19 February 2024

Hot spots real and imagined

Practical Fragments has written several times about “hot spots”: regions on proteins where small molecules and fragments readily bind. Knowing whether your target protein has a hot spot can help you decide whether to pursue the target in the first place. A variety of computational approaches have been developed for finding hot spots, most of which start with a crystallographically determined structure. In a new J. Chem. Inf. Mod. paper, Sandor Vajda and collaborators at Boston University and Stony Brook University ask whether computational models of proteins can also be used for one of the more popular methods, FTMap.
The researchers started with a set of 62 proteins, each of which had a published crystal structure bound to a fragment (MW < 200 Da) as well as to a larger molecule. The predicted structures of these proteins were then downloaded from the AlphaFold2 (AF2) site, and these models were truncated to correspond to the residues seen in the crystal structures to facilitate comparisons. The computational models were quite similar to the experimental models, particularly when comparing the positions of the peptide backbone atoms which define the overall shape of the proteins.
Next, the researchers applied the program FTMap, which computationally explores the surface of proteins using a set of 16 very small probes such as ethanol. Hot spots are regions where lots of probes bind, and the “hotness” of these spots correlates with the number of bound probes. FTMap assessed hotness on the AF2 structures and the crystallographicaly determined structures. (Before running FTMap, the bound ligands in the crystal structures were computationally removed.) Additionally, the researchers ran FTMap on unliganded crystal structures for the 47 proteins where these had been reported.
FTMap was broadly successful at finding the hotspots defined by bound fragments, succeeding 77% of the time starting with either the fragment-bound or unliganded structures and 71% starting with the AF2 models. Implementing stricter criteria (demanding the experimental fragment binding site be the top hot spot, for example) reduced the success to 56% for the crystallographic starting points and 47% for the AF2 models.
The paper discusses several examples in detail, in particular the two where the AF2 models were most different from the experimental models. Both of these were large, multidomain proteins. When AF2 models of just the ligand-binding domains were used, the models were significantly improved. This seems to be a generally useful hack: generating truncated AF2 models for other proteins also improved the performance of FTMap.
The utility of AF2 models for docking has been the subject of some debate, with some arguing that even though the overall protein folds may be accurate, local side chain conformations may be wrong, and a single side chain rotation may make the difference between ligand binding or not. This paper suggests that hot spots are not too sensitive to these subtleties, and that AF2 models can be used for finding hot spots.

12 February 2024

Fragment screening across the proteome, noncovalently

Last week we discussed methodological improvements to industrialize covalent fragment screening across the proteome. While I’m a huge fan of covalent binders, their noncovalent counterparts are the vanilla ice cream of FBLD: also tasty and much more common. Back in 2017 we described how “fully functionalized fragments,” or FFFs, could be used to screen noncovalent fragments in cells. A new paper in Nat. Chem. Biol. by Christopher Parker and collaborators at Scripps and BMS further optimizes the approach.
FFFs contain, in addition to the variable fragment, a photoreactive group (often a diazirine) and an alkyne tag. When exposed to light the photoreactive group can react with nearby proteins and the alkyne tag can be used to fish out the proteins. In the new paper the researchers started with a dozen FFFs.
One challenge, which we discussed in 2021, is that the FFFs may react with many sites on a given protein. During analysis, a protein is typically digested into peptides for mass spectrometry. If a FFF reacts at several sites on a peptide the resulting spectra will be “chimeric” and more difficult to characterize.
The researchers developed methods to take these chimeric spectra into account when searching for sites of modification. The approach, called Dizco (for diazirine probe-labeled peptide discoverer) can identify three times as many peptides as standard approaches, as well as more detailed information on sites of modifications. 
Two pairs of FFF probes consisted of enantiomers, and these showed differential labeling across the proteome, consistent with specific molecular recognition. The researchers also confirmed binding of a few FFF probes to several proteins using a cellular thermal shift assay (CETSA).
In all, the probes modified 3603 peptides on 1669 proteins. The sites of modification were then mapped onto predicted or modeled three dimensional structures of the proteins. Importantly, and consistent with the 2017 work, most of the labeled sites were near predicted pockets. The researchers confirmed this for four proteins by showing that FFF probe binding could be competed by adding ligands known to bind to the pockets.
Next, the researchers docked (using AutoDock) their FFF probes onto 175 proteins (108 from structures in the Protein Data Bank and 67 from AlphaFold structures). They found that the docking experiments recapitulated the experimental data, and in fact often placed the diazirine tag near the protein residues found to react. Strikingly, and in another step forward for in silico approaches, docking against structures from AlphaFold was nearly as effective as those from the protein data bank.
As the researchers conclude, “we identified many binding pockets that have no reported ligands… these probes may serve as leads for further optimization.” It will be fun to see how far they go.

