Review of 2013 reviews

The year is coming to an end, and as we did last year, Practical Fragments is looking back at notable events as well as reviews that we haven’t previously highlighted.

The fragment calendar started in March in Oxfordshire, at the RSC Fragments 2013 conference, closely followed in April by CHI’s FBDD meeting in San Diego (here and here). Closing out the year for conferences that Teddy or I attended was the Novalix conference on Biophysics in Drug Discovery in Strasbourg (here, here, and here).

There weren’t any new books published (though the special issue of Aus. J. Chem. practically counts as one), but there were several notable reviews.

Stephen Fesik and colleagues at Vanderbilt University published “Fragment-based drug discovery using NMR spectroscopy” in J. Biomol. NMR. This is an excellent overview that covers library design, NMR screening methodologies, and compound optimization. The researchers make an interesting case for including multiple similar compounds and allowing for larger, more lipophilic fragments, while always being careful to avoid “bad actors”. They also do a good job of summarizing the various NMR techniques, including their strengths and limitations, in language accessible to a non-spectroscopist. Finally, the section on fragment linking discusses the theoretical gains in affinity, the practical challenges to achieving these, and strategies to overcome them.

Turning to the other high-resolution structural technique, Rocco Caliandro and colleagues at the CNR-Istituto di Cristallografia in Italy published “Protein crystallography and fragment-based drug design” in Future Med. Chem. This provides a fairly technical description of X-ray crystallography and its role in FBDD, along with a table summarizing around 30 examples, five of which are discussed in some detail.

Of course, it’s always best to use multiple techniques for finding fragments, so it’s well worth perusing “A three-stage biophysical screening cascade for fragment-based drug discovery,” published in Nature Protocols by Chris Abell and colleagues at the University of Cambridge. This expands on a gauntlet of biophysical assays (involving differential scanning fluorimetry (DSF), NMR, crystallography, and isothermal titration calorimetry (ITC)) that we discussed earlier this year. Nature Protocols are highly detailed, with lots of troubleshooting tips, so this is a great resource if you’re exploring any of these techniques.

Finally, Christopher Wilson and Michelle Arkin at the University of California San Francisco published “Probing structural adaptivity at PPI interfaces with small molecules” in Drug Discovery Today: Technologies. Protein-protein interactions are frequent targets for FBLD: see for example here, here, here, here, and here – and that’s just for 2013! The current review gives a nice overview of the technology called Tethering, focusing on the cytokine IL2 and an allosteric site on the kinase PDK1.

And with that, Practical Fragments thanks you for reading and says goodbye to 2013. May your 2014 be happy and fulfilling!

Fragments in Australia

Last year we highlighted the first FBDD conference held in Australia. That meeting has now led to a dozen papers in the December issue of Aus. J. Chem. Many of the papers use the same fragment libraries, so this is a good opportunity to survey a variety of outcomes from different techniques and targets.

The collection of papers (essentially a symposium in print) starts with a clear, concise overview of fragment-based lead discovery by Ray Norton of Monash University. Ray also outlines the rest of the articles in the issue.

A well-designed fragment library is key to getting good hits, and the next two papers address this issue. Jamie Simpson, Martin Scanlon, and colleagues at Monash University discuss the design and construction of a library built for NMR screening. Compounds were selected using slightly relaxed rule-of-three criteria, and special care was taken to ensure that at least 10 analogs of each were commercially available to facilitate follow-up studies. Remarkably, of 1592 compounds purchased, only 1192 passed quality control and were soluble at 1 mM in phosphate buffer. The properties of the final library are compared with nearly two dozen other libraries reported in the literature; this is the most extensive summary I’ve seen on published fragment libraries. The paper also analyzes the results of 14 screens on various targets using saturation transfer difference (STD) NMR. As the researchers note, this technique is prone to false positives, and indeed the average hit rate of 22.5% is high, with only about 50% confirming in secondary assays. There is also a nice analysis of what features are common to hits, along with a list of the 24 compounds that hit in more than 90% of screens.

The other paper on library design, by Tom Peat, Jack Ryan and others (including Pete Kenny), discusses library design at CSIRO. The researchers started with 500 fragments commercially available from Maybridge and supplemented these with roughly the same number of fragments from a collection of small heterocycles that had been synthesized internally; additional “three-dimensional” fragments are also being constructed. At CSIRO the primary screening method appears to be surface plasmon resonance (SPR), in particular the ProteOn instrument that allows simultaneous analysis of six fragments against six targets. Eight of about ten targets have yielded confirmed hits. The researchers show examples of specific (good), nonspecific (probably bad) and ill-behaved (ugly) fragments.

Next up is an excellent discussion of PAINS by Jonathan Baell (at Monash) and collaborators. Although Practical Fragments has covered this topic repeatedly (here, here, here, here, here, and here) it is a sad fact that more examples appear in the literature every day, so there is always something new to write about.

Fragment-finding methods make up the next several papers, starting with a nice overview of native mass spectrometry by Sally-Ann Poulsen at Griffith University. This paper covers theory, practical issues, and recent examples. Roisin McMahon and Jennifer Martin at University of Queensland, along with Martin Scanlon, describe thermal shift assays. In addition to highlighting a number of published examples, the paper also delves into some of the technical challenges and issues with false positives and false negatives, concluding with a nuanced discussion of how to deal with conflicting data.

The subject of conflicting data is central to the work of Olan Dolezal and Tom Peat, both of CSIRO, and their collaborators. They screened the protein trypsin against 500 Maybridge fragments using SPR. Unfortunately they couldn’t go higher than 100 micromolar without running into problems of solubility and aggregation, but even at this relatively low concentration they found 18 hits. X-ray crystallography validated 9 of them, and isothermal titration calorimetry (ITC) also validated 9, with 7 confirmed by all three techniques. (Incidentally, there are lots of great experimental details here.) Four of the SPR hits could not be confirmed by either ITC or X-ray, and 3 turned out to be false positives when repurchased and tested; in one case this appeared to be due to cross-contamination with a more potent compound. In general, the more potent compounds tended to be the ones that reproduced best, and solubility seemed to be a limiting factor for ITC. Despite the imperfect agreement of biophysical techniques, these were still superior to computational approaches on the same target with the same library. As they conclude:

It is gratifying to know (at least for these authors) that experimental data are still of enormous value in the area of fragment-based ligand design and that the modelling community still has a way to go before the experimentalists are put out to pasture.

