From fragment linking to covalent RAD52 inhibitor

Targeting proteins involved in DNA repair is a promising approach against cancer. However, finding effective small molecule inhibitors can be difficult because the protein-DNA interface tends to be very polar, with few classically ligandable pockets. In a new J. Am. Chem. Soc. paper, Nicolas Bocquet, Olivier Querolle, Ulrich Lücking, and collaborators at FoRx Therapeutics, WuXi AppTec, Diamond Light Source, University of Bern, Sandexis, and Weizmann Institute of Science provide a proof of concept.

 

The researchers were interested in RAD52, which mediates several DNA repair functions. The N-terminus of the protein forms an oligomeric ring structure, and each subunit contains a solvent-exposed groove that binds to single-stranded DNA. Since DNA is negatively charged, the researchers conducted a crystallographic screen against a library of 320 carboxylic acid containing fragments. This identified three related molecules, of which compound 3 had the highest occupancy. Because the compounds bind at the DNA-binding interface, the researchers developed a fluorescence polarization (FP) DNA-displacement assay and found that the fragments disrupt DNA binding at high micromolar concentrations.

 

Interestingly, the crystal structure revealed two copies of compound 3 bound to each subunit of RAD52: each positioned where a single nucleotide would normally bind, with the carboxylic acids making similar contacts as the phosphates. In other words, the fragments mimic two consecutive bases of a single-stranded oligonucleotide. Naturally, this suggested a linking approach.

Linking two close analogs of compound 3 led to compound 5, with a satisfying two orders of magnitude improvement in activity in the FP assay, which the researchers suggest is largely due to lowering the entropic penalty. Linking directly off the thiazole moieties produced similar results, and a macrocycle had slightly better activity, though not so much better as to justify the synthetic hassle. A crystal structure of compound 5 revealed a small hydrophobic cleft, and this was exploited with a phenyl moiety in compound 11, a high nanomolar inhibitor in the FP assay.

 

A crystal structure of compound 11 bound to RAD52 confirmed that the compound binds as designed. The structure also revealed a cysteine residue (C64) in close proximity to the phenyl moiety. Adding an acrylamide warhead yielded a covalent inhibitor, and further optimization ultimately led to FORX-864, which has low nanomolar activity in the FP assay after 4 hours. The researchers appropriately measured the k_inact/K_I, which came in at a respectable 230,000 M^-1s^-1, better than some covalent clinical compounds. Removing the warhead led to a dramatic loss in potency (IC₅₀ = 27 µM in the FP assay).

 

In a cellular assay to measure single-stranded annealing (SSA), FORX-864 showed roughly 1 micromolar activity, while a compound lacking the warhead was inactive. FORX-864 was also inactive in a cell line in which RAD52 had been knocked out, suggesting on-target activity. The researchers profiled the reactivity of FORX-864 in an activity-based proteomic profiling (ABPP) assay and found that it was reasonably selective. Intravenous dosing in mice showed encouraging pharmacokinetic properties, though with two carboxylic acid moieties oral dosing might be challenging.

 

Overall this is a nice example of liganding a difficult target. Given that FORX-864 mimics a dinucleotide, it may bind to other DNA- or RNA-binding proteins. However, covalent binding is required for high potency, and it is unlikely these other proteins will have cysteine residues in the appropriate position, which should help with selectivity. FORX-864 certainly appears to be the best chemical probe for RAD52 to date.

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.’”

(Not) getting misled by crystal structures part 6: low ligand occupancies

It’s been a while since our last “getting misled by crystal structures” post. That one described unrecognized conformational heterogeneity of ligands. A more basic issue is ligand occupancy. It’s normally assumed that every protein in the crystal lattice has a bound ligand. A new paper in Structure by Timothy Stachowski and Marcus Fischer at St. Jude Children’s Research Hospital reveals that this is not always the case.

 

The researchers selected roughly 10,000 protein-ligand structures from the Protein Data Bank (PDB) and did a simple re-refinement of the ligand occupancies and B-factors (measures of conformational heterogeneity and modeling errors). 10% of the structures already presumed ligand occupancies at or below 0.9, but re-refinement saw the fraction jump to 35%, more than three-fold higher. There were no overall differences between covalent and non-covalent ligands, but 37% of fragments (defined as having MW <300 Da) saw decreased occupancy in re-refinement compared to just 22% of larger ligands. A few structures even saw occupancy drop completely. The authors wrote that “manual reviewing these revealed that ligands were built into spurious electron-density.”

