylvania researchers take up the story. As their paper points out, several drugs do contain boron, most notably bortezomib, which forms a covalent adduct with a threonine in the proteasome. When Eaton and Christianson took a closer look at the PDB entry showing GSK4394835A bound to PDE3B, they “noticed unusual features such as extra density around the boron atom of GSK4394835A, steric clashes between the boronic acid moiety and H737, and aberrant refinement statistics… from ideal bond lengths.” Upon re-refinement, they found that the boronic acid in fact makes a covalent bond with histidine 737. The structure explains why the boronic acid moiety was essential for activity, and the new paper suggest that other covalent warheads could potentially be used in place of the boronic acid. (Eaton and Christianson write that they contacted the GSK researchers in February of 2024, but it is not clear whether they heard back.)

 

This is a nice correction of the literature and a reminder not to take crystal structures at face value. The beauty of the PDB is that, with the experimental data deposited, the new researchers were enabled to re-refine the data even without input from the original authors.

 

As we’ve previously discussed, this example is not the only misleading crystal structure in the PDB. Many fragment structures have lower occupancy and more ambiguous electron density and would be even more prone to misinterpretation. As the community moves to establish guidelines for depositing fragment structures, it will be important to provide access to the raw data to facilitate this type of reanalysis.

Twentieth-Third Annual Discovery on Target Meeting

The CHI Discovery on Target (DoT) meeting was held last week in Boston. More than 850 people from 24 countries attended, 75% from industry. As usual I’ll just touch on some broad themes.

 

Covalent approaches

Covalent approaches were prominent throughout the conference. One of the very first talks was by Stefan Harry (Harvard/MGH), who described screening 416 cancer cell lines with three reactive “scout probes,” identifying some 6000 cysteine residues that could be covalently liganded. There are some interesting cell and context-dependent differences, and all the data are publicly available and easily searchable through a free online portal called DrugMap. He is now profiling a library of dual-electrophile-containing compounds to identify molecular glues.

 

Knowing which cysteines can be targeted is the first step for covalent drug discovery, and Sherry Ke Li described how she and colleagues at Genentech go about finding ligands. They’ve experimentally determined the reactivity of more than 6400 compounds against free cysteine and used this to train a machine-learning model to predict chemical (as opposed to specific) reactivity. Mass spectrometry (MS) using isolated proteins is the workhorse screening approach, but Sherry also described using variable temperature surface plasmon resonance (SPR) to dissect the individual components of k_inact/K_I.

 

AstraZeneca has also been doing considerable covalent screening, and Hua Xu briefly described the BFL1 story we wrote about here. In addition to pure proteins, they are now also starting to screen their covalent library in cells. Hua also presented earlier work from Pfizer on the discovery of the covalent kinase inhibitor ritlecitinib, which started with a noncovalent binder. Proteomic studies revealed that in addition to the intended target JAK3, it hits other TEC-family kinases too.

 

Adding a covalent warhead to a reversible binder is also the approach taken by MOMA Therapeutics in the discovery of their clinical WRN inhibitor MOMA-341, as presented by Momar Toure. They ended up targeting the same cysteine as Vividion (see here), though the binding mode is somewhat different. MOMA is also pursuing covalent fragment screening using intact protein MS, and Brian Sosa-Alvarado described how they were able to identify nanomolar inhibitors of RAD54L within six months of starting the program, aided by DNA-encoded libraries (DEL).

 

Not everyone is pursuing cysteine: Ken Hsu described how he and his team at University of Texas Austin are using sulfur-triazole exchange chemistry (SuTEx) to target tyrosine residues across the proteome. He noted that although cysteine could react with this warhead, the resulting thiosulfonate would be unstable. This is true in general, but I wonder if, just like the reversible cyanoacrylamide warheads we wrote about more than a decade ago, they could be stabilized within folded proteins.

 

Noncovalent approaches

Covalency is not the only game in town, as exemplified in a talk by Emma Rivers on “integrated hit discovery” at AstraZeneca. I was tickled that she grouped FBLD with HTS as “traditional” approaches, onto which they’ve added DEL and peptide libraries. Importantly, they’re focused on generating and capturing as much high-quality data as possible to enable machine learning – a topic we’ll touch on more below.

 

Nor are proteins the only target; there was a whole track on RNA- and DNA-targeting small molecules, where Benjamin Brigham described the plate-based equilibrium dialysis-based approach taken at Atavistik to screen metabolites and metabolite-like molecules. This led to two fragment-sized hits against RNA encoding SERPINA1, and although the affinities are modest, they do inhibit translation in a cell-free system.

 

At FBLD 2018 Astex presented the first cryo-EM structure of a fragment-protein complex, noting that throughput was an issue. The company has leaned into that challenge and now has three microscopes, including a top-of-the-line Krios, with another on the way. Miguel Zamora-Porras described how they have now solved hundreds of structures. Their Krios can collect data on two compounds per day, and the full cycle time from protein-ligand preparation to structure is about a week. Miguel described how structures of ligands bound to the ion channel protein TRPML1 helped reveal why some were agonists and others antagonists.

 

Data and its discontents

On the subject of structures, Steve Burley (Rutgers) gave an eloquent history and defense of the “RCSB Protein Data Bank: an open access research resource that benefits all humanity.” From its humble beginnings with just seven structures in 1971, the PDB now contains more than 240,000. And these are not just of scientific interest: all 88 of the new molecular entities the FDA approved for oncology between 2010 and 2023 had PDB structures that informed the biology or druggability, and 75% of the efforts involved structure-based design. Steve also mentioned that the question of where and how to store large-scale crystallographic data will be discussed in a meeting sometime in the spring of next year. Finally, Steve is hoping to retire from his position as Director of the RCSB PDB, so if you’re looking to make an impact, please apply.

 

The dramatic advances in protein structure prediction exemplified by AlphaFold would not have been possible without the PDB, but unfortunately the same high-quality information on protein-ligand binding modes and affinities is not available, as noted by Woody Sherman of Psivant. To illustrate the importance of training data, Woody asked ChatGPT to produce a picture of an analog clock set to 6:32. The result? A clock with three hands, one at 10, one at 2, and one at 6, because most images of clocks are set to 10:10.

 

Woody asked whether machine-learning-based docking can extrapolate or just interpolate. Although impressive results have been reported for some protein-ligand complexes, it turns out that there are often similar ligands in the training data. For truly novel ligands, the predictions tend to fall flat. Similarly, allosteric ligands are often (mis)placed into an orthosteric site – just because the model has been trained that that’s where ligands should go. Indeed, although Psivant is heavily invested in computational approaches, Woody mentioned that they often use “wet” approaches for finding initial chemical matter.

 

On the subject of dubious data, Al Edwards (University of Toronto) noted that a third of all immunofluorescence images in the literature use antibodies that give signals in knockout cells. And as we wrote just last month, many reported small molecule “probes” are just as bad. Al is CEO of the Structural Genomics Consortium, whose ambitious Target 2035 aims to find a pharmacological probe for every target in the human genome. As a starter, they’re aiming for 2000 probes in the next five years. They’re using affinity-selected mass spectrometry (ASMS), screening pools of 500 compounds and 8 proteins at a time, and are getting micromolar hits against about 30% of targets. They’re accepting protein submissions, so if you’re looking for starting points against your favorite protein contact them.

 

I’ll end here, but please leave comments. And mark your calendar for Sep. 28 to Oct. 1 next year, when DoT returns to Boston.