But experimentalists should not get too cocky: the next paper, by Jamie Simpson and collaborators at Monash University, describes some of the things that can go wrong. An STD NMR screen of the antimicrobial target ketopantoate reductase (KPR) using the same Maybridge library of 500 compounds revealed 196 hits! The 47 with the strongest STD signals were then tested in a ¹H/¹⁵N-HSQC NMR assay, leading to 14 hits, of which 4 gave measurable IC₅₀ values in an enzymatic assay. Unfortunately, follow-up SAR was disappointing, and subsequent experiments revealed that aggregation was to blame: when the biochemical experiments were rerun in the presence of 0.01% Tween-20, only a single fragment gave a measurable IC₅₀ value. The researchers redid their STD-NMR screen in the presence of detergent, resulting in 71 hits, all of which were tested in the biochemical screen. This led to the identification of a new (and fairly potent) hit that had previously been missed. This nicely illustrates the fact that false positives are not just a problem in terms of wasted resources, they can also overwhelm the signal from true positives. The moral? Always use detergent in your assay!

The question of whether structure is needed to prosecute fragments has come up before, and the next paper, by Stephen Headey, Steve Bottomley, and collaborators at Monash University, addresses this question directly. The target protein, a mutant form of α1-antitrypsin called Z-AAT, unfolds and polymerizes in vivo, causing a genetic disease. The researchers used an STD NMR fragment screen of 1137 fragments to identify several hundred hits, and focused on those that bound to the mutant form of the protein rather than the wild-type. They then used a technique called Carr-Purcell-Meiboom-Gill (CPMG) NMR (which relies on line broadening when fragments bind to a protein) to confirm 80 hits, the best of which had a dissociation constant of 330 micromolar. If you’ve stuck through this post thus far you’ll recall that the Monash library was designed for “SAR by catalog”, and 100 analogs of this fragment were purchased and tested, leading to several new hits, one with a dissociation constant of 49 micromolar. Although there is still a long way to go, metastable proteins are tough targets, so this is a nice start.

The next paper, by Ray Norton at Monash University and collaborators, describes a fragment screening cascade against the antimalarial target apical membrane antigen 1 (AMA1). An initial STD NMR assay of 1140 fragments produced 208 hits, but competition experiments with a peptide ligand whittled this number down to 57 that confirmed in both STD and CPMG NMR assays. Of these, 46 confirmed in an SPR assay, and although most are fairly weak, some SAR is starting to emerge as new analogs are synthesized.

Another antimicrobial target, 6-hydroxymethyl-7,8-dihydropterin pyrophosphokinase (HPPK), is the subject of a paper by James Swarbrick at Monash and collaborators. An initial STD NMR screen gave an unnervingly high hit rate (notice any themes emerging?), so 2D ¹⁵N-HMQC experiments were performed on 750 Maybridge fragments, yielding 16 hits. Competition experiments using CPMG NMR and close analyses of the chemical shifts suggested that these fragments bind in the substrate binding site, and SPR confirmed binding for some of the fragments.

Finally, Martin Drysdale of the Beatson Institute highlights some of the success stories of FBDD, including clinical compounds, and ends with a call for shapelier fragments.

All in all this is a great collection of papers, particularly for those relatively new to the field. It will be fun to revisit some of these projects in a few years to see how they’ve progressed.

Undruggable? Pshaw, Fragments can do it.

Bromodomains, as well as other epigenetic targets, are hot right now.  This paper adds to the growing library of fragment success against bromodomains.  As in any nascent field, some of the targets do not have a known biological role.  BAZ2B (bromodomain adjacent to zinc finger domain protein 2B) is one of these.  BAZ2B is interesting compared to the 41 other bromodomains where there is structural information.  Its KAc binding pocket is smaller than other bromodomains (92-105 Angstrom vs 130-220 Angstrom for the other bromodomains and lacks features of BET bromodomains, like the ZA channel and the hydrophobic groove adjacent to the WPF motif.  

BAZ2B (Left), BRD2-BD1 (Right)

Due to these structural differences, the strategies that have been applied successfully to other bromodomains will not transfer to BAZ2B; thus, it considered one of the least druggable bromdomains.  Hence, fragments to the rescue!  

They screened 1300 commercially available (and thus Voldemort Rule compliant) with an alpha screen with hits being defined as 50% activity at 1mM.  10 compounds were identified and confirmed using STD, Waterlogsy, and CPMG NMR experiments (0.8% hit rate).  

(The same library was screened against BRD2-BD1 and CREBBP and had hits rates of 1.8% and 6.1% respectively.)  The ten fragments were then soaked into BAZ2B crystals yielding structures for 1,3,6 and KAc. 

a) KAc, b) 1, c) 3, d) 6

Fragment 6 had poor solubility, so direct ITC was not possible, instead a competition ITC study was used and yielded a 65 uM Kd.  They attempted to optimize this fragment from the 1 position of THgammaC.  All of these modifications resulted in worse affinity.  They then attempted to replace the Chlorine with aryl substituents (based on modeling results).  These compounds showed some improvement in solubility, but no significant improvements in affinity.  They then tried to change the electronic properties of the aromatic substituent.  EWG showed the expected reduction in affinity, but EDG did not show an increase in affinity.  

The most ligand efficient fragments (7, 8, and 10) all contained thioamides, so they synthesized thioamide and thiourea analogs of fragment 6.  Both of these molecules did not bind to the protein. Finally, they attempted to merge 3 and 6 putting the KAc mimetic on the scaffold of 6 . 

This urea containing compound (40) showed improved solubility, and 8-fold reduction in Kd, and a corresponding increase in ligand efficiency. 
This paper is a nice story of using fragments, structural biology, and modeling to generate useful compounds as tools.  I think it also points to "druggability" being a useless term when it comes to fragments.  Archimedes may have wanted a lever long enough, but for me, I just want fragments diverse enough.

Fragment linking on RPA: another protein-protein interaction inhibitor

Protein-protein interactions have often been targeted by fragment efforts, partly I think out of desperation when all else fails. That said, there have been notable successes. Earlier this year we highlighted one example from Stephen Fesik’s group at Vanderbilt University. In a recent paper in J. Med. Chem., the same lab now reports progress on a different target.

Replication protein A (RPA) is important for DNA replication and repair, and is thus an intriguing anti-cancer target. RPA binds to single-stranded DNA as well as to various other proteins involved in the DNA-damage response, such as ATRIP. The site targeted here is the “basic cleft” of RPA that binds ATRIP.

