Keeping molecular dynamics cool for fragments

Accurately and reliably predicting fragment binding modes would be preferrable to doing messy, expensive, and sometimes tedious experimental work, but we’re not there yet. One of the biggest problems is that, because fragments usually bind weakly to proteins, it is hard to tell which of several possible binding modes is most favorable. In an open-access J. Chem. Inf. Model. paper published earlier this year, Stefano Moro and colleagues at University of Padova report progress.

 

Their approach, called Thermal Titration Molecular Dynamics (TTMD), analyzes short molecular dynamics simulations across increasing temperatures; if the ligand remains bound to the protein, this indicates a more stable binding mode. (It seems a bit like the dynamic undocking we wrote about here.) The researchers had previously reported good results for larger, drug-sized molecules, but not for four fragment-protein complexes.

 

Recognizing the low affinities of fragments, the researchers decided to lower the (virtual) temperatures. Rather than heating from 300 to 450 K, they heated from 73 to 233 K; ie, from just below the boiling point of liquid nitrogen to a moderately cold winter’s day in Minnesota. They first docked fragments using PLANTS-ChemPLP, which is free for academics, and chose the five best-scoring poses for evaluation.

 

Next, the researchers performed TTMD. There are several different ways to assess how well the ligand remains bound to the protein over the course of a molecular dynamics simulation, and four different scoring methods were chosen. When TTMD was tested on the four fragment-protein complexes that had previously failed, at least two of the scoring methods correctly identified the crystallographic binding mode for three of the fragments.

 

Thus encouraged, the researchers tested ten more compounds bound to six new proteins. The results were quite encouraging, with up to 86% of crystallographic binding modes being correctly identified by at least one of the scoring functions in TTMD vs 50% for docking alone. Impressively, two of the examples were MiniFrag-sized, with just 6 or 7 non-hydrogen atoms, yet the crystallographic pose was identified as the lowest energy in all four TTMD scores.

 

This is nice work, but the question arises how these specific ligands and proteins were chosen. Several years ago we highlighted a curated set of 93 protein-ligand structures that were used to benchmark other virtual approaches, and it would be nice to see how TTMD performs on these. Still, TTMD’s performance on its chosen examples is encouraging, and laudably the researchers have made their code freely available. If you try it out, please let us know how it works in your hands.

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.

Hundreds of crystallographic ligands for FABP4 – many not as expected

The ten human fatty-acid binding proteins (FABPs) shuttle lipids around cells. As we noted several years ago, FABP4 and FABP5 are potential drug targets for diabetes and atherosclerosis, but selectivity over FABP3 is needed to avoid cardiotoxicity. Markus Rudolph and colleagues at Hoffmann-La Roche describe progress towards selective molecules in three consecutive open-access Acta. Cryst. D papers. Perhaps more importantly, they gift a massive high quality data set to the scientific community – along with some important caveats about data for protein-ligand structures.

 

The first paper focuses on purification and NMR characterization of FABP4. Recombinant FABPs are normally expressed in E. coli, and they always contain natural fatty acids that copurify with the protein. This can complicate ligand binding studies, since the endogenous fatty acids act as competitors. Indeed, the researchers highlight two structures in the protein data bank (PDB) whose supposed ligands are probably fatty acids.

 

To solve this problem, the researchers denature FABP4, separate the fatty acid, and then refold the protein. This truly apo form of the protein was studied by NMR, revealing that the protein becomes more rigid upon ligand-binding.

 

The second paper is of more general interest. It reports a set of 229 crystal structures of various FABPs, of which 216 have a bound ligand. Of these, 75 have associated IC₅₀ values for at least one FABP, and 50 compounds have IC₅₀ values reported for FABP3, FABP4, and FABP5. Importantly, the structures are solved to high resolution, with a median of 1.12 Å. Two crystal forms are particularly suitable for soaking, and compounds were typically soaked at 60 mM in 30% DMSO overnight.

 

All the crystal structures are deposited in the PDB, and all the binding data are provided in the supporting information. Given FABPs’ predilection for carboxylic acids, the ligands contain a variety of carboxylic acid mimetics. This wealth of high-quality data should be valuable for constructing machine-learning binding models, and the researchers conclude by calling “on other industrial organizations to also make their legacy data available such that prediction models with broader applicability may be developed more quickly.”

 

But it was the third paper that really caught my attention: the researchers summarized it as “what is written on the bottle is not what is in the crystal.” In fact, of the 216 ligands reported, a whopping 33 (15%) do not match the compound registered. These are grouped into several categories and described in detail.