 

Crystallographers use several metrics to assess the quality of their structures. In addition to B-factors, unique to each residue or atom, real-space correlation coefficients (RSCC) and real-space R values are commonly used, and the researchers compared these parameters before and after re-refinement. In many cases the metrics improved with decreased occupancy, but not for all metrics, and not always meaningfully. This means that standard assessments do not always flag partially occupied ligands.

 

OK, so a ligand binds at only 80% occupancy rather than the 100% assumed: does this matter? The researchers describe three categories where the answer may be yes. In the first, correcting ligand occupancy reveals alternative conformations of protein side chains, which could be informative for understanding the mechanism of binding. In the second, correcting ligand occupancy can reveal water molecules that interact with the ligand and/or protein. As we noted more than a decade ago, water is an essential player in protein-ligand interactions, and a single water molecule can make the difference between a binder and a non-binder. Finally, correcting ligand occupancy can reveal alternative binding modes for the ligand, and even ligands binding to other sites.

 

Importantly, this analysis was done on individually refined structures in the PDB, and it seems likely that the issues would be even more severe for structures batch-refined in high-throughput crystallographic fragment screens. As we wrote last year, the community needs to figure out how to deal with the increasing number of these structures.

 

The fact that roughly a third of PDB structures have less than 100% ligand occupancy has implications for training AI models. It also has implications for individual targets. As the researchers note, “non-crystallographers who rely on the PDB often assume that deposition itself is an implicit stamp of approval. Structural biologists, however, know that this is not always the case.” Before using a structure, it would be wise to re-refine it yourself.

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.

Malicious metals muddy fragment-to-lead optimization

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

 

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

 

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

 

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

 

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

 

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

 

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

xSAR: Crystallographic SAR from crude reactions

Last year we highlighted an example of crystallographic screening of crude reaction mixtures to find inhibitors against the oncology target PHIP(2). Of 957 molecules tested, 22 showed crystallographic binding in two different orientations: 19 in a “lateral” pose and 3 in a “diving” pose. In a new open-access Chem. Sci. article, Philip Biggin and collaborators at Diamond Light Source and University of Oxford try to extract information from both the binders and the non-binders using crystallographic structure-activity relationships, or xSAR.

 

Chemists often think about SAR in qualitative terms: a methyl group here improves affinity, a chlorine atom there reduces it. In xSAR, the researchers sought to take a more quantitative approach. They converted each molecule into “Morgan fingerprints,” a set of more than 2048 binary bits describing structural features such as atom type, hybridization, and connectivity to other atoms within a certain distance. Some bits were found in all binding compounds, and these were referred to as conserved binding bits (CBB), while conserved nonbinding bits (CNB) were found only in non-binding compounds. These bits were then used to calculate Positive and Negative Binding Scores (PBS and NBS); a compound with a PBS of 1 contains all the CBB. Since there were two separate binding modes, the researchers calculated PBS and NBS values for both lateral and diving poses individually as well as for all binders.

 

As the researchers note, false negatives are a likely issue in crude reaction screening for a variety of reasons. To hunt for these, the PBS and NBS values were calculated for all 957 molecules previoulsy tested. A set of 97 pure compounds having mostly high scores were acquired and tested crystallographically, yielding an additional 23 lateral binders and 3 diving binders, more than doubling the initial yield. PBS was particularly informative in this retrospective exercise to recover false negatives, outperforming both NBS as well as other methods such as Tanimoto similarity scores.

 

The researchers also used PBS and NBS scores to search prospectively for new binders in a virtual set of more than 1.7 billion compounds in the Enamine REAL database. After filtering for high PBS/NBS scoring compounds followed by docking, 93 compounds were acquired and tested crystallographically. Interestingly, this yielded a relatively low hit rate of 9 binders, 6 in the lateral pose and three in somewhat different poses. None of the new compounds bound in the diving pose, which the researchers suggest may be due to the small sample size used to calculate PBS and NBS for this binding mode.