The researchers used the venerable SAR by NMR approach, screening a library of 14,976 fragments against ¹⁵N-labeled protein using HMQC and looking for changes in chemical shifts. A total of 149 fragments produced significant and specific chemical shift differences at 0.8 mM concentration. One of the nice features of SAR by NMR is that not only do you get hits, you find out where they bind. In this case, most of the hits bind in the basic cleft. This region has two sub-sites; some fragments bind to one or the other, while many bind to both. Not surprisingly for a basic binding site, most of the fragments identified are negatively charged.

Although all the hits are relatively weak (the best have dissociation constants around 0.5 mM), some could be improved through various strategies to low micromolar inhibitors, the subject of a paper earlier this year.

In the current paper, the researchers used crystallography to further define the binding modes of select fragments. They found that fragment 2 and fragment 4 could bind to both subsites of the basic cleft, but that when co-crystallized together fragment 2 binds to one subsite while fragment 4 binds to the other. The two fragments come within a few Ångstroms of one another, suggesting that they could be linked.

Fragment linking doesn't always work as well as one might hope, and although the initial linked compound 7 is nearly 30-fold more potent than fragment 4, its ligand efficiency drops considerably. However, structure-based optimization to compound 8 was able to improve the affinity by another two orders of magnitude.

Teddy has argued that SAR by NMR is dangerous because of its reliance on labeled protein and because the initial application involved fragment linking, leading people to believe that these are necessary requirements for successful prosecution of fragments. There are now plenty of examples of using other methods to find and advance fragments, but this paper illustrates that SAR by NMR can still be incredibly powerful.

Of course, the final molecule reported here has warts, notably a thioamide, two carboxylic acids, a molecular weight over 600, and a ClogP>7. Indeed, the absence of reported cellular data is perhaps telling. And yet, Bcl inhibitors are also superficially unattractive but are in the clinic. Clearly more medicinal chemistry needs to be done on these molecules, if nothing else to improve potency, but that’s not to say there isn’t a path forward. It will be fun to watch this story progress.

Upon Request

Dan and I blog here because we love it; we don't get paid, it takes a lot of time, and has very little reward.  I love it when I meet someone new and they say, "Oh, I read your blog."  However, this allows us to have freedom to review what we want, when we want, and how we want.  We don't sell advertising, we don't generate revenue, and so on.  Sometimes people agree with us, sometimes they don't.  These posts are our opinions and like bellybuttons, everyone has one.  Sometimes, we get pinged by somebody who just published a paper and would really like to see us blog about it.  Sometimes we do, sometimes we already have and they missed it, and sometimes we don't.

I received a polite email recently, pointing out this paper.  It was already on my radar to blog about, so I bumped it up in the queue.  This paper caught my attention because it is a fragment screen against a DNA-target, specifically the G-quadruplex from c-MYC.  G-quadruplexes are found in the promoters to many oncogenes and the supposition is that by stabilizing them you can reduce their transcription.  It is an intriguing idea which has already been investigated with a number of compounds to date.  These authors decided to use fragments against the G-quadruplex without knowing if fragments would bind to a nucleic acid target with sufficient affinity and selectivity.  Their primary screen was an Intercalator Displacement Assay (IDA) which has been used previously to find G-quadruplex binding ligands.  A 1377 fragment library (@5mM) (previously used against riboswitches) was used and it obeyed the Voldemort Rule, had >95% purity, and 1mM aqueous solubility. The top 10 hits from this screen could be placed in three groups.

Then, in order to confirm their biochemical assay results they decided to dock them these top 10 fragments.  WHHAAAAT you say?  That was my initial reaction.  Why oh why doth they vex me so?  They then go into EXCRUCIATING detail about the docking results, even concluding from the results some SAR hypotheses.  I kid you not.  They also evaluated these top 10 fragments in a cellular assay (125um and 250uM) using a Western blot readout.  These concentrations were chosen in order to not show short or long-term toxicity, but Mirabile dictu, Data Not Shown.  All fragments, except two (7A3 and 2G5), showed significant changes in c-Myc expression levels. Interestingly, "no significant changes" still gives a 20% reduction in c-Myc levels. 

Four fragments were able to reproduce this effect, of which 11D6 was the best.  The four best were then run pair-wise to and every combination induced a significant reduction of c-Myc.  

So what does this tell us?  Well, I think they have found fragments which bind to the c-MYC promoter G-quadruplex.  It may be exhibiting this binding in the cells.  There are a few experiments that I would like to see (and would have asked for if I had reviewed this paper): a binding assay (SPR, ITC, NMR, whatevs) being he primary one.  We also continue to know that docking really does not add anything to the discovery process. 

Docking vs TINS on a GPCR

Practical Fragments has featured a number of posts comparing various fragment-finding methods. In some cases there is good agreement, while in others – not so much. Computational methods can in theory sample the greatest swath of diversity space: a virtual library can be orders of magnitude larger than any physical library. In a recent paper in J. Chem. Inf. Model. Gregg Siegal at ZoBio and Leiden University and Jens Carlsson at Stockhom University and their colleagues compare the performance of virtual screening with a biophysical method.

The target they chose, the A_2A adenosine receptor (A_2AAR) is a GPCR implicated in a variety of diseases. It also has the advantage of multiple published co-crystal structures with either agonists or antagonists bound, making it a good candidate for computational screening.

The researchers began by conducting a computational screen of 500 fragments using DOCK 3.6 against the crystal structure of an antagonist-bound A_2AAR and ranked these according to how well they scored. Next, the researchers physically screened the same library of 500 fragments against A_2AAR using an NMR-based screening method called TINS (see also here). This resulted in a whopping 94 primary hits, which were followed up in a radioligand displacement assay to yield 5 confirmed hits with K_i values ranging from 14-600 micromolar. Happily, 4 of the 5 hits from the TINS screen were within the top 5% scoring hits identified in silico.

This is satisfying at first glance, but what does it say about the other top-scoring computational hits? Computational screening virtually docks fragments in many possible positions, or poses, which are automatically evaluated. Manual inspection of the top 50 in silico hits showed that, in some cases, the best poses had desolvated polar groups, which would presumably be energetically unfavorable. Indeed, identifying the “correct” pose seems to be a general problem with docking fragments.

But some of the top-scoring fragments looked fine by visual inspection, so 5 of these were tested in a radioligand displacement assay. Surprisingly, 3 of these were active, with K_i values ranging from 18-128 micromolar. In other words, these were false negatives in the primary TINS assay.