 

Human error is the simplest to explain: the researchers show an example where a 1,2-benzoxazole was registered as a 1,3-benzoxazole. Because the molecules have the same molecular weight, mass spectrometry could not distinguish them. Similarly, the researchers find several cases where the wrong enantiomer or diastereomer was registered. In another case, a racemic mixture led to a single enantiomer bound to FABP4, with the protein acting as a “chiral sponge.”

 

Other cases are more unusual, and include ring closing, ring opening, acyl shifts, hydrolysis, and instances of ligand decomposition or incomplete reactions. The researchers note that small amounts of impurities could be particularly problematic at the high ligand concentrations used for soaking; they calculate that just 0.06% impurity would be equivalent to the total amount of FABP in a crystal. Some fragment screens are done at even higher concentrations, further increasing the risk of enriching impurities.

 

A 15% rate of unexpected ligands is comparable to the numbers we blogged about here, but those were commercial libraries, whereas this set is from Roche, which likely has better internal quality control. One factor that led to the recognition of the problem is the high resolution, where a single atom change could be readily seen. Another is the buried nature of the ligands; ligands bound on the surface of a protein may have more dynamically disordered bits, which would be difficult to distinguish from missing moieties caused by decomposition.

 

Indeed, the researchers examine two other proteins, PDE10 and ATX, for which they have also released ~200 ligand-bound structures but at lower average resolutions. There are some unexpected ligands for these proteins too, but many fewer than for the FABPs – or perhaps we just can’t observe some of them.

 

As we noted back in 2014, up to a quarter of ligand-containing crystal structures in the PDB may contain serious errors, and the researchers cite a study suggesting that 12% are “just bad.” These could have obvious negative consequences for training computational models, and the researchers call on the community to set standards to create a rigorously chosen training set. Perhaps this discussion could be held in parallel with the discussion on how to house fragment screening data, which we wrote about last month.

Fragments vs CYP125 and CYP142 for M. tuberculosis

Although 2020 and 2021 were baleful exceptions, tuberculosis is normally the world’s deadliest infectious disease. The pathogen Mycobacterium tuberculosis (Mtb) makes its home inside macrophages, the very cells that normally destroy microorganisms. Worse, some strains have become resistant to approved drugs. In a recent open-access J. Med. Chem. paper, Madeline Kavanagh, Kirsty McLean, and collaborators at University of Manchester, University of Cambridge, and elsewhere explore a new mechanism to fight this ancient disease.

 

An important nutrient source Mtb exploits inside human cells is cholesterol, which bacteria oxidize with the cytochrome P450 enzyme CYP125. A second enzyme, CYP142, is also present in some strains and is functionally redundant. Thus, the researchers set out to make a dual inhibitor.

 

Mtb has some 20 CYPs, and the Cambridge researchers have been studying them for a long time: we wrote about their work on CYP121 in 2016 and their work on CYP126 in 2014. All these enzymes contain a heme cofactor, and much is known about targeting the bound iron. However, some ligands are promiscuous, hitting human P450 enzymes, or they are rapidly effluxed out of cells. Thus, the researchers built a fragment library of just 80 likely heme binders but excluded particularly promiscuous moieties, such as imidazoles. The library was screened using UV-vis spectroscopy; ligands that bind to the heme group cause a red-shift in the λ_max. Only four hits were found for CYP125, while a dozen were found for CYP142, including three of the four CYP125 hits. Compound 1a had modest affinity for CYP125 and low micromolar affinity for CYP142.

 

Compound 1a was soaked into crystals of CYP142, and interestingly two molecules bound at the active site: one coordinating to the iron atom as expected, the other binding near the entrance of the active site. This suggested a linking or merging strategy, so the researchers made small libraries based on compound 1a and tested these against the two enzymes. Compound 5m was the most potent against both. Crystal structures of this molecule bound to both CYP125 and CYP142 confirmed that the pyridine nitrogen maintained its interaction with the heme iron, while the added bit nicely filled the space previously occupied by the second copy of compound 1a.

 

Functional assays revealed that compound 5m inhibited both enzymes with nanomolar activity, comparable to their affinities. It also inhibited the growth of Mtb grown on media containing cholesterol as the sole source of carbon. More impressively, it even inhibited the growth of Mtb in standard media spiked with just low concentrations of cholesterol. Oddly though, it also inhibited the growth of Mtb grown on media not containing cholesterol, albeit at a higher concentration, suggesting perhaps other targets. But one reason tuberculosis is so hard to treat is that the bacteria persist inside human cells. Encouragingly, compound 5m inhibited the growth of Mtb in human macrophages at low micromolar concentrations, and it  did not show cytotoxicity up to 50 micromolar concentration.