 

The 93 new compounds were also tested for binding using grated-coupled interferometry (GCI), and 13 showed measurable affinity, with most better than 50 µM. Two even showed single-digit micromolar affinity, more than an order of magnitude better than the best compound from the screen we discussed last year, and with better ligand efficiencies too. Surprisingly, these two compounds were not hits in the crystallographic screen.

 

This is an interesting paper with a couple important lessons. First, despite the fact that affinity was not used in calculating PBS and NBS, these metrics were nonetheless useful for identifying molecules with better affinity than those in the original training set, arguing for their utility. But perhaps just as importantly, the molecules with the best affinity were missed by crystallographic screening. If anything, this observation only strengthens my conclusion last year that while “there is a strong case for using crystallography first for finding fragments, I am not yet convinced the same applies for optimizing fragments.”

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.

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.

Searching monstrously large chemical space with FrankenROCS

Back in 2023 we highlighted a computational fragment linking/merging approach which was used to find high nanomolar inhibitors of the SARS-CoV-2 macrodomain (Mac1), a COVID-19 target. However, those molecules contained carboxylic acids, often associated with poor cell permeability. In a new open-access Sci. Adv. paper, James Fraser and collaborators at UCSF, Relay Therapeutics, Enamine, and Chemspace describe a related approach to find new, non-charged inhibitors.

 

The new approach, called FrankenROCS, “takes pairs of fragments as input to query a database using the rapid overlay of chemical structure (ROCS) method of comparing 3D shape and pharmacophore distribution;” the goal is to find larger molecules that most closely resemble the initial fragment pairs. As with the previous publication, the team started with more than 200 crystallographic fragment hits published in 2021. A set of 7,181 pairs of adjacently-bound fragments were searched against 2.1 million compounds commercially available from Enamine. The top 1000 were inspected, and 39 were purchased and soaked into crystals of Mac1. This led to 10 successful structures, of which AVI-313 did not contain a carboxylic acid. This molecule had weak but measurable activity in an HTRF competition assay.

 

Two million compounds is a lot but pales in comparison to Enamine’s “make-on-demand” REAL space, which at the time this research was done consisted of more than 22 billion molecules. The REAL space molecules are constructed from 960,398 building blocks that can be combined using 143 reactions. We previously described an approach called V-SYNTHES to screen Enamine’s REAL space. FrankenROCS takes a different active-learning approach called Thompson Sampling, which dates back nearly a century.

 

Imagine two sets of 1000 building blocks, R1 and R2, which could be coupled to generate 1,000,000 molecules. Rather than searching all possibilities, each R1 building block is linked to three random R2 building blocks, and each R2 building block is linked to three random R1 building blocks. These are virtually screened, and the R1 or R2 building blocks from those with the highest scoring compounds are used for further iterations. In theory, after tens of thousands of iterations, the best compounds will have been identified.

 

The researchers fed 97 fragment pairs from the 2021 paper into Thompson Sampling FrankenROCS to find molecules that would best overlay with the fragment pairs.  Ultimately 32 compounds were purchased, six of which were successfully crystallized with Mac1. Unfortunately, the most potent was a weaker inhibitor than AVI-313 and contained a carboxylic acid. The researchers speculate that the inability to find better molecules in larger chemical space may have stemmed from limitations of the scoring function, a problem we’ve previously discussed.

 

The researchers returned to focus on AVI 313, making substitutions at multiple positions, ultimately synthesizing 148 compounds, 121 of which could be characterized crystallographically. Importantly, several compounds had low micromolar activity, even without a carboxylic acid. The crystal structures show the binding site to be somewhat flexible, as evidenced by side chain and main chain movements to accommodate some of the binders.

This is a nice, thorough investigation, and the 137 protein-compound crystal structures deposited into the protein data bank provide useful training data for next-generation computational approaches. Moreover, the fact that immeasurably weak fragments can be advanced to low micromolar, ligand-efficient hits is yet another reason for the research community to figure out how to make crystallographic fragment screening data more widely available, as we exhorted here.

