23 November 2015

Fragments vs DAPK3, computationally and experimentally

Computational approaches for discovering hits often involve sorting through many possibilities and examining a few closely. With luck, some of the predicted molecules will bind to the protein of interest. However, these don’t always bind for the “right” reason: sometimes a fragment predicted to bind one way will turn out to bind in quite a different manner. A recent Angew. Chem. Int. Ed. paper by Gisbert Schneider and colleagues at the ETH in Zürich and SARomics in Lund reports a possible example.

The researchers were interested in death-associated protein kinase 3 (DAPK3), which is implicated in several diseases. Previous work had shown that fasudil inhibits this kinase, though it hits others as well. Fasudil was used as a starting point for de novo fragment discovery using software called DOGS (Design of Genuine Structures). This is a scaffold-hopping approach in which virtual chemistry is used to generate readily accessible alternatives to a starting molecule. In this case, 347 of the 521 suggested inhibitors were fragment-sized. These were prioritized using in-house software, and compound 2 – one of the top hits – was chosen for synthesis and characterization.

Happily, compound 2 turned out to be fairly potent for its size, with impressive ligand efficiency. It is also quite different from fasudil (Tanimoto similarity = 0.16). Indeed, while fasudil is likely to be positively charged at physiological pH, compound 2 is likely to be negatively charged. Moreover, of 27 other kinases tested, compound 2 hit only one other with similar potency.

For those who have worked on kinases, compound 2 does appear unusual. A crystal structure of this molecule bound to DAPK3 revealed that it sits in the ATP-binding pocket but without making any conventional hydrogen bond interactions to the so-called hinge region of the kinase. Although no reported crystal structures show fasudil bound to DAPK3, structures with other kinases reveal the nitrogen of the isoquinoline moiety making a hydrogen bond to a backbone amide in this part of the protein.

The software used to prioritize compound 2 is based not on docking but on machine learning using the ChEMBL database, and the researchers were interested in what else this fragment might inhibit. Not surprisingly given the aryl sulfonamide moiety, several carbonic anhydrases came up, and two were confirmed experimentally.

Interestingly, the diuretic drug azosemide, whose physiological target is unknown, contains compound 2 as a substructure, and the researchers found that this molecule inhibits DAPK3 with low micromolar affinity. It also binds human carbonic anhydrase IX with similar affinity. The researchers suggest that these targets could at least partially explain the mechanism of the drug, as well as some of its side effects. It would be interesting to see cell data against these two targets, as well as the crystal structure of azosemide bound to DAPK3.

The ability to predict biological targets of molecules with the aid of machine learning would clearly be valuable (see also here). And of course new approaches for scaffold hopping are always valuable. In this case DOGS did retrieve an active (albeit odd) molecule when fed a conventional kinase inhibitor; it is as if you threw a ball and your dog fetched a slipper. I will be curious to see this applied to more systems.

16 November 2015

Fragments vs PDE10A: growing potency and selectivity

People often wonder how selective fragments need to be. According to molecular complexity theory, the answer is “not very”. After all, it would be hard to get a decent hit rate with a library of just a few thousand fragments if they were too selective. In the case of kinases, experimental studies support this theory. Indeed, a single fragment has given rise to several drugs – one of which is approved. In a new paper in J. Med. Chem., William Shipe and colleagues at Merck demonstrate the utility of a non-selective fragment for another class of enzymes, phosphodiesterases (PDEs).

The human genome contains more than 50 different PDEs, which cleave phosphodiester bonds. PDE10A hydrolyzes cyclic guanosine monophosphate (cGMP) and cyclic adenosine monophosphate (cAMP) and is a potential target for schizophrenia. It has been pursued extensively, both with fragments (see for example here and here) as well as more traditional approaches.

The researchers started with a biochemical assay that screened each fragment at 200 µM; 60 of the 1600 tested gave > 80% inhibition. Nine of these were soaked into PDE10A crystals, producing seven structures, including compound 5, with impressive potency and ligand efficiency. Initial SAR by catalog led to the even more potent compound 6, which revealed that an amino group was tolerated and pointed nicely towards another pocket, offering a way for further elaboration.