 

Unfortunately, compound 5m did show cytotoxicity to human HepG2 cells, and it also inhibited several human P450 enzymes at high nanomolar concentrations, which could cause drug-drug interactions. Also, selectivity against other MTb P450 enzymes is unclear. Finally, no in vitro ADME data are reported. Nonetheless, this is a nice fragment to lead story, and compound 5m could be used – cautiously – as a chemical probe to study Mtb biology.

The Chemical Probes Portal turns ten. Use it!

Last week we highlighted a new tool to computationally predict whether a molecule might aggregate, thereby causing false positives. This doesn’t necessarily mean the molecules are bad (after all, some approved drugs aggregate), but it’s all too easy to screen molecules under inappropriate conditions. This brings up the topic of chemical probes, and as it happens the Chemical Probes Portal turns ten years old this year, as celebrated in a Cancer Cell Commentary by Susanne Müller, Domenico Sanfelice, and Paul Workman and a blog post by Ben Kolbington at the Institute of Cancer Research.

 

We first wrote about the Chemical Probes Portal in July 2015, when it contained just 7 compounds. When we returned in 2023 it contained more than 500 compounds, and by the end of last year the number was up to 803. As of today it lists 1174 probes for 622 targets. Nearly a third of the probes also have chemically related inactive controls. These seem like large numbers, but the the human genome conservatively encodes for some 20,000 proteins, and the ambitious Target 2035 initiative seeks chemical probes for all of them.

 

The new paper emphasizes that the standards are in some ways higher for chemical probes than for approved drugs: “whereas probes principally require a high degree of selectivity, drugs need ‘only’ to be safe and effective and may often hit several targets.” Dimethyl fumarate comes to mind as a highly promiscuous covalent modifier that is nonetheless a useful drug for multiple sclerosis and psoriasis.

 

Even when a compound hits a target of interest, that doesn’t mean any biological effects observed are due to the target, particularly when the readout is cell death. The researchers note that TH588 was originally reported as a potent inhibitor of MTH1, but it actually kills cancer cells by binding to tubulin, a fact not always mentioned by chemical suppliers. Another study found that ten clinical compounds were still active in cells even when their putative target was knocked out using CRISPR.

 

The tone of the Commentary is pragmatic, emphasizing that for new or difficult targets, it may be difficult to find good chemical probes. For example, LY294002 is mentioned as a “pathfinder tool” that was useful to explore the biology around the PI3 kinase family but has now been superseded by more selective molecules.

 

Unfortunately, not everyone seems to have gotten the message. Curcumin, which as we noted can aggregate, form nonselective covalent adducts, fluoresce, and generate reactive oxygen species, appears in >2600 PubMed publications – just in the past year. What a waste.

 

If you’re exploring the biology of a target, please check the Portal to see whether there are good probes. If you’re reading (or reviewing!) a paper that reports small molecule studies, please check to see whether the probe has been assessed - especially to see if it shows up as one of more than 250 Unsuitables. And if you’re interested in participating, please consider reviewing or even hosting a Probe Hackathon.

Can machine learning help you avoid SCAMs?

Among the many types of artifacts that can fool screens and derail efforts to find leads, small colloidally aggregating molecules (SCAMs) are particularly pernicious. As we discussed way back in 2009, these molecules can form aggregates in aqueous buffer that interfere with a variety of assays, leading to wasted resources and embarrassing publications.

 

The problem is that there isn’t necessarily anything wrong with the molecules per se, and even many approved drugs can form aggregates. Thus, it is difficult to predict whether any given molecule will be a troublemaker. In a new (open-access) Angew. Chem. Int. Ed. paper, Pascal Friederich, Rebecca Davis, and collaborators at Karlsruhe Institute of Technology and University of Manitoba Winnipeg explore whether machine learning can help.

 

The researchers built a Multi-Explanation Graph Attention Network, or MEGAN, which is accessible through a simple web interface. Rather than a homicidal doll, this MEGAN represents atoms as nodes and bonds as edges in a graph, similar to the Fragment Network we wrote about here. MEGAN was trained on a set of 12,338 aggregators and 177,048 non-aggregating molecules. Importantly, the researchers used explainable AI (xAI), which colors portions of the molecule according to their importance for (non)aggregation.

 

Testing MEGAN on a set of 1500 aggregators and 1500 non-aggregators, none of which were included in the training set, yielded an accuracy of 82%. Given that most molecules don’t aggregate, a model biased towards non-aggregators would be expected to have a high accuracy, and to account for this the researchers assessed the “F1” score, which was similarly impressive.