05 February 2024

Fragment screening across the proteome, industrialized

Last week we discussed covalent fragment screens against isolated enzymes, which can be very effective. But screening in cells or cell lysates preserves proteins in a more physiological environment and allows many proteins across the proteome to be screened simultaneously. In 2016 we wrote about covalent screens in human cell lysates which identified fragment hits for 758 cysteine residues in 637 proteins. Mass spectrometry techniques have improved since then in terms of both speed and sensitivity, as illustrated in a new Cell Chem. Biol. paper from Steve Gygi, Qing Yu, and collaborators at Harvard Medical School and Biogen. (Disclosure: Steve Gygi is on the Scientific Advisory Board of my current company, Frontier Medicines.)
The approach is called TMT-ABPP, or tandem mass tag activity-based protein profiling, and it involves multiple improvements to previous methods, some of which Steve discussed at the Discovery on Target meeting last year. Covalent fragments are added separately to cell lysate aliquots, after which a desthiobiotin iodacetamide (DBIA) probe is introduced. If a given site on a protein has reacted with a fragment, it will not be available to react with the DBIA probe.
Next, proteins are digested to peptides and labeled with TMT (tandem mass tag) reagents, which allow multiple samples (18 in this case, either individual fragments or DMSO-only controls) to be combined for simultaneous analysis. Peptides functionalized with the DBIA probe are captured on streptavidin resin while those that had previously reacted with a covalent fragment will not stick to the resin and be lost. Peptides eluted from the resin are then analyzed by mass spectrometry. The “competition ratio” between treated and untreated lysate gives a measure of how strongly a given site on a given protein is labeled by a fragment.
Multiple other tweaks, such as capturing proteins using magnetic beads and using a special type of mass-spectrometry (high-field asymmetric waveform ion mobility spectrometry, or FAIMS), further streamline the process to a 96-well plate format, with each well containing a mere 10-20 µg of cell lysate, as much as 100-fold less than earlier approaches.
The researchers benchmarked TMT-ABPP using three reactive “scout fragments,” including compound 1 from last week’s post. Collectively they identified 6813 cysteine residues hit by one or more of the scouts.
To demonstrate throughput, the researchers next screened 192 fragments, a third of which were acrylamides while the rest were chloroacetamides. Even with two controls for every 16 samples, this only required 12 injections on a mass spectrometer and resulted in hits against 38,450 cysteines, about 50-fold more than the 2016 paper. Proteins that were more highly expressed were better represented, as were proteins with known reactive cysteine residues, such as thioredoxins. Surprisingly though, surface-exposed cysteine residues were only slightly enriched over more buried cysteines.
The researchers also applied TMT-ABPP to five well-characterized covalent molecules, including the mutant KRASG12C inhibitor ARS-1620, which we wrote about here. In addition to the G12C site of KRAS, several other proteins were also liganded, including adenosine kinase (ADK). The researchers confirmed that ARS-1620 inhibited ADK in an enzymatic assay.
As the researchers note, “proteome-wide profiling of thousands of compounds remains a formidable challenge, both technically and financially.” This paper reveals how to significantly reduce the costs. By using such approaches, it is possible to build a catalog of fragment ligands for thousands of proteins. Doing so with a well-curated library could enable rapid fragment-to-lead campaigns.