Fragment growing from the amino group was accomplished through several rounds of parallel synthesis, with crystallography used to understand and optimize the binding interactions. Compound 9s showed particularly impressive low nanomolar potency, as well as at least 80-fold selectivity against nine other PDEs. In contrast, the initial fragment 5 was at most only 11-fold selective against any of the other PDEs.

Previous work with PDE10A had revealed another “selectivity pocket” nearby,  and the researchers further grew their molecule towards this, leading ultimately to compound 15h, with low picomolar affinity and at least >5900-fold selectivity against nine other PDEs. The compound also showed functional activity in a rat model, though it suffered from suboptimal pharmacokinetic properties.

This is a beautiful illustration of the power of combining fragment screening, structure-based drug design, and parallel synthesis. The researchers were able to gain more than a million-fold improvement in potency and take a marginally selective fragment to a highly selective lead. Of course, there is still plenty of work to do, and it will be fun to watch this story unfold.

09 November 2015

Group efficiency

Ligand efficiency (LE) is one of the more controversial topics we cover at Practical Fragments. One critic asserted – incorrectly – that it is mathematically invalid. Another has stated that it is “not even wrong,” because the metric is predicated on standard state conditions and thus "arbitrary". (As he acknowledges, this also applies to the value and even the sign of the Gibbs free energy for a reaction.) A related metric that has received less attention is group efficiency (GE). In a paper just published in ChemMedChem, Chris Abell and colleagues at the University of Cambridge use this to help them optimize pantothenate synthetase (Pts) inhibitors.

Ligand efficiency is defined simply as the free energy of binding divided by the number of non-hydrogen, or “heavy” atoms (often abbreviated as HAC for heavy atom count) in the ligand. (Geek notes: although the binding energy is negative, LE is expressed as a positive number, so LE = - ΔG / HAC. Also, on Practical Fragments, units are assumed to be kcal mol-1 per heavy atom unless otherwise stated.)

Instead of focusing on a single ligand, group efficiency compares two ligands that differ by the presence or absence of a given group of atoms. To calculate GE, you simply subtract the ΔG values for the two ligands and divide by the number of heavy atoms in the group. For example, if you add a methyl group to your molecule and are lucky enough to get a 100-fold pop in potency, the methyl group has a group efficiency of 2.7 kcal mol-1 per heavy atom.

The current paper chronicles lead discovery for Pts, a potential target for tuberculosis. Previous screening efforts followed by fragment growing and fragment linking had generated low micromolar and high nanomolar inhibitors. The researchers turned to group efficiency to improve their molecules further.

As expected from ligand deconstruction studies (see for example here, here, and here), different portions of a molecule are likely to have vastly different group efficiencies. Indeed, this turned out to be the case here: the acetate moiety had high group efficiency, whereas the pyridyl moiety had lower group efficiency. Thus, the researchers set out to replace the pyridyl with ten diverse substituents. Happily, one of these improved the dissociation constant to 200 nM as assessed by isothermal titration calorimetry of the fully elaborated molecule. Compound 11 also showed reasonable enzyme inhibition in a functional assay.

One potential problem with group efficiency is that it assumes the molecules being compared bind in a similar fashion, which is not always a safe assumption. In this case, the researchers obtained a crystal structure of compound 11 bound to the enzyme, which not only revealed that it binds similarly to compound 5, but also suggested that inserting a methylene may improve binding. The resulting compound 20 showed better activity in the inhibition assay, as well as activity against M. tuberculosis in a cell assay (though unfortunately the dissociation constant was not reported).

This paper offers a clear illustration of how group efficiency can be useful for prioritizing which portions of a molecule to change. In some cases, such as the example here, it makes sense to try to replace groups with low group efficiency. On the other hand, the core fragment may bind in a hot spot, and so just a slight tweak can dramatically boost potency. As with lead optimization in general, there are many paths – both to enlightenment and to perdition.

02 November 2015

NMR poll results

The results of our latest poll are in – thanks to all who participated! Of the 119 people who responded to the first question, 87% said they use NMR for finding or validating fragments. Even if we assume that responses were biased towards NMR aficionados, big magnets are clearly popular.