 

 

The researchers provide several examples in which subtle variations transform a molecule from a non-aggregator to an aggregator, and show that MEGAN correctly predicts these. Furthermore, it “shows its work,” highlighting the chemical features underlying the prediction. For example, 9H-pyrido[3,4-b]indole is predicted with 92% confidence not to be an aggregator.

 

 

Just adding a methyl group flips the odds in favor of aggregation to 92%.

 

Exploring the molecular features that lead to aggregation can reveal general trends, such as rigid, “flat” molecules with moieties that can serve either as hydrogen bond donors or acceptors. This is consistent with a paper we discussed last year, though unfortunately the researchers do not cite it.

 

To further assess the tool, it was tested against a set of drugs that had been characterized as aggregators or non-aggregators. MEGAN correctly classified 15 of 30 aggregators and 24 of 28 non-aggregators. In contrast, a different program caught only 2 of the aggregators. The researchers note that most of the training data for MEGAN came from a single screen in phosphate buffer at pH 7, and aggregation can be very dependent on buffer components and pH.

 

Practical Fragments has previously highlighted other aggregation predictors, most notably Aggregator Advisor and Liability Predictor. As for any computational model, the old chestnut “trust but verify” applies. MEGAN appears to be a useful tool, but please run physical experiments if the molecule is important.

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?

The importance of specific reactivity for covalent drugs

As we noted in our thousandth post, covalent drugs are becoming increasingly popular, particularly for tackling tough targets. But finding and optimizing covalent ligands entails unique challenges, as discussed in a new paper by Bharath Srinivasan at Cancer Research UK. (Derek Lowe also recently blogged about this.)

 

Interactions between noncovalent drugs and their targets are characterized by dissociation or inhibition constants K_D or K_{I ,} where lower numbers mean stronger binding. In contrast, irreversible covalent drugs are characterized by a ratio we discussed last year, k_inact/K_I, where the rate constant k_inact represents the covalent modification step. (Side note: although the term k_inact is commonly used, covalent modulators can also be activators; my company Frontier Medicines recently announced a covalent activator of p53^Y220C. Perhaps k_cov would be more general?)

 

To explain k_inact/K_I, Srinivasan draws a useful analogy to enzymes, which are mechanistically described by the specificity constant k_cat/K_m in Michaelis-Menten kinetics. In both cases, higher numbers mean more rapid modification or greater catalytic efficiency. A study of several thousand enzymes found the median k_cat/K_m to be around 100,000 M^-1s^-1, with 60% between 1,000 and 1,000,000 M^-1s^-1. Enzymes operate by stabilizing the transition state of the reaction, which means that the affinities for the substrates do not necessarily have to be high, particularly if the structures of the substrates differ from the transition states.

 

Just as catalytic efficiency for enzymes can be increased either by increasing k_cat or lowering K_m, the inactivation efficiency of covalent drugs can be optimized either by increasing k_inact or by decreasing K_I. Historically, drug hunters have focused on the latter; we previously described the discovery of TAK-020 in which the affinity of a fragment for the kinase BTK was first optimized and then a covalent warhead was appended.

 

However, focusing on k_inact can also be productive, and Srinivasan argues this is particularly true for challenging targets with shallow pockets where noncovalent affinity is difficult to obtain. As a case in point he discusses covalent KRAS^G12C inhibitors such as sotorasib, which I wrote about here. Just as residues within enzyme active sites stabilize the transition state of a reaction, a lysine residue in KRAS forms a hydrogen bond to the carbonyl of the acrylamide electrophile, thereby increasing its reactivity for the protein.

 

Srinivasan emphasizes that k_inact is specific for each particular protein-ligand pair as well as distinct from intrinsic or chemical reactivity. This is a critical point. Newcomers to the field often worry that a high k_inact value means a molecule is generically reactive and thus likely to react with many proteins, but this is not necessarily true. For example, sotorasib’s favorable k_inact/K_I is driven by a high k_inact for KRAS^G12C but it is still quite specific. Indeed, Srinivasan points out that even a chemically reactive molecule may not react with a protein if the geometry isn’t right.

 

A nice way of assessing specific reactivity (which unfortunately is not cited) is the reactivity enhancement factor, or REF, as defined by Alan Armstrong, David Mann, and colleagues at Imperial College London in an (open-access) 2020 ChemBioChem paper. Akin to the k_cat/k_uncat ratio used to assess rate enhancement for enzymes, REF is defined as the rate of reaction for a specific protein divided by the rate of reaction for glutathione, an abundant cellular thiol. The higher the REF score, the higher the specific reactivity for the protein of interest.