29 January 2024

Covalent fragments vs a SARS-CoV-2 helicase

Last week we wrote about the difficulties of trying to understand even well-characterized covalent inhibitors of well-characterized targets. Most projects have far less information, as illustrated in a recent paper in J. Am. Chem. Soc. by Ekaterina Vinogradova, Tarun Kapoor, and collaborators at Rockefeller University and Sanders Tri-Institutional Therapeutics Discovery Institute, who report the first inhibitors of a particular SARS-CoV-2 enzyme.
The researchers were interested in helicases, enzymes that unwind DNA, RNA, or both. To do so, helicases cycle between “open” and “closed” forms, with conformational changes of as much as 15 Å. That dynamism complicates structure-based drug design, and many screens have yielded false positives. An irreversible covalent inhibitor that remained bound to the enzyme through its gyrations would potentially be easier to optimize.
The protein nsp13 from SARS-CoV-2 is essential for viral replication and thus an attractive drug target. The researchers started by testing previously reported and reactive “scout fragments” in a functional assay. Compound 1 inhibited the enzyme, and mass-spectrometry (MS) assays revealed that it modified three sites on the protein. Although multiple modifications are not desirable, the enzyme does contain 26 cysteine residues, so it could be worse. Peptide mapping and mutagenesis experiments revealed that modification of cysteine 556 (C556) is responsible for the inhibitory activity of compound 1.
A series of analogs culminated in compound 3b, which had low micromolar activity after a four hour incubation and also seemed more selective than compound 1, with less modification of other cysteine residues. The enantiomer of compound 3b was at least 6-fold less potent, suggesting molecular recognition rather than simple reactivity. In addition to nsp13, the researchers examined two mammalian helicases with disease relevance, WRN and BLM, and found that compound 3b was modestly selective for nsp13. (The researchers find different inhibitors for these two enzymes, though these are weaker and not as extensively characterized as those for nsp13.)

Cysteine 556 is not in the ATP-binding site and does not seem to be involved with RNA binding, and the researchers suggest that compound 3b may act allosterically. It seems to be highly conserved too, which might mean mutational resistance is less likely to evolve.
As the researchers acknowledge, compound 3b contains a chloroacetamide warhead, which is likely too reactive and unstable to move forward into in vivo studies, let alone the clinic. Also, had I reviewed the manuscript I would have requested the researchers to provide kinact/KI values rather than merely IC50 values; a rough calculation using the methodology in this paper suggests a modest 10 M-1s-1 for compound 3b. That said, the discovery that liganding C556 inhibits nsp13 means that working to develop more potent and selective molecules may be worth the effort.

22 January 2024

Covalent complexities for kinase inhibitors

Covalent drugs are becoming increasingly popular. But as more researchers search for them, they may encounter pitfalls. A new paper in J. Med. Chem. by  David Heppner and collaborators at the State University of New York Buffalo, AssayQuant Technologies, and Eberhard Karls Universität Tübingen provides a nice roadmap for avoiding them.
The researchers focus on covalent inhibitors of epidermal growth factor receptor (EGFR), a kinase that is frequently mutated in cancer. The first drugs against this target, such as erlotinib, were non-covalent, and these have been largely displaced by more effective covalent molecules such as afatinib. Unfortunately, these earlier drugs are not effective against a common mutant (T790M), spurring the development of third generation molecules such as osimertinib, which was approved by the FDA in 2015. Osimertinib has been extensively studied, with more than 2800 references in PubMed. Yet it is not as well understood as you might expect.
The team uses this system to demonstrate how characterizing irreversible inhibitors is not simple. For reversible enzyme inhibitors, researchers frequently discuss IC50 values or, when they are being more precise, inhibition constants (Ki). The latter are in theory absolute values that do not depend on concentrations of cofactors such as ATP. But for irreversible inhibitors, the IC50 values change depending on how long (and at what concentration) incubation occurs. The proper assessment of an irreversible inhibitor is kinact/KI, which takes into account both the irreversible inactivation step (kinact) as well as the inhibition constant (KI). Note that Ki is not the same as KI ; the former describes only the initial reversible association between protein and inhibitor, while KI incorporates the irreversible step. Told you it was complicated!
And it gets worse. The researchers examined three irreversible covalent inhibitors under various conditions. In one condition, the inhibitors were pre-dissolved in 10% DMSO before being added to the assay mixture to give a final DMSO concentration of 1%. In another condition, the inhibitors were dissolved in pure DMSO before being added to the assay. Despite the final concentration of DMSO being the same (1%), the second condition gave kinact/KI values up to 11-times greater (more potent).
If subtle experimental variations in one lab can change values by more than an order of magnitude, you might expect the literature to vary even more, and you’d be right. In the case of osimertinib, the reported values of kinact/KI vary by nearly 500-fold. Some of the experimental parameters the researchers consider are concentrations of reducing agents such as DTT, which can react with covalent inhibitors, and serum albumin, which also contains a free cysteine residue. Although these did not seem to be problematic for osimertinib itself, they could affect other molecules.
Another consideration for kinases in particular is the concentration of the cofactor ATP. The value of kinact/KI itself will vary depending on [ATP], and the researchers describe how to calculate a “true” kinact/KI which could be used to compare the potency of a given inhibitor against the wild-type vs mutant forms of the enzyme. But while this is more theoretically rigorous, it may be less biologically relevant, since physiological ATP concentrations are less variable than differences in the Michaelis constant (KM) for ATP for different kinases and mutants.
There is lots more to digest in this paper, including analyses of structure-kinetic relationships (SKR, akin to structure-activity relationships, or SAR) for different inhibitors and thorough experimental descriptions. The take-home message is that, due in part to different and often incomplete details, “potency measurements are generally difficult to compare among literature studies,” and “any potency assessments should include appropriate controls under the same conditions as the experimental inhibitors.”