The second question asked about specific NMR techniques. If everyone who said they used NMR in the first question also answered the second, this means the average user applies more than 3 different techniques; I’ll let Teddy weigh in to see whether this matches his experience.
One surprise for me was that, although many techniques are widely used, none are nearly universal; even the most popular methods seem to be used by just over half of respondents.

Among ligand-detected methods (blue in the figure), STD ranks at the top, with line-broadening, WaterLOGSY, and fluorine-based techniques all tied for second place.

Protein-detected methods (red in the figure) also appear quite healthy, with nearly as many respondents using 15N-HSQC/HMQC as STD.

Finally, 11 of you said you use "other" techniques. We didn't include TINS, even though it seems quite useful, because it is only available through the services of ZoBio. But what else is out there?

28 October 2015

Hidden gem of a finding or not?

Todays paper is from a group in Korea.  It's a typical "we did some in silico screening, limited biochemical testing, made a compound or two, and voila!" paper.  In this case, the target is Tyk2 (the target of Xeljanz).
Figure 1.  Xeljanz (tofacitinib)
2000 diverse fragments were selected from the Otava library and docked against Tyk2.  64 top ranked fragments were selected and 9 were selected that had inhibition over 50% at 100 microM, with the best compound (1) having 60% inhibition at 3 microM.  

Figure 2.  Cpd 1 docked to Tyk2. 
What I don't like here is that they didn't do full dose-response curves.  That seems lazy.  Also, the only structures they show are the docked structures.  Maybe its just me, but show me some line drawings.  They then did some limited SAR (3 cpds) based on 1 as the scaffold.  Cpd 12 was the best compound 
Figure 3.  Cpd 12
(10nM IC50).  In the end, 12 was equipotent (or superior) with tofacitinib in terms of shutting down Tyk2/Stat3 signalling.  However, they could not rule out that this is due to non-specific inhibition of other JAK proteins. So, is this a great result?  If so, why BOMCL (not to be snobby)?

26 October 2015

Fragments in the clinic: PLX3397

Practical Fragments covers a wide variety of journals. J. Med. Chem., Bioorg. Med. Chem. Lett., Drug Disc. Today, and ACS Med. Chem. Lett. are all well-represented, but we also range further afield, from biggies such as Nature and Science to more niche titles such as ChemMedChem, Acta. Cryst. D., and Anal. Chim. Acta. The increasingly clinical relevance of fragment-based approaches is highlighted by a recent paper by William Tap and a large group of collaborators appearing in the New England Journal of Medicine. This reports on the results of the Daiichi Sankyo (née Plexxikon) drug PLX3397 in a phase I trial for tenosynovial giant-cell tumor, a rare but aggressive cancer of the tendon sheath.

The story actually starts with a 2013 paper by Chao Zhang and his Plexxikon colleagues in Proc. Nat. Acad. Sci. USA. The researchers were interested in inhibiting the enzymes CSF1R (or FMS) and KIT; both kinases are implicated in cancer as well as inflammatory diseases. The team started with 7-azaindole, the same fragment they used to discover vemurafenib. Structural studies of an early derivative, PLX070, revealed a hydrogen bond between the ligand oxygen and a conserved backbone amide. Further building led to PLX647, with good activity against both CSF1R and KIT. Selectivity profiling against a panel of 400 kinases revealed only two others with IC50 values < 0.3 µM. The molecule was active in cell-based assays, had good pharmacokinetics in mice and rats, and was active in rodent models of inflammatory disease.

The new paper focuses on the results of a clinical trial with PLX3397, a derivative of PLX647. Despite its close structural similarity to PLX647, it binds to CSF1R in a slightly different manner. Both inhibitors bind to the inactive form of the kinase, but PLX3397 also recruits the so-called juxtamembrane domain of the kinase to stabilize this autoinhibited conformation. Pharmacokinetic and pharmacodynamics studies in animals were also positive.

Tenosynovial giant-cell tumor seems to be dependent on CSF1R, so the researchers performed a phase 1 dose-escalation study with an extension in which patients treated with the chosen phase 2 dose were treated longer. Of the 23 patients in this extension, 12 had a partial response and 7 had stable disease. A quick search of clinicaltrials.gov reveals that PLX3397 is currently in multiple trials for several indications, including a phase 3 trial for giant cell tumor of the tendon sheath.