Having found hits that had been missed using a biophysical screen, the researchers then docked 328,000 commercially available fragments against the target – an exercise that took only seven hours on a computer cluster. Of the top hits, 22 were purchased and tested in the radioligand displacement assay, and a remarkable 14 of these were active, with K_i values ranging from 2-240 micromolar. (I do wonder how much chemical intuition played a role in choosing hits to purchase.)

Interestingly, all of the 14 hits from docking had respectable ligand efficiencies (LE > 0.3 kcal/mol/atom, with a single exception). This is consistent with previous fragment docking studies that show that the best results are obtained with the most ligand-efficient fragments. It’s also a nice feature; after all, these are exactly the kind of hits you would hope to find, though of course you want to first filter out any garbage from your virtual library.

This paper provides more evidence that computational approaches can find fragment hits for GPCRs, at least relatively “druggable” ones with good structural characterization. It is also a useful reminder of the importance of using multiple methods, to avoid both false positives and false negatives.

Finally, if you haven't already voted on your fragment-finding methods, please do so on the right side of the page!

Fragments vs hematopoietic prostaglandin D2 synthase

Prostaglandins are modified fatty acids involved in myriad biological processes. The enzyme hematopoietic prostaglandin D2 synthase (H-PGDS) converts prostaglandin H2 to prostaglandin D2 and is a potential target for inflammatory disorders. In an article just published online in MedChemComm, Gordon Saxty and co-workers at Astex and collaborators at GlaxoSmithKline describe how they used fragment-based methods to develop orally available inhibitors of this enzyme.

The researchers started by screening their fragment library against crystals of H-PGDS, resulting in 76 fragment hits, some of which were quite potent (sub-micromolar). Two are described in the paper, with most of the focus being on Fragment 6, which wasn’t the most potent but did produce an interesting conformational shift in the protein. Also, although H-PGDS typically binds lipophilic molecules, the researchers were intrigued to observe that the polar pyrazole moiety made two hydrogen bonds to the protein.

Structure-guided optimization of Fragment 6 led to Fragment 8 with a modest improvement in affinity, and fragment growing led to Compound 9, with a satisfying 400-fold boost in affinity. In one of those “nice to have” problems, this compound was actually too hydrophilic (ClogP < 1), but increasing the lipophilicity slightly led to Compound 10 (AT24111 / GSK2696124A), which has low nanomolar potency and oral bioavailability in both mice and rats. The compound also blocked prostaglandin D2 production in mice when dosed orally.

This is a concise but elegant paper, and it is impressive that the researchers managed to maintain or improve ligand efficiency throughout optimization. Of course, all the molecules contain a pyrazole moiety, which is a privileged pharmacophore for kinases, so it will be important to carefully assess selectivity.

Finally, you may recall that Astex was acquired by Otsuka earlier this year. Whenever an acquisition happens there is always the worry that the acquired company will be decimated or shuttered entirely. Happily, this doesn’t seem to be the case here. In fact, Astex actually seems to be expanding: I recently saw a full page job advertisement from them in Nature, and as of this morning their website lists 9 openings, with several in research. Hopefully the honeymoon lasts a long time!

Updated polls: affiliation, methods, and library size

Practical Fragments has run polls on topics including readership, screening methods, favorite metrics (here and here), maximum and minimum fragment size, and the importance of structural information.

We’re interested in how things are changing, particularly in terms of our readership (last polled in 2010) and what fragment-finding methods are most popular (last polled in 2011).

Also, one topic that comes up repeatedly (for example here and here) but has never been actually polled is how many fragments make a good library – we’ve added a question about that too.

Please add your input for 2013; the more people who vote, the more representative the numbers will be. You can find all three questions on the right side of the page, below the "Links of Utility".

We’ll summarize the results and compare them to the previous polls in an upcoming post.

Also, please let us know if you would like us to repeat any of our other polls, and feel free to suggest new topics.

Happy voting!

Fragments against PPI Hot Spots

Protein-Protein interactions are important to so many physiological processes.  There is mounting literature examples of utilization of fragments to block PPIs.  In this paper, Rouhana et al. show how they approached the PPI of Arno and ARF1, ADP-ribosylation factor (part of the RAS superfamily). Arno is part of the brefeldin A-resistant GEFs and share a 200 amino acid domain called SEC7.  SEC7 interacts with ARF through insertion of ARF switch regions into hydrophobic regions of SEC7.  This interaction is interesting from a ligand design standpoint is very interesting because it does not involved an alpha-helix inserting into the partner's hydrophobic groove.  Rather SEC7 has a rather large interface denoted by "hot spots". 

The figure shows their "innovative" FBDD strategy.  First, a Voldemort Rule compliant library was screened in silico.  Since in silico screening is not typically used for fragment screening (but becoming more common) they imposed some initial rules: docking site is small (1-2 residues!), hot spots defined by interaction energy (>1kcal/mol from alanine scan), and very strict selection criteria.  3000 fragments from the Chembridge library were screened.  33 molecules were selected and 40 random fragments chosen as negative controls. 

This was followed by a fluorescence assay (2mM fragments) to test their computational results, just as I say you should do.  Promiscuous binders were removed, not by using detergent, but using protein polarization to directly detect interaction with the target.  This seems like over-complexation of an assay, but without knowing the details of system there may be a very good reason for this approach. 

Compounds 1-4 were identifed as inhibitors (35%, 16%, 38%, and 23% inhibition at 2mM respectively) from each of the "hot spots".  I think it is interesting that these compounds were predicted to have affinities of 10uM or better from the docking.  To me, that just illustrates that predicted affinites are rediculous.  Why do people even report them?  Compound 1 had a Kiapp of 3.7mM which is a LEAN of 0.12!  These were then compared to the PAINS list and 3 is "ambiguous".  Compounds 5 and 6 were chosen as negative controls.  SPR confirmed the binding of 1,2, and 4, but at less than stoichiometric binding levels (the assay was run at 250uM).  3 could not be confirmed as a binder.  Does this mean anything for ambiguous PAINS? 

STD NMR was then used to confirm binding.  In a nice departure, they actually talk about conditions they used: 10 and 30uM ARNO with 0.1mM and 1mM compounds at 32 and 12C.  30uM ARNO with 1mM fragments @12C was what worked (33x fold excess fragments). Confirming the SPR, compounds 1, 2, and 4 were shown to bind, while "ambiguous" 3 had some binding. Finally, compounds were soaked with fragments 1, 2 and 4.  This led to crystal structures which could then be used for more model building, compound design, etc.  This led to the following compound (1.61mM KiApp, LEAN = 0.13) (the methoxy derivative of 1) for further analysis:

By and large, this is a well done, thoughtful work.  They really understand how to setup and interpret STD-NMR. However, these compounds are really atom inefficient.  Is that a consequence of the type of interaction they are inhibiting?  As a fragment, there is nothing wrong with it. 