 

Srinivasan also considers tradeoffs between k_inact and K_I as k_inact/K_I approaches the rate of diffusion, suggesting that above 1,000,000 M^-1s^-1 or so any further improvement in affinity will come at the cost of specific reactivity. While this is theoretically interesting, from a practical perspective you can have a perfectly fine drug with a k_inact/K_I of just 10,000 M^-1s^-1.

 

Covalent drugs will only become more important as we pursue increasingly hard targets that have resisted previous efforts. For these targets in particular, focusing on specific reactivity will be rewarding.

Fragment events in 2025 and 2026

For better or for worse, 2025 is half-way over. There are still some good conferences coming up, and 2026 is also starting to take shape.

September 21-24: FBLD 2025 will be held in the original Cambridge (UK),  where it was supposed to be held in 2020. This will mark the ninth in an illustrious series of conferences organized by scientists for scientists. You can read impressions of FBLD 2024, FBLD 2018, FBLD 2016, FBLD 2014, FBLD 2012, FBLD 2010, and FBLD 2009. 

 

September 22-25: You'll need to make a tough choice: FBLD 2025 or CHI’s Twenty-Third Annual Discovery on Target in Boston. As the name implies this event is more target-focused than chemistry-focused, but there are always plenty of FBDD-related talks. You can read my impressions of the 2024 meeting, the 2023 meeting, the 2022 meeting, the 2021 meeting, the 2020 virtual meeting, the 2019 meeting, and the 2018 meeting.

 

November 11-13: CHI holds its second Drug Discovery Chemistry Europe in beautiful Barcelona. This will include tracks on lead generation, protein-protein interactions, degraders and glues, and machine learning, with multiple fragment talks throughout. 

2026

February 17-19:  The Twelfth NovAliX Conference will be held for the first time in San Diego! (Please note the date and location change.) You can read my impressions of the 2018 Boston event here, the 2017 Strasbourg event here, and Teddy's impressions of the 2013 event here, here, and here. 

 

April 13-16: CHI’s Fragment-Based Drug Discovery turns 21, old enough to legally drink in the US! The longest-running annual fragment event returns as always to San Diego. This is part of the larger Drug Discovery Chemistry meeting. You can read impressions of the 2025 meeting, the 2024 meeting, the 2023 meeting, the 2022 meeting, the 2021 virtual meeting, the 2020 virtual meeting, the 2019 meeting, the 2018 meeting, the 2017 meeting, the 2016 meeting; the 2015 meeting here, here, and here; the 2014 meeting here and here; the 2013 meeting here and here; the 2012 meeting; the 2011 meeting; and the 2010 meeting. 

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

 

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

Fragments vs HNF4: a chemical probe

Driven by the spectacular success of diabetes and obesity drugs, metabolism is a hot therapeutic area. Much of the focus has been on GPCRs such as GLP-1R, but there are plenty more potential targets. For example, the two isoforms of hepatocyte nuclear factor 4 (HNF4α and HNF4γ) are nuclear receptors that control transcription of genes associated with metabolic homeostasis; mutations in HNF4α lead to an inheritable form of diabetes. But the biology is complicated and not fully understood. In a new J. Med. Chem. paper, Daniel Merk and collaborators at Goethe University Frankfurt and elsewhere describe a chemical probe.

 

The story actually starts with an (open-access) 2020 paper in Int. J. Mol. Sci., in which the researchers screened 480 fragments at 50 µM in a reporter gene assay. The fragments were derived from FDA-approved drugs and roughly rule-of-three compliant, though with some more lipophilic members.

 

HNF4 is activated by binding to naturally occurring fatty acids, and the cell-based assay could detect ligands that altered gene activity, either by increasing it (agonists) or decreasing it (inverse agonists). After follow-up dose response assays and binding validation via isothermal titration calorimetry (ITC), the researchers found one low micromolar agonist and two low micromolar inverse agonists. The most potent of these became the subject of the new paper.

 

Structure-activity studies on compound 4 revealed that both the carboxylic acid and hydroxyl moieties are important for binding, and that affinity could be improved by growing the fragment. Interestingly, while compound 20 acts as an inverse agonist, compound 23 is an agonist. A crystal structure of compound 23 bound to the ligand binding domain of HNF4α shows that it binds in the same site and in a similar manner as the natural ligand myristic acid. Further medicinal chemistry ultimately led to compound 46, an agonist with low nanomolar activity in cells.

 

 

Compound 46 was selective against nine other nuclear receptors and not toxic up to 10 µM. The compound was also active in a separate reporter assay using the human insulin promoter. Purified recombinant HNF4α normally retains a bound fatty acid, and the researchers measured binding affinity of compound 46 with and without this natural ligand. The binding affinity of compound 46 measured in the absence of the fatty acid was stronger (low nanomolar by ITC), demonstrating competition, as expected.