Several lessons can be drawn from these studies. First, as the authors note, one fragment can give rise to multiple different clinical candidates. Indeed, in addition to vemurafenib, 7-azaindole was also the starting point for AZD5363. This is a good counterargument to those who believe that novelty is essential in fragments.

A second, related point is that selectivity is also not necessary for a fragment. The fact that 7-azaindole comes up so frequently as a kinase-binding fragment has not prevented researchers from growing it into remarkably selective inhibitors. An obvious corollary is that even subtle changes to a molecule can have dramatic effects: the added pyridyl nitrogen in PLX3397 is essential for stabilizing a unique conformation of the enzyme.

Finally, careful patient selection is critical to answering biological questions. I confess that I had never heard of tenosynovial giant-cell tumor, nor the role of CSF1R, but I’m glad others had. I look forward to seeing an increasing stream of fragment papers in clinical journals.

21 October 2015

LO-MS...Coming of Age.

There are many ways to screen for fragments.  One of the really emerging areas uses mass spec detection: WAC, native mass spec, HDX, and ligand-observed MS.  The group that we highlighted back in March has a new paper where they look at the method in terms of accuracy of Kd determination and compare the results to other biophysical methods.  

Their previous work they looked at relative affinity ranking of bound fragments.  In this study, they compared the accuracy of Kd determination in this method to ITC.  First they used a pool of 3 or 4 known CAI inhibitors and a pool of 50 fragments (from their collection).  The conditions were defined:
The hCAI protein was incubated with each inhibitor mixture in the binding buffer with a total volume of 50 mLat room temperature for 40 min. The protein concentration was maintained at 25 mM and the inhibitor concentration increased from 1 mM to 50 mM. The control was prepared by using the binding buffer substitute for hCAI during incubation. The incubation solution was then filtered through a 10 kDa MW cutoff ultrafiltration membrane by centrifugation at 13,000 g for 10 min at 4 C followed by a quickwash with 10mM ammonium acetate (pH 8.0) to remove the unbound compounds.
 Two different methods were used to calculate Kd: 1. saturation curves and 2. measuring the unbound fraction of ligand .  Method 1 was deemed unsuitable for determining Kds for ligands with largely different Kds.  Method 2 did not depend on saturation curve fitting, instead using a calibration curve and did not observe any fragment competition at higher P:L ratios (6:1 or 8:1).  This approach also improved the sensitivity of the assay allowing better detection of lower affinity ligands.  Kds determined using this method matched those determined by ITC.  

To further test the method, they ran a pool of 50 fragments against HCV RNA polymerase NS5B.  This gave, as expected, a complicated chromatographic baseline.  To exclude promiscuous binders, they ran against BSA in parallel.  Eight fragments in the mixed pool showed selective binding to NS5B using unbound fraction analysis vs. 2 from the bound fraction analysis.  7 of 8 fragments were confirmed by SPR (ITC could not be used).  The 1 BFA fragment to be analyzed by SPR showed that it was a very weak binder.  

This sort of work makes me happy.  Methods always need to be pushed and evaluated.  When evaluating methods, if this sort of cross validation hasn't been done, question as to why not? 

19 October 2015

Fragments vs Trypanosoma cruzi spermidine synthase, allosterically

Chagas disease, caused by Trypanosoma cruzi, is spread throughout Central and South America by a nasty blood-sucking insect. A couple drugs are approved to treat it, but they can cause severe nausea and peripheral neuropathy, so there is room for improvement. In a recent paper in Acta Cryst D., Yasushi Amano and colleagues at Astellas Pharma describe their efforts against the T. cruzi spermidine synthase (TcSpdSyn).

TcSpdSyn transfers an aminopropyl moiety from the cofactor decarboxylated S-adenosylmethionine (dcSAM) to the evocatively-named putrescine (1,4-diaminobutane) as one step in the synthesis of an essential antioxidant. Small amines can bind in the putrescine-binding pocket and inhibit the enzyme with low micomolar activity, so the researchers decided to find other fragments that could bind in this pocket. They screened in the presence of dcSAM, using surface-plasmon resonance (SPR), with each fragment present at 0.25 mM, as well as in thermal shift assays, with each fragment present at 2 mM. Although nothing is reported about library size or hit rate, hits from either assay were taken into crystallography, resulting in six structures described in detail and deposited in the Protein Data Bank (pdb).