[Quibble: The authors claim that this is an innovative approach, but I am not seeing it.  They claim their in silico screen first then following up by biophysical techniques is the innovation. ] 

Supplemental Information here.

Natural products as fragments

Natural products were used as drugs long before there was a drug industry, and there is a case to be made that they make good starting points for lead discovery. For one thing, they tend to be more “three-dimensional” than many synthetic molecules. For another, the fact that some organism, somewhere, made them proves that they can bind to proteins. Practical Fragments has previously highlighted examples in which natural products were conceptually fragmented into smaller molecules or incorporated into a fragment library. In a new paper in ACS Chemical Biology, Ronald Quinn at Griffith University and a team of Australian and US collaborators describe a fragment library consisting entirely of natural products.

The researchers assembled a library of 331 natural products with the following characteristics:

• MW ≤ 250 Da (mean = 195.6)
• ClogP < 4 (mean 0.4)
• hydrogen bond donors ≤ 4 (mean 1.3)
• hydrogen bond acceptors ≤ 5 (mean 2.6)
• rotatable bonds ≤ 6 (mean 2.2)
• polar surface area ≤ 45% (mean 17.7%)

The maximum number of donors and acceptors allowed is slightly higher than typical in a fragment library, consistent with the fact that natural products tend to have more oxygen and nitrogen atoms than your typical Suzuki-derived biphenyl. However, the molecular weights are kept low, and despite the tolerance for more lipophilic molecules, the vast majority of the library has ClogP < 3 (with many molecules having ClogP < 0).

Having assembled the library, the researchers used native mass spectrometry to screen pools of eight fragments against the malarial enzyme Plasmodium falciparum 2′-deoxyuridine 5′-triphosphate nucleotidohydrolase (PfdUTPase). They found that a molecule called securinine binds to the enzyme, and six analogs also showed varying degrees of binding as assessed by mass spectrometry.

At this point things get a bit strange. Most of the molecules show some anti-plasmodial activity in culture, but they all seem to modestly activate PfdUTPase. It is unclear whether these two observations are mechanistically related: is the activation of PfdUTPase really what’s causing the anti-plasmodial activity, or are the molecules hitting a different target?

In fact, securinine comes up as a hit in a variety of different biological assays. Looking at the molecular structure this is perhaps not surprising: it contains a reactive electrophilic center that has previously been shown to react with amines under mild conditions, so presumably it can react with all sorts of biological nucleophiles in vivo. This is not to say that covalent inhibitors are unacceptable – dimethyl fumarate looks set to become a blockbuster drug – but it is nice to know if you are dealing with them, and the authors seem not to have considered the possibility.

In the end, I do think libraries of natural products such as these could be useful, but they will require care in their construction, use, and interpretation. Just as there are many synthetic compounds best left out of screening collections, the same goes for natural products. Toxoflavin, for example, is a notorious redox cycling PAIN that has (embarrassingly) been reported as an inhibitor for multiple targets with no evidence for specificity. I’m not ready to put securinine into this category, but I would urge caution.

Just because something is natural doesn’t mean it’s healthy.

WAC vs other methods: all roads lead to good fragments

Among the many ways to find fragments, one of the relatively inexpensive newcomers is weak affinity chromatography, or WAC (see also here). The technique works by immobilizing a target protein onto a column and flowing fragments over it; molecules that bind to the target will elute more slowly than those that don’t. WAC has a number of potential benefits, but as with any technique the question is how well it really works. In a paper published a few months ago in Analytical Chemistry, Sten Ohlson at Linnaeus University and collaborators at Vernalis compared WAC with more established methods.

The protein they chose, HSP90, is sort of the fruitfly of FBLD: just about every technique has been tested on it. It’s also an oncology target with which Vernalis has many years of experience. The researchers chose 111 fragments from the Vernalis library and screened these using WAC. They also screened most of the fragments using surface plasmon resonance (SPR), fluorescence polarization (FP), thermal shift, and NMR (using three techniques: STD, waterLOGSY, and relaxation filtered spectra; only fragments that confirmed in all three NMR assays were considered hits).

The top 27 hits from WAC were also investigated with isothermal titration calorimetry (ITC), and 32 hits were soaked into crystals for X-ray crystallography.

The results were quite encouraging, with good agreement between the different methods:

NMR performed the best, though this could be due in part to the fact that three separate NMR techniques were used. Thermal shift performed the worst, with both false positives as well as false negatives, but even here the agreement was always greater than 50%. It is also important to note that assay conditions varied from technique to technique (for example, the pH ranged from 6.5 to 7.5), which could account for many of the discrepancies.

These results are in sharp contrast to some other comparisons of fragment finding methods (such as here and here), which showed little or no correlation between hits. Why the difference? One possibility is that the folks at Vernalis have worked out all the kinks in their assays and are very adept at separating the true hits from the chaff. Of course, it probably doesn’t hurt that they were working with a well-behaved and extensively characterized target.

The main focus of the paper is WAC, which performed admirably. Compounds could be screened in pools of up to 16 fragments when mass-spectrometry was used as a detection method, and less than 2 milligrams of HSP90 was used to prepare all three of the WAC columns made. One worry with immobilizing your protein is long term stability, but the columns seemed to be stable for at least 6 months through multiple runs.

Of course, no technique is perfect, and one area where WAC gets whacked is in determining dissociation constants. The correlation between K_D values measured by SPR and ITC was excellent (R² = 0.91) but much worse for WAC versus ITC (R² = 0.38) and nonexistent for WAC versus SPR (R² = 0.016), though some of this could possibly be explained by differences in buffer conditions.

Overall it looks like WAC is a great way to find fragments, though you may want to use other methods to actually quantify binding. This paper provides a detailed guide for using WAC, as well as good descriptions of other fragment-finding methods.