 

This is a nice fragment to lead success story in academia. It is also an uncommon example where the primary assay was cell-based, and structural biology did not play a significant role in compound optimization. No ADME data are provided, so in vivo studies may be premature, but with its low molecular weight and high ligand efficiency compound 46 is well positioned for further optimization.

Playing fast and loose with electrostatic anchors on RNA

Two weeks ago we discussed how to find ligand-binding sites in RNA. Last week we wrote about how difficult it is to find good ligands even for good binding sites in RNA. A recent open-access paper in J. Med. Chem. by Christian Kersten and colleagues at Johannes Gutenberg-University explores why targeting RNA is so tough.

 

The researchers were interested in two well-characterized riboswitches, naturally occurring RNA elements that bind to small molecules such as metabolites. Specifically, they chose to study a riboswitch that binds to S-adenosyl methionine (SAM, structure here) and a riboswitch that binds to prequeuosine-1 (PreQ₁) and prequeuosine-0 (PreQ₀). 

Due to the phosphate backbone, RNA is highly negatively charged. The researchers asked whether positively charged moieties on ligands can serve as “electrostatic anchors” to generally improve affinity, and if so whether this can lead to any design principles. Multiple biophysical techniques were used to study the interactions of the two riboswitches with various natural and synthetic ligands: surface plasmon resonance (SPR), isothermal titration calorimetry (ITC), and microscale thermophoresis (MST).

 

In the case of the SAM-VI riboswitch, the researchers compared the binding of SAM with closely related molecules having either one fewer positive charge (S-adenosyl homocysteine, or SAH) or synthetic ligands with the same or one more positive charge than SAM. Not surprisingly, SAM has the highest affinity, binding 20-50 fold more tightly than SAH. Further analysis suggested this is largely driven by an increased association rate, in which the positive charge accelerates the kinetics of binding. The driving energy for binding the ligands is enthalpic, but the favorable electrostatic interactions for more positively charged ligands are largely countered by an entropic penalty.

 

Similarly, the affinity of positively charged PreQ₁ for the PreQ₁ riboswitch is higher than the affinity of neutral PreQ₀, though not dramatically. As in the case of the SAM-VI riboswitch, the association rate of the positively charged ligand is more rapid than that of the neutral ligand. Binding for both ligands is highly enthalpic, with unfavorable entropy.

 

Previous reports had described other synthetic ligands for the PreQ₁ riboswitch, each with between one and three cationic centers. However these ligands showed no binding by ITC, questionable binding by MTC, and non-saturable, non-specific “loose binding” by SPR. Positive charges alone are not sufficient for high affinity, specific binding.

 

So what does it all mean? While adding positive charges can improve affinity of ligands for RNA, the increased affinity is usually not dramatic due to enthalpy-entropy compensation. The researchers note that, even for good ligands, the “thermodynamic binding profiles differ from typical protein-ligand interactions, where enthalpic and entropic contributions are usually more balanced.” Moreover, as we’ve noted, protein ligands often gain significant affinity with entropic gains by displacing "high energy water" molecules, but such opportunities are likely less common on the polar surface of RNA.

 

The affinity and ligand efficiency of PreQ₁ for its riboswitch are impressive, so clearly it is possible for small drug-like ligands to bind tightly to RNA. But this interaction is the product of countless eons of evolution. This careful paper suggests why building similarly effective synthetic ligands for most RNA will be difficult.

Targeting SARS-CoV-2 RNA – but not specifically

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

 

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

 

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

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

 

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

 

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

 

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

 

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

Identifying ligand-binding pockets in RNA, computationally and experimentally

Most drugs bind to proteins, but RNA provides many interesting targets. Unfortunately, finding drug-like small molecules that bind to RNA is difficult. A new paper in Proc. Nat. Acad. Sci. USA from Kevin Weeks and colleagues at University of North Carolina Chapel Hill provides tools to do so.

 

RNA presents several challenges for drug discovery. First, there are far fewer high-resolution structures than there are for proteins. This is in part due to the second challenge: RNA strands are often wriggly, able to form multiple conformations. And finally, RNA is highly charged and more polar than most proteins, so there are fewer opportunities for the hydrophobic interactions that often provide significant affinity in protein-ligand complexes.

 

These challenges have not deterred intrepid investigators: Practical Fragments first wrote about targeting RNA with fragments way back in 2009. However, examples of high-affinity ligands remain elusive, and in 2023 I wondered whether “most RNA is truly undruggable.”