Two fragments were found that bind in the putrescine-binding pocket, and in both cases the enzyme shows some conformational changes to accommodate the fragments. Although these two fragments have only modest potency (IC50 = 0.18-0.48 mM), they do have satisfying ligand efficiencies, and are good starting points for structure-based design.

Unexpectedly, the other four fragments bound not in the putrescine-binding pocket but at an interface between two proteins of TcSpdSyn, which forms a homodimer. One of these fragments, an isothiazolinone, showed mid-nanomolar activity in a functional assay. Readers may recall a paper we pilloried earlier this year which also reported an isothiazolinone as a screening hit. In that case, the researchers failed to recognize that this PAINS compound has ample precedent for reacting with thiols. Happily, in the current paper the researchers are not only aware of this, they actually see covalent bond formation between the fragment and a cysteine residue in the crystal structure. Interestingly though, the fragment reacts with only a single cysteine residue at the dimer interface, despite the presence of six other cysteine residues in the protein.

The researchers carefully analyzed this structure and found that binding of the fragment disrupts the putrescine-binding pocket; in other words, the fragment is an allosteric inhibitor. Moreover, the other three fragments that bind at the dimer interface also appear to act allosterically, and one of them is a single digit micromolar inhibitor.

This is a nice example of how even PAINS compounds can be useful if they are well-characterized and not hyped. Moreover, the structures suggest new approaches for tackling a target for a neglected tropical disease, either covalently or more conventionally.

14 October 2015

Magic Methyl and SBDD

As sites have closed down, we have seen a fair number of paper come out describing work at various sites.  Today's paper is another of these, from Roche, Nutley.  The target is Tankyrase, blogged previously here.  

This group started with a biochemical screen of their in house fragment library (here for analysis of their library) against both TNKS1 and 2.  This screen resulted in two compounds: 1. a pyranopyridone and 2. a benzopyrimidone (that looks like the first published TANK inhibitor).  
Figure 1.  Fragments identified from biochemical screen.
They were able to co-crystallize 1 with the TANKS2 and then were able to model 2's binding based upon the known inhibitor.  This pleasingly revealed both similarities and differences in the binding.  the main recognition elements are largely the same.  A big difference is that the phenyl maintains hydrophobic interactions that the cyano group does not. 
Figure 2.  TANKS2 X-ray structure with 1 (orange carbons) and 2 (yellow carbons). 
This lead to the obvious decision to merge the two fragments leading to fragment 3. This compound was reasonably potent (320 nM), however it should moderate to high clearance in an in vivo PK study. 
Compound 3
Then of course, the medchem kicks in aiming to decrease cLogP and increase solubility by focusing on two areas: the fused phenyl ring and the isopropyl side chain. This work was able to achieve their goals, but they also stumbled on a "magic methyl" (9) which improved potency 100 fold.  
9.  Magic methyl on fused phenyl ring.
This magic methyl works by sliding into a little pocket defined by three tyrosine residues. 
Figure 3.  Magic methyl binding mode. 
The t-butyl alcohol derivative of 9 had excellent properties: reasonable solubility, excellent permeability, stopped axin degradation in cells in a dose dependent manner, prevented mRNA production of beta-catenin dependent genes, and in mouse had satisfactory in vitro activity and PK profile.  

This is an excellent example of FBLG.  The magic methyl does exist and X-ray is a highly enabling technology. 

12 October 2015

What works for crystallography?

As a recent post emphasized, crystallography is a key technique for fragment-based lead discovery. We’ve occasionally touched on things that can go wrong in crystallography, but in a recent paper in Drug Discovery Today, Helena Käck and colleagues at AstraZeneca (Mölndal) put things in a more positive light by asking what factors lead to success.