Fragment to Lead

In this paper, Constellation and their partner Jubilant Biosys report on their FBHG effort that lead to BET (bromodomain and Extra C-terminal)  inhibitors (BRD4).  Since this is a letter details are short, so hopefully a longer, more detailed paper will be forthcoming.  What they report is a fragment screen that identified micromolar compounds.  These were then co-crystallized (not soaked) leading to several high resolution crystals.  Of particular interest was this fragment: This fragment should ring a bell it is part of the known inhibitor IBET151 from GSK (and is the known preferred binding motif for bromodomains).  Compound 1 binds in a similar fashion to JQ1, binding to the asparagine that recognizes the endogenous Ac-K.  This suggested to them that the isoxazole fragment could replace the triazole of JQ-1.  It's LEAN is 0.34 (33uM) and has a Binding Efficiency of 25.7.  This works describes the replacement of the triazole (Left) with the preferred isoxazole (Right).

Their SAR work is shown in the table below.  They were able to improve biochemical and cellular potency to that of the known inhibitors by replacing the sidechain with a carboxamate (Cpd 3).  the crystal structure of this compound showed similar binding to previously described isoxazoles.

They then went after the 4-chlorophenyl ring to see if they could modify the biophysical and three-dimensional properties of the molecule, while maintaining the potency seen with 3. 

A chloro scan around the ring showed that the o-Cl substitution was 10x less potent, but ortho-Me was tolerated, which led them to believe that it was a steric rather than electrostatic interaction. Overall, there was no better aromatic moiety for this position, and aliphatic moieties were definitely no good.  Compounds 3, 21 (phenyl), 22 (cyanophenyl), and 25 (aminopyridyl) were tested in in vitro ADME assays.  They showed good stability in human microsomes and generally stable (I am not ADME expert, so really what do they mean here?) in rat microsomes.  They showed high plasma protein binding in human plasma but negligible CYP inhibition.  Compounds 3 and 22 (cyano-phenyl) supported further profiling in rat PK experiments.  Compound 3 was superior to 22 and showed adequate exposure in mouse and showed excellent PK in dogs.  They were able to see a dose-dependent decrease of MYC.  MYC suppression was correlated with the amount of compound in the tumor and plasma.  

This is a really nice example of how fragments can be used to "scaffold hop", even if the entire scaffold is not changed.  Also, I think, based on the author list, this is a really good example of CRO-client collaboration.  There are many more out there I am sure, I just don't think we are aware enough of them.

The calm before the click in chitinase

In situ click chemistry is a topic we’ve covered before on Practical Fragments. Essentially, two ligands bind near one another on a target protein and react to form a linked molecule. There are several published examples, but it is not clear why it sometimes works and sometimes doesn’t. A new paper in Proc. Acad. Nat. Sci. USA by a team of Japanese and US researchers led by Satoshi Ōmura and Toshiaki Sunazuka at the Kitasato Institute in Tokyo addresses this question.

The researchers had previously discovered potent inhibitors of an antibacterial target enzyme called Serratia marcescens chitinase B, or SmChiB, using in situ click chemistry. In the presence of SmChiB, azide 2 reacts with alkyne 3 to yield triazole 4, which binds 26-fold more tightly than azide 2:

 

In the new paper, the goal was to use crystallography and computational chemistry to investigate how the reaction proceeds. To avoid azide 2 reacting with alkyne 3 in the crystal and so better visualize starting points, the researchers prepared the closely related alkene 5 mimic of alkyne 3. Unfortunately, due to its (unmeasurably poor) affinity, alkene 5 did not yield a co-crystal structure on its own.

The researchers were able to obtain a co-crystal structure of SmChiB bound to triazole 4 (green carbons below). Surprisingly, a co-crystal structure of azide 2 showed the molecule bound in a quite different orientation. However, a co-crystal structure of the ternary complex of azide 2 (cyan below) and alkene 5 (magenta) bound simultaneously to SmChiB revealed a close overlay of azide 2 with the corresponding fragment in triazole 4. Alkene 5 in the ternary complex adopted two conformations (the electron density is memorably described as resembling “a two-horned goat head”). As shown in the figure below, one of these orientations places the alkene moiety in close proximity to the azide moiety, primed for clicking.

Next, the researchers used this ternary structure to run high-level density functional theory calculations to determine the energetics of the click reaction and compared these with the same reaction run in water. The values were quite similar (if anything, the protein had a slightly higher activation barrier), suggesting that the protein was not directly catalyzing the reaction with specific amino acid side chains. Rather, the reaction was being accelerated simply by the preorganization of the azide and alkyne.

On the one hand, these results aren’t really a surprise: I think most people assumed that in situ chemistry works by bringing the reactants together rather than anything more exotic (with the odd exception). On the other hand, it is nice to see experiment match theory.

More generally, the results help to explain why in situ click chemistry is so challenging. The crystal structure of azide 2 and alkene 5 shows the relevant moieties quite close to each other, yet the reaction is still somewhat inefficient. Finding two fragments that not only bind near one another but are also oriented properly is likely to be a rare event.

Biophysics Conference (pt 3)

I have been giving my thoughts on the Novalix Conference on Biophysics in DD here and here.  Today's installment is on the "Emerging Technologies" and "Hits and Leads" section of the conference.  

Stefan Duhr- NanoTemper: Microscale Thermophoresis (MST) has been discussed here previously. Both Dan and I really like this technology.  This talk was an excellent overview of the theory.  Nanotemper claims that it has a dynamic range up to the mM range, however in their talk all of the examples were relatively, or very, tight binding complexes.  It has definite advantages in that it only uses 4 uL of sample/data point and it takes 40s/data point.  

There were a variety of talks on technologies that are definitely cool in a "Amazing they can do that" sort of way.  However, as an application to drug discovery, not so much.  There was a talk about Backscattering Interferometry (BSI), a switchable DNA chip (definitely cool tech, but with no discernible advantage over similar technology), Cryo-TEM (!), most of these talks I could not figure out how you would use in screening/FBHG.  However, the point of emerging technology is to emerge, so maybe in the near future there will be pretty boxes that have notable, robust discovery uses.

Chris Marshall -UToronto: This talk and Till's (below) were about GTPases.  This talk focused on a NMR-based GTPase assay.  What was particularly interesting was that they tethered their GTPase (Rheb) to a nanodisc, which should tumbling properties semi-independent of the nanodisc. This is a much more "biological" condition that many people typically use.  Other than that, this was a decidely academic talk.  In an organization with unlimited resources, and no time lines, you might follow the same approach as this group did.  In reality, I can't imagine you would.