Checking halogen bonds

Halogen bonds (or X-bonds) are one of the less appreciated protein-ligand interactions. As we discussed in 2022, the polarized nature of a carbon-halogen bond creates a partially-positively charged “σ-hold” at the bit of the halogen furthest from the carbon, and this can make favorable interactions with lone pairs on oxygen or sulfur atoms (or nitrogen, but in most proteins this is limited to histidine residues and is rare.) Halogens can also interact with aromatic π-systems such as the side chains of phenylalanine, tryptophan, histidine, and tryptophan. Since many fragments contain halogen atoms by design, halogen bonds may occur frequently. But how do you decide whether “a halogen in proximity of a possible acceptor” actually contributes to binding? In a new (open-access) paper in Protein Science, Ida de Vries, Robbie Joosten, and colleagues at Oncode Institute and The Netherlands Cancer Institute provide a new metric.

 

The researchers examined structures of halogen-containing ligands bound to proteins in PDB-REDO, a database of carefully vetted and refined structures from the Protein Data Bank. They only included structures solved to better than 2.5 Å resolution and omitted structures where halogens had high B-factors, which may be the result of radiation damage. This led to 8423 structures in which a halogen possibly interacted with an oxygen or sulfur atom and 8096 potential halogen-π interactions, which were analyzed in detail.

 

A halogen bond to an oxygen or sulfur atom can be described by the interatom distance and two angles: θ1 (carbon-halogen-oxygen/sulfur) and θ2 (halogen-oxygen/sulfur-carbon). Halogen-π-system bonds can be defined by distance to the centroid of the π-system and θ1, the carbon-halogen-centroid angle. (The paper has a nice diagram.) These parameters were calculated and annotated for all the structures.

 

Median distances were 3.5 Å between halogen and oxygen/sulfur, regardless of the halogen. Median θ1 angles were smaller than the 150º-180º expected, particularly for fluorine atoms, while median θ2 angles were more consistent with theory, at 90º-120º.

 

For halogen-π-systems, median distances were 4.8 Å for all halogens except iodine, which came in slightly higher. But θ1 angles were still smaller than expected, mostly between 110º-140º.

 

Armed with this tranche of high-quality data, the researchers established a Halogen Bond Score, or HalBS. For any potential halogen bond in a new crystal structure or other structural model, the distance, θ1, and, if applicable, the θ2 values are calculated, and if any of these diverge too far from the median values, HalBS flags them. Importantly, the researchers acknowledge that “the current HalBS cannot be used as a direct validation metric but can provide an indication of genuine halogen bonds and ‘not so proper’ halogen bonds.”

 

With this caveat HalBS could be useful, and the researchers have made the source code available at https://github.com/PDB-REDO/HalBS (though the link doesn’t seem to work for me). As they note, more data, such as might be provided by widespread deposition of large crystallographic fragment screens, could further refine HalBS. Of course, the existence of a halogen bond exists says little about how much binding energy it contributes, but it’s a start.

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.

Fragments vs KEAP1: Fragment growing this time

Kelch-like ECH-associated protein 1 (KEAP1) binds to nuclear factor erythroid 2-related factor 2 (NRF2), targeting it for degradation. Blocking this interaction has anti-inflammatory effects, and indeed the approved drugs dimethyl fumarate and omaveloxolone are believed to act in part through this mechanism. But those drugs hit a lot of other targets, and more specific molecules have long been sought; we wrote about one in 2016 and another in 2021. In an open-access paper just published in Angew. Chem. Int. Ed., Anders Bach and an international team of collaborators at University of Copenhagen and elsewhere describe a new chemical series.

 

As in the 2016 paper, the researchers started with a crystallographic screen, in this case using the 768-member DSI-poised library, which we wrote about here. This resulted in 80 hits, all binding in the so-called Kelch pocket, which has previously been targeted. Thirteen of these bound in the central region, and compound 1 showed modest but measurable affinity by SPR.

 

All previously reported non-covalent high-affinity KEAP1 ligands contain at least one acidic moiety to interact with arginine residues in the protein, so the researchers used structure-based design to add carboxylic acids, resulting in compound 4, with low micromolar affinity. This molecule, unlike the initial fragment, could also block the KEAP1-NRF2 interaction in a fluorescence polarization assay.