The paper starts with a literature review of successful fragment structures published between 2012 and 2014 and summarizes some of the key findings. First is the need to easily generate robust crystals that diffract well and are stable for long periods of time. If the ligand-binding site is known, it is important that this is accessible and not occluded by protein or ligands. Finally, the crystals should be stable when soaked in high concentrations (> 10 mM) of ligand, ideally in the presence of 10% DMSO.

None of these factors will come as a surprise to experienced crystallographers, but the authors do a nice job of concisely summarizing them as well as providing solutions to common problems. For example, the use of surrogate proteins can help in cases where the target itself is hard to crystallize. Proteins can be grown in the presence of known ligands, which can then be soaked out. And various additives can also help.

All of this is nice, but what really makes this paper noteworthy is the second part, in which the authors discuss their own experience with soluble epoxide hydrolase (sEH), a potential cardiovascular and immune target that we’ve previously discussed here, here, and here. This protein seems to have all the hallmarks of technical success. Indeed, both HTS and fragment screens at AstraZeneca produced high hit rates, and 65% of hits taken into crystallography produced structures. In all, 55 structures were determined, with ligands ranging in size from 130 to 540 Da and affinities ranging between 0.003 and 600 µM. Of these, 38 could be considered fragments. As seen before, the protein is relatively rigid, and the ligands bind in a variety of subsites within the large lipophilic active site.

With so much data, the researches asked whether ligand properties could predict crystallographic success. The most robust correlation was seen with affinity: 94% of compounds with affinities below 0.1 µM produced structures, while only 36% of compounds with affinities above 100 µM did.

Ligand efficiency (LE) was also correlated with crystallographic success, though three small fragments (MW < 160 Da) with very high LE values did not produce structures – a phenomenon which has been noted by others.

In contrast to another recent study that compared many fragment-screening approaches, solubility did not predict success. The researchers suggest that this is because crystal conditions are so different from the conditions under which standard solubility measurements are run.

Admirably, the structures of 52 of the ligands are reported in the supplementary material – along with their measured affinities – and the resulting crystal structures have been deposited in the protein data bank. Some of the ligands bind at multiple sites and some have dual conformations; these ambiguities are noted. Moreover, a set of inactive analogs has also been included. Together with smaller sets of previously released structures, this provides a bonanza of structural and affinity data with which to benchmark computational docking programs. Hopefully we’ll see more of this public sharing of data.

07 October 2015

Fragment finding smackdown: 2015 edition

Our current poll (right-hand side of page) asks about NMR. But of course, there are lots of other ways to find fragments, and the question often arises as to which ones are best. This is the subject of a recent paper in ChemMedChem by Gerhard Klebe and collaborators at Philipps University Marburg, Proteros, NovAliX, Boehringer Ingelheim, and NanoTemper.

Long-time readers will recall that the Klebe group assembled a library of 361 fragments, some of which violated strict “rule of 3” guidelines. These were screened in a high-concentration functional assay against the model aspartic protease endothiapepsin, resulting in 55 hits, of which 11 provided crystal structures. The authors wondered how other techniques would fare. In the new paper, they retested their entire library against the same protein using a reporter displacement assay (RDA), STD-NMR, a thermal shift assay (TSA), native electrospray mass spectrometry (ESI-MS), and microscale electrophoresis (MST). To the extent possible they tried to use similar conditions (such as pH) for the different assays, though the fragment concentrations ranged from a low of 0.1 mM (for ESI-MS) to a high of 2.5 mM (for TSA), while protein concentrations ranged between 4 nM (for the biochemical assay) to 20 µM (for ESI-MS).

All told, 239 fragments hit in at least one assay – a whopping hit rate of 66%. Actually, the number is even higher since, for various reasons, not all fragments could be tested in all assays. And yet, not a single fragment came up in all of the assays! Overall agreement was in fact quite disappointing, with most methods having overlaps of less than 50%, and often below 30%. This is in contrast to a study from a different group highlighted a couple years ago.

What’s going on? One clue might be the solubilities, which were experimentally measured for all library members. In general, hits tended to be more soluble than the library as a whole, emphasizing the importance of this parameter not just for follow-up studies but for identification of fragments in the first place.

Another possibility is that some fragments bind outside the enzyme active site, and thus would not be picked up in a biochemical assay or the RDA. Some evidence for this is provided by follow-up NMR studies in which