Helena Danielson - Uppsala U/Beactica: This was a very interesting talk (per usual).  One key comment she made was: ease of use of a technology is NOT the same as ease of implementation.  In terms of Beactica's fragment library: 2000 compounds (from her slide) that are largely Voldemort Rule compliant.  It is enriched in known drug frameworks with diversity and scaffold representation (I am not sure what is meant by that).  For her first case study, they only used 930 fragments.  She didn't mention why a subset of the entire library was used.  She mentioned that they use an early biochemical screen as an orthogonal assay.  She spent a lot of time discussing the deconstruction of sensorgrams, in particular, if you have specific and non-specific binding contributing.  She also presented a case study against a GABA-A like receptor.  She then spent the rest of her talk discussing Chemodynamics: varying sample conditions, like temperature or pH. For BACE, for example, compounds need to bind at neutral and then acidic pH. 

Till Maurer- Genentech:  Till's talk was on k-RAS by NMR.  (As an aside, k-RAS has become a "hot" target largely due to this work.  Way back in 2003, we published a new method for NMR screening using k-RAS as one of our targets because it was so interesting we knew legal would let it go.)  Their fragment library had 3285 fragments (it is now 5000) biased towards high solubility for X-ray follow up in mixtures of 5.  Of 3285 fragments they found 1092 with a S/N >5.  Of these 266 confirmed (higher S/N threshold and other criteria) and were followed up by H-N HSQC.  Of 25 confirmed by HSQC, 6 produced crystals. 

Johannes Ottl- Novartis: The last talk of the conference was another really nice overview of the various biophysical methods and how they are applied in a few different case studies. 

So, what was the take home of this conference?  Biophysics is a rich and diverse toolbox.  However, in many cases we still don't know how to use these powerful tools prospectively, rather they are much more used retrospectively. 

Substrate activity screening for phosphatase inhibitors

Regular readers will be aware that there are lots of ways to find fragments, but one approach we haven’t covered yet is substrate activity screening, or SAS. A new paper in J. Med. Chem. by Jon Ellman and coworkers at Yale uses this technique to find inhibitors of striatal-enriched protein tyrosine phosphatase (STEP), which is implicated in cognitive decline in a variety of diseases.

Many enzymes can accept a wide range of substrates, and these are often fragment-sized. The basic idea behind SAS is that, since substrates (by definition) bind to a target, finding new substrates gets you new binders, and for some target classes it is straightforward to transform substrates into inhibitors. Of course, you could screen for inhibitors from the start, but the nice thing about looking for substrates is that you are far less likely to encounter artifacts. This is because artifacts normally muck up assays; it’s harder to envision a spurious substrate.

Phosphatases clip phosphates from their substrates. Protein tyrosine phosphatases (PTPs), for example, dephosphorylate tyrosine residues in proteins; they essentially perform the opposite reaction of protein tyrosine kinases. Like kinases, though, finding selective inhibitors can be challenging. The researchers started by building a small library of 140 phosphorylated fragments (previously described here) and looking for those that were particularly good substrates. One of the best for STEP was substrate 8, which looks quite different from phosphotyrosine.

Replacing the substrate phosphate group with a bioisostere (difluoromethylphosphonic acid) that could not be hydrolyzed by the enzyme gave compound 12, which had an inhibition constant (K_i) similar to the Michaelis constant (K_m) of substrate 8. Subsequent optimization led to compound 12s, with a low micromolar K_i and at least 18-fold selectivity against four other PTPs.

Unfortunately, the highly acidic phosphate bioisosteres in these molecules limit membrane permeability: although compound 12s inhibits STEP activity in rat neuronal cell cultures, it is not permeable in a model of the blood-brain barrier. Perhaps some of the less polar phosphate bioisosteres discovered in a previous virtual screen could help.

SAS is an interesting method, and I’m curious as to why more people aren’t using it. Of course, it does require generating bespoke libraries of fragment substrates, but once you have these they are useful for many members of a target class. What do you think?

Biophysics with White Wine (pt 2)

Tarte flambe or flammekuchen.  Doesn't matter what you call it, DELICIOUS!  Another fantastic find from the Novalix Biophysics in Drug Discovery Conference.  Yesterday I wrote up the Biophysical Characterization section, today's yummy-ness: Mechanistic Analysis. 

Ann Boriack-Sjodin -Epizyme:  She emphasized that X-ray is the key to Epizyme's work, but they also use STD, ITC, SPR, Fortebio, thermal shift, and enzymology.  This was a theme, especially for the non-fragment specific talks: we use any and all biophysical techniques.  This talk focused on the methyl-transferase DOT1L. They struck out with a diversity library and in silico screening.  They did find, with SBDD, a selective inhibitor.  They key to this compound was its VERY long residence time: 24 hours.  The concept of koff driven inhibitors was brought up in several other talks.

Glyn Williams -Astex: First off, let me say the best thing Glyn said during his talk was that the Ro3 was meant as a guideline.  His talk spoke about the variety of methods in use at Astex: MS, NMR, Thermal shift, ITC, and X-ray.  MS is used for protein validation and QC, thermal shift was used for affinity ranking, but they have moved away from it (his comment, "when it doesn't work, you don't know"), ITC is a good way to discriminate good compounds from bad, and NMR is used in competition mode.  In terms of their library, they have had 2600 fragments EVER and their current fragment library iteration has 1500 members.  Their core library has a avg MW of 176 (~13 HA) and clogP of 0.9 and the X-ray subset of 350 fragments 146 Da (~10 HA) clogP of 0.5.  40% of their fragments are NOT commercially available.  These are small fragments and he noted that on average each fragment has hit two targets.  Greater than 50% of their hits have never generated a X-ray structure, but have hit in the biophysics assays.  He presented how they do 3D-arity.  The draw a "best plane" through the molecule and then calculate the average deviation of each atom from that plane.  I found this approach unwieldy and I still think PMI is a better way to go.
They take several approaches to fragment screening: with their core fragment library they WATER-LOGSY and thermal shift which then goes into X-ray follow up ( with MS, ITC, and 2D-NMR).  If the X-ray works, they have a X-ray validated hit and it moves forward.  They also sometimes go straight into a X-ray screen (with a 350 fragment subset).  One of the advantages of the NMR-based screening is that NMR can detect hits < Kd, while X-ray can only detect hits > Kd. 
In terms of properties, he showed a fascinating graph (that I bet lots of people have) that shows that improvements in enthalpy occur during H2L and improvements in entropy during LO.  LogP occurs in H2L and stays the same in LO. 

Marku Hamalainen -HealthCare:  This was an interesting talk, especially when contrasted with Goran Dahl's.  He showed a very interesting graph (I am not showing slides without specific permission; I have asked for this one) that shows binding site occupancy as a function of on/off rates.  It is fascinating as it buckets your compounds in various regimes: "Ancient Medchem knowledge", "Without on you are off", "High affinity does not help if clearance is rapid", and "With slow off, you might still be on when the drug is gone". 