 

The latest paper leaves me more optimistic. It describes a computational approach to find small-molecule binding sites in RNA. The researchers started with an open-source tool called fpocket, which was built for proteins. The fpocket program places virtual spheres all around a biomolecule, where each sphere contacts the center of four atoms. The size of each sphere depends on local curvature, and clusters of spheres define pockets.

 

To benchmark fpocket on RNA, the researchers first constructed a curated database of drug-like ligands bound to RNA. Of 538 RNA-ligand structures solved at the fairly low bar of ˂ 3.5 Å resolution, only 48 ligands were deemed drug-like by the quantitative estimate of drug-likeness (QED) score. (Although the QED score may be overly restrictive, and many approved drugs have low QED scores, setting a strict threshold means that any pockets identified are likely to be particularly attractive.)

 

Using default (protein-appropriate) parameters, fpocket identified just 63% of known ligand-binding sites in RNA, vs 83% for proteins. Worse, many predicted RNA pockets probably aren’t actually ligandable because they are too exposed to solvent. By tweaking parameters, the researchers improved performance of the program for RNA to 92%, and they also identified several attractive pockets that had previously been missed.

 

When the researchers applied the reparametrized program, redubbed fpocketR, to two bacterial ribosomes, they found several dozen pockets in each, including known antibiotic-binding sites. To assess whether the new pockets could bind fragments, they used an experimental approach called Frag-MaP, which uses fully functionalized fragment (FFF) probes containing a variable fragment, a photoreactive diazirine, and an alkyne. Treating bacterial cells with these FFF probes in the presence of UV light crosslinks them to nearby RNA. Crosslinked probes can then be isolated using click chemistry with the alkyne, and RNA sequencing reveals the sites of modification. Impressively, 89% of ligand binding sites found in the Frag-MaP experiments were predicted by fpocketR.

 

In another validation experiment, fpocketR identified pockets where 7 out of 17 antibiotics bind to bacterial ribosomes. Notably, all but one of the undetected pockets bind antibiotics such as aminoglycosides that don’t appear conventionally drug-like and indeed are not orally bioavailable.

 

Continuing to apply fpocketR to more RNAs led to the identification of dozens of new pockets. Interestingly, most of these pockets occur in complex RNA structures, such as multi-helix junctions or pseudoknots, rather than simpler structures such as bulges and consecutive loops. This could explain the paucity of fragment hits in a study we highlighted in 2023, which focused on simple loops.

 

Now that we know where to find attractive ligand-binding pockets in RNA, hopefully we will be more successful finding high-affinity ligands.

Small and simple, but novel and potent

Back in 2012 we wrote about GDB-17, a database of possible small molecules having up to 17 carbon, oxygen, nitrogen, sulfur, and halogen atoms, most of which have never been synthesized. Although novelty isn’t strictly necessary for fragments, as evidenced by the fact that 7-azaindole has given rise to three approved drugs, it’s certainly nice to have. In a new (open-access) J. Med. Chem. paper, Jürg Gertsch, Jean-Louis Reymond, and colleagues at the University of Bern synthesize fragments that had not been previously made and show that they are biologically active.

 

When you start drawing all possible small molecules you get lots of weird stuff, including an explosion of compounds containing multiple three- and four-membered rings, which may be difficult to make. The researchers wisely focused on “mono- and bicyclic ring systems containing only five-, six-, or seven-membered rings.” They further limited their search to molecules containing just carbon and one or two nitrogen atoms (as well as hydrogen, of course). Systematic enumeration led to 1139 scaffolds, ignoring stereochemistry, of which 680 had not been previously reported in PubChem. Out of these, three related scaffolds were chosen for investigation.

 

Computational retrosynthesis was used to devise routes to the three bicyclic scaffolds, and these were successfully synthesized, along with mono-benzylated versions, for a total of 14 molecules (including stereoisomers), all rule-of-three compliant. The online Polypharmacology Browser 2 (PPB2) was used to predict targets, and several monoamine transporters came up as potential hits. The molecules were tested against norepinephrine transporter (NET), dopamine transporter (DAT), serotonin transporter (SERT), and the σ-R1 receptor in radioligand displacement assays. None of the free diamines were active, but several of the benzylated compounds were, in particular compound 1a.

 

Compound 1a was initially made as a racemic mixture, and when the two enantiomers were resolved (R,R)-1a was found to be a mid-nanomolar inhibitor of NET while (S,S)-1a was 26-fold weaker. Compound (R,R)-1a was also a mid- to high nanomolar inhibitor of σ-R1, DAT, and SERT. Pharmacokinetic experiments in mice revealed that the molecule had poor oral bioavailability but remarkably high brain penetration and caused sedation. The researchers conducted additional mechanistic studies beyond the scope of this blog post and conclude that (R,R)-1a could be a lead for “neuropsychiatric disorders associated with monoamine dysregulation.”