 

Building into a hydrophobic sub-pocket yielded compound 12, and adding strategically placed hydrogen-bond acceptors led to further improvements in affinity, ultimately leading to compound 28, with low nanomolar activity. Crystallography revealed that these molecules bound in a similar fashion as the initial fragment.

 

Compound 28 and related molecules were tested in a variety of assays. They were selective for KEAP1 over 15 other human Kelch domains in a thermal shift assay. Compound 28 activated NRF-2 regulated cytoprotective genes and decreased inflammatory markers in multiple cell lines. It also displayed RNA expression profiles similar to those of other reported non-covalent KEAP1 inhibitors. Cellular potency in some of these assays was as good as 60 nM.

 

This is a nice fragment-to-lead story, though no ADME or DMPK data are reported, and the combination of relatively high molecular weight, negative charge, and lipophilicity suggest that permeability and oral bioavailability may be challenging. Indeed, the researchers note that no non-covalent KEAP1-NRF2 inhibitors have entered the clinic. Perhaps this target is better suited for covalent inhibitors, preferably ones more selective than dimethyl fumarate. More on those later.

How can we house our crystallographic data?

Three years ago we highlighted a growing debate about how and where to house crystallographic fragment data. With the recent surge in high-throughput crystallography, issues including access, accuracy, and capacity have only become more urgent. An open-access perspective in Nat. Comm. by Manfred Weiss (Helmholtz-Zentrum Berlin) and multiple coauthors, including yours truly, calls on the scientific community to make some difficult decisions. Indeed, a session at the 75^th annual meeting of the American Crystallographic Association going on today is devoted to the topic.

 

High-throughput crystallography can involve soaking more than 1000 crystals with fragments, sometimes yielding hundreds of protein-ligand structures. The paper tabulates a dozen synchrotrons around the world with current or planned high-throughput capabilities. We’ve written recently about the XChem facility at the Diamond Light Source, which is currently running about 80 fragment screens per year. Assuming similar productivity at the other synchrotrons, we might soon see 1000 fragment campaigns per year worldwide. If each of these involves 1000 crystals and we get 10% hit rates, that could mean 100,000 new fragment structures annually.

 

That is a big number. For reference, 10,000 new crystal structures are currently being released by the protein data bank (PDB) each year. (Director of the RCSB PDB Stephen Burley is one of the authors of the perspective.)

 

The problem is that, as we discussed in the 2022 blog post, most fragment structures from high-throughput screens are not refined to the level required for the PDB, a process which typically takes a day or two for the researcher and up to 3 hours by a biocurator at the PDB. Moreover, fragments are often identified using PanDDA (Pan-Dataset Density Analysis, which we wrote about here), a process which makes use of the many unbound structures obtained in a dataset. Ideally, these datasets should also be made available.

 

The challenge is balancing practicality with FAIR (Findable, Accessible, Interoperable, and Reusable) principles. The paper outlines four non-exclusive options. Very briefly, these are:

 

Option One: Fully refine and deposit all protein-fragment structures just as with other structures.

 

Option Two: Partially refine structures, and possibly flag or even segregate them from other structures in the PDB.

 

Option Three: Rather than treating each protein-ligand structure independently, treat each high-throughput screen as a single experiment, and archive all of the data in its entirety, including unbound structures. These data could be housed in the PDB or elsewhere.

 

Option Four: A hybrid approach, where fully refined structures would be deposited in the PDB and the rest of the data would be stored in a separate branch of the PDB or elsewhere entirely.

 

There are pros and cons for each option. At the extremes, the first option puts a tremendous burden on experimentalists and the PDB, and potentially valuable information regarding unbound structures is lost, while option three requires setting up new repositories to store vast quantities of data.

 

The paper intentionally avoids making a specific recommendation and instead calls for discussion within the scientific community. Personally, I favor some sort of hybrid approach such as option four. As the paper notes, no one could have foreseen AlphaFold2 when the PDB was launched in 1971. Over the next decade researchers around the world are likely to generate hundreds of thousands of protein-fragment structures. I don’t pretend to know what the artificial intelligence tools of the future will be able to make of such data, but I hope they will have access.

 

What do you think?