15 January 2024

What makes molecules aggregate?

The propensity for some small molecules to form aggregates in water has bedeviled fragment-finding efforts for decades. Indeed, the phenomenon was not fully recognized until early this century. Although plenty of tools are available for detecting aggregates, I still see too many papers that omit these crucial quality controls. As annoying as aggregation can be in activity assays, in certain cases it could actually be useful for formulating drugs. There has been speculation that the good oral bioavailability of venetoclax is due to aggregation. But despite computational methods to predict aggregation, the structural features of molecules that cause them to aggregate are still not well understood. In a new open-access Nature Comm. paper, Daniel Heller and collaborators at Memorial Sloan Kettering Cancer Center and elsewhere provide some answers.
The researchers had previously published an article describing how indocyanine green (ICG) could be used to stabilize and visualize aggregates, and they applied the same technique to examine the aggregation potential of a small set of fragments. Benzoic acid and 2-napthoic acid did not aggregate, while 4-phenylbenzoic acid did. Intrigued, the researchers tested a set of 14 4-substituted biphenyl fragments and found that those containing both a hydrogen bond donor and acceptor, such as acids, sulfonamides, amides, and ureas, could aggregate, while those containing only donors (aniline) or acceptors (nitrile) did not.
Fourier transform infrared spectroscopy was used to examine the stretching region of the carbonyl of 4-phenylbenzoic acid in various states: in an aqueous aggregate, in solution in either t-butanol or DMSO, or in the solid state. Interestingly, the aggregate most resembled the solid state, consistent with close-packed self-assembly as opposed to free in solution.
From all this, the researchers hypothesized that a combination of aromatic groups and hydrogen bond donors and acceptors was necessary for aggregation. However, having these features does not mean aggregation is inevitable. Neither 3-phenylbenzoic acid nor 2-phenylbenzoic acid formed aggregates, with the former precipitating while the latter remained completely soluble. These three phenylbenzoic acid isomers behave very differently despite the fact that they have the same calculated logP values, and the suggestion is that the latter two molecules are less able to form pi-pi stacking interactions that lead to stable aggregation.
Next the researchers examined the approved drug sorafenib, which had previously been shown to aggregate. This was confirmed, and the aggregates were characterized with a battery of biophysical methods including dynamic light scattering, transmission electron microscopy, and X-ray scattering, along with molecular dynamics simulations. The conclusion is that sorafenib forms amorphous aggregates whose assembly is driven by a combination of pi-pi stacking and hydrogen-bonding. A series of sorafenib analogs was synthesized, and those that could not form strong intermolecular hydrogen bonds were less prone to aggregation.
All of this is fascinating from a molecular assembly viewpoint and will help to explain and predict which compounds are likely to aggregate, for better or for worse. But as of now, experimental assessment is still best practice for any new compound.

08 January 2024

Electrophilic MiniFrags vs HDAC8

In fragment-based lead discovery, small is good – at least down to a certain point. While most fragments consist of between 7 and 20 non-hydrogen atoms, some investigators have built libraries of much smaller fragments with at most 7 or 8 heavy atoms. We’ve written about MiniFrags and MicroFrags, which are typically screened crystallographically at high concentrations to find hot spots. In a new open-access J. Med. Chem. paper, Franz-Josef Meyer-Almes, György Keserű, and collaborators at the Budapest University of Technology and Economics, the University of Applied Sciences Darmstadt, and the University of Veterinary Medicine Vienna have applied the concept to covalent fragments.
The researchers started with a set of 84 fragments, all heterocycles functionalized with one of six warheads, which we wrote about here. They systematically methylated nitrogen atoms on some of these to generate 58 more fragments containing obligate positive charges, such as compound B6+ below. The intrinsic reactivity of the fragments was assessed by reacting them with the biologically relevant thiol glutathione (GSH).
Methylating the heterocycles made them more electrophilic and thus more reactive. For example, only 16 of the 84 non-methylated fragments had a half-life (t1/2) < 48 hours against GSH, in contrast with 30 of the 58 methylated fragments. In fact, 17 of the methylated fragments had t1/2 < 10 minutes.
Next, all 142 fragments were screened at 250 µM for 2 hours at 30 ºC in a biochemical assay against histone deacetylase 8 (HDAC8), an enzyme important for cell cycle progression. Hits were confirmed in dose-response experiments after 1 hour pre-incubation. Consistent with the glutathione data, only 12 of the non-methylated compounds showed IC50 < 50 µM, while 54 of the 58 methylated compounds were active. One of the fragments, B6+, had a kinact/KI value of 4006 M-1s-1, not far from that found in approved covalent drugs.
HDAC8 contains ten cysteine residues, and sites of modification were determined using both site-directed mutagenesis as well as tryptic digestion followed by mass spectrometry. In total, seven residues could be labeled by one or more fragments. The most reactive cysteine, C153, is close to the binding site of a previously reported inhibitor (compound 1), and the researchers tried merging reactive fragments such as B6+ onto this molecule. The best molecule, compound 3, had a kinact/KI value of 1566 M-1s-1. However, the drop from B6+ alone suggests that the non-covalent affinity component of compound 1 may have been lost.

This is an interesting approach, and as the researchers note, activity assays available for covalent fragments are higher-throughput than the crystallographic screens required for MiniFrags and MicroFrags. On the other hand, there are limitations. For one thing, the obligate positive charge on the methylated fragments could overwhelm other properties, and could even lead to denaturation of proteins at high concentrations, rendering screens uninformative. These fragments are also less likely to be cell permeable.
Finally, as we wrote ten years ago, characterizing irreversible covalent fragments presents a challenge in deconvoluting intrinsic reactivity from specific binding. Computational mapping of hot spots on HDAC8 using FTMap revealed that some correlate with modified cysteine residues. But other modified cysteine residues are in surface-exposed flexible loops with no nearby pockets, and hits against these are likely not advanceable. The fact that some of the fragments modify as many as five cysteine residues on HDAC8 suggests they may be too reactive.
Still, the systematic characterization of this library is useful experimentally and for training models. It will be interesting to see it deployed against additional protein targets.

02 January 2024

Fragment events in 2024

We don't know for sure what 2024 has in store for us, but barring pandemics or other disasters, the year is shaping up to be an annus mirabilis for fragments. For the first time ever, all four of the recurring fragment meetings are scheduled for the same year, and other conferences also look exciting. I hope to see you at one.

March 3-5: RSC-BMCS Ninth Fragment-based Drug Discovery Meeting will be held in Cambridge, UK. This venerable biannual event will be particularly focused on case studies "that have delivered compounds to late stage medicinal chemistry, preclinical, or clinical programmes." You can read my impressions of the 2013 meeting here and the 2009 event here.
April 1-4: CHI’s Nineteenth Annual Fragment-Based Drug Discovery, the longest-running fragment event, returns as always to San Diego. This is part of the larger Drug Discovery Chemistry meeting. You can read impressions of the 2023 meeting here, the 2022 event here, the 2021 virtual meeting here, the 2020 virtual meeting here, the 2019 meeting here, the 2018 meeting here, the 2017 meeting here, the 2016 meeting here; the 2015 meeting herehere, and here; the 2014 meeting here and here; the 2013 meeting here and here; the 2012 meeting here; the 2011 meeting here; and 2010 here
June 2-4:  The theme of the Tenth NovAliX Conference, to be held in the Swiss resort town of Brunnen, is "reinventing drug discovery." You can read my impressions of the 2018 Boston event here, the 2017 Strasbourg event here, and Teddy's impressions of the 2013 event herehere, and here.
June 25-27: FBDD Down Under 2024 will take place in beautiful Brisbane. I believe this is the fifth FBDD DU event and the first to be held outside Melbourne. You can read my impressions of FBDD DU 2019 and FBDD DU 2012.
September 22-25: After a six year hiatus, FBLD 2024 will be held in Boston. This will mark the eighth in an illustrious series of conferences organized by scientists for scientists. You can read impressions of FBLD 2018FBLD 2016FBLD 2014, FBLD 2012FBLD 2010, and FBLD 2009.
September 30 to Oct 3: Autumn is usually a nice time of year in Boston, so why not stick around to attend CHI’s Twenty-Second Annual Discovery on Target. 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 2023 meeting here, the 2022 meeting here, the 2021 event here, the 2020 virtual event here, the 2019 event here, and the 2018 event here.
Know of anything else? Please leave a comment or drop me a note.