 

There are several nice lessons in this paper. First, as we noted more than a decade ago, there is plenty of novelty at the bottom of chemical space. Moreover, and in contrast to our post last week, even small fragments can have high affinities. But novelty comes at a cost: synthesis of compound 2a required eight steps from an inexpensive starting material with an overall yield of just 9%, though this could certainly be optimized. Nonetheless, particularly for CNS-targeting drugs which usually need to be small in order to cross the blood brain barrier, the price might be worth paying.

 

Of course, even within this paper there are hundreds more scaffolds to look at than the three tested, and perhaps the researchers were lucky that their choices were biologically active. As computational methods continue to advance, it will be worthwhile turning them loose on GDB-17.

Crystallography first in fragment optimization: Binding-Site Purification of Actives (B-SPA)

At FBLD 2024, Frank von Delft (Diamond Light Source) announced the ambitious goal of taking a 100 µM binder to a 10 nM lead in less than a week for less than £1000. Fragment to lead optimization usually takes longer, as dozens or even hundreds of compounds need to be synthesized and tested. One way to speed things up is through “crude reaction screening,” otherwise known as “direct to biology,” in which unpurified reaction mixtures are tested directly. In a new (open-access) Angew. Chem. Int. Ed. paper, Frank, John Spencer, and collaborators at University of Oxford, University of Sussex, and Creoptix apply this approach to crystallographic screening.

 

The researchers were interested in the second bromodomain of Pleckstrin Homology Domain-Interacting Protein, or PHIP(2), an oncology target. As we discussed in 2016, they had previously run a crystallographic screen and identified multiple hits, including F709, which, despite having no measurable affinity, had good electron density and multiple vectors for optimization. Six separate libraries based on this fragment were constructed, with between 58 and 1024 targeted small-molecule products per library and up to four steps done without purification.

 

One challenge for crude reaction screening is assessing whether or not a reaction has actually generated product. Typically this is done by analytical liquid chromatography mass spectrometry (LCMS), but analyzing results manually is tedious. Fortunately academics have graduate students and postdocs, and it was presumably these intrepid souls who spent 17 days analyzing the 1876 small-molecule products attempted.

 

I can say from personal experience that spending hours perusing LCMS chromatograms is not enjoyable, so the researchers built an automated tool called MSCheck, which appears to be freely available here. This showed 83% agreement with the manually curated data, and even identified additional true positives that had been missed. All together 1077 of the reaction mixtures had the desired product, with success rates for the various libraries ranging from 39% to 97%.

 

The successful reactions were soaked into crystals and screened, and nearly 90% of these generated usable data. A total of 29 crystals had interpretable density in the ligand binding site: 7 were starting materials and 22 were desired products. Of the products, 19 bound with the piperazine core in a similar position as the initial fragment, while three bound in an alternate manner.

 

Of course, the whole point of this exercise is to find improved binders, so the researchers tested pure versions of each of the 22 crystallographic hits in two different assays. Only compound PHIP-Am1-20 had measurable affinity, with modest ligand efficiency.

This is not the first example of crude reaction screening by crystallography; we wrote about REFiL_x and a related technique in 2020. In one of those papers, the crude reaction mixtures were assessed by SPR as well as crystallography, which revealed that the crystallographic screen missed some binders, and there is no reason to think the same did not happen here. Indeed, molecules that bind tightly in a different conformation may be more likely to shatter the crystal lattice and thus go undetected.

 

The researchers state that for non-crystallographic crude reaction screening “only strong assay readouts are informative.” But is this bug, or a feature? A 2019 publication that used crude reaction screening to identify KRAS ligands (which I wrote about here) used an assay cascade to quickly select the most potent hits. Even the fastest crystallographic screens can’t compete with plate-based assays in terms of speed.

 

Perhaps PHIP(2) is a particularly challenging test case. As we discussed in 2022, multiple computational screens performed poorly in predicting crystallographic binding modes of ligands for this protein. But as I wrote at the time, it may be that many crystallographic ligands are just too weak to be useful.

 

Although there is a strong case for using crystallography first for finding fragments, I am not yet convinced the same applies for optimizing fragments.

From fragment to macrocyclic Ras inhibitors

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

Solving protein-ligand NMR structures without isotopic labeling

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

 

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

 

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

 

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

 

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

Protein-detected NMR without isotopic labeling

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

 

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

 

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

 

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

 

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

 

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

 

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