Goran Dahl - AZ: This talk was definitely in the "Yeah, of course" category.  Not to diminish his talk, which was excellent, but it makes sense in a only after someone points it out to you kind of way.  Kudos to him for saying it first (chronologically at least): koff does NOT correlate with PK.  Prolongation kicks in when koff< elimination rate.  Pure and simple, yet how many people had actually thought about it that.  Plasma t1/2/ residence time > 1, duration is driven by PK, < 1 and it is driven by binding kinetics. 

Geoff Holdgate -AZ: This was an excellent talk giving a high-level overview and then diving into some very interesting topics.  He spoke on combining thermodynamics and kinetics to drive chemistry.  Key Questions: "How do you improve medchem decisions with kinetic data?" One Kd can arise from many different kinetic profiles this would allow you to pick and chose one that could be beneficial, but how do you know what that would be?
 "Is biophysics simply useful for retrospection?"  There are no examples of the use of biophysical data to drive medchem prospectively. 
"Should you drive affinity/LO by Delta H only?"  From Glyn's talk, it seems like LO is driven by entropy, NOT enthalpy. 

His take home lesson, which I wholeheartedly agree with: the Drug Discovery paradigm of focusing on affinity needs to change. 

Biophysics in the Alsace

Two weeks ago, the first Novalix conference on Biophysics in Drug Discovery was held in Strasbourg.  I was lucky enough to be one of 160 people in attendance (this was largely a european affair, with ~10% of attendees from outside the EU).  The split of attendees was 60/40 industry/academia.  The conference was split into four themed sessions: Biophysical characterization, Mechanistic Analysis, Emerging Technologies, and Biophysical Methods for Identifying Hits and Leads.  This was not a fragment conference, but many of the talks were specifically about fragments, and the rest could be impactful in fragments.  I want to share my impressions/thoughts on the speakers relevant to the readers here.  You can also go to my website to see my thoughts on the speakers not relevant to FBHG. 

Michael Hennig- Roche: His talk discussed the various methods and showed examples for each.  This was a great talk giving a great overview of the various methods available for active follow up.  Specifically fragments: The Roche fragment library is ~5000 compounds.  In terms of QC, 80% of the samples show >85% purity (by LC-UV-MS).  Purity of fragment libraries has been discussed here previously.  For Roche's uses, every fragment hit is followed up by MC and NMR, so a lower threshold of purity will not have a negative impact.  He also presented results from a 2D-HTS.  This was a new concept for me and I found it intriguing.  The basic concept is to graph the results from two screens (or related proteins) to identify compounds that activate one, but not the other, or activate one and stimulate the other, etc.  He also presented direct and in-direct methods using Mass Spec methods.  To me, this area was one of the more fascinating areas discussed at the conference.  Theoretically, this could be applicable to fragments, but I would really like to see specific applications.  Lastly, he spoke on biophysical methods and membrane proteins. 

Rob Cooke- Heptares:  He presented the STaR approach that has been widely published and presented here, here, here, and here.  The talk was very similar to other talks that Heptares and Rob have presented in various fora over the past year.  The main thing that I was taken by was that there was no mention of NMR.

Matthias Frech - Merck: I really enjoyed this talk.  One of the main things I noted was his use of the phrase "hit affirmation".  Confirmation (according to the dictionary) is a piece of corroboration, while affirmation means it is true.  Is this parsing meaning where none exists?  Maybe, but I think it may also inform on mindset.  He said that SPR is the workhorse for FBHG, but they also use NMR, MST, ITC, stop-flow, and X-Ray.  95-98% of their projects are accomplished using SPR and ITC.  However, he stated that SPR is used to rule out compounds, not rule them in.  This is key to the proper use of SPR.  I would be interested to see if anyone else takes this approach.  They use SPR and ITC to obtain the enthalpic and entropic terms for compound binding.  ITC yields the enthalpy, SPR yields the DeltaG et voila, simple math (my favorite kind) yields the entropic term.  One other very interesting item that he noted was that there was no correlation between affirmation rate and target class for 31 projects they undertook (2009-2012).  

 

Tomorrow I will update the Mechanistic Analysis session.  

Fragment merging revisited: CYP121

Last year we highlighted a paper from Chris Abell and colleagues at the University of Cambridge in which they applied FBLD to CYP121, a potential anti-tuberculosis target. Several fragments with different binding modes were identified, and while some could be successfully merged to produce higher affinity binders, others couldn’t. In a new paper in ChemMedChem, the researchers take a closer look at why some of their initial attempts at fragment merging failed, and figure out how to succeed.

In the original paper, fragment 1 was particularly interesting for two reasons. First, crystallography revealed that it did not make direct interactions with the enzyme’s heme iron, as do most inhibitors of CYPs, suggesting that higher specificity might be achievable. Moreover, the co-crystal structure revealed that fragment 1 could bind in two nearly overlapping orientations, practically begging to be merged. Unfortunately, the resulting merged compound 4 actually bound worse than the initial fragment.

Computational modeling suggested that a primary reason for this disappointing result is the steric clash between two hydrogen atoms on the two phenyl rings of compound 4. These are forced into an unfavorable configuration when the molecule binds the protein. To fix this, the researchers sought to introduce a new interaction with the protein that would allow the molecule to relax into a lower energy conformation, alleviating the steric clash. This led to compound 5, with a satisfying increase in affinity. But lest folks become too cocky, an attempt to pick up an additional hydrogen bond to the protein (compound 8) actually led to a decrease in affinity despite the presence of the designed hydrogen bond, as assessed by crystallography. More successfully, building into a cavity led to the most potent compound 9. (Geeky aside: the aminopyrazole versions of fragment 1 had similar affinities as fragment 1, suggesting that the aminopyrazole moiety per se only gives a boost in potency in the context of the merged molecule.)

High-resolution co-crystal structures were solved for several of the molecules; the figure below uses color-coded carbons to show the overlay of the two different binding modes of fragment 1 (green), compound 4 (cyan) and compound 5 (magenta). What’s striking is how closely all the molecules superimpose, despite their very different affinities.

This is a nice case study in fragment merging that emphasizes just how difficult the strategy can be, even when it looks like it should be easy. And while Practical Fragments has not always looked kindly on computational methods, this is a beautiful example of how modeling can be used to understand why things that look good on paper don’t work, as well as how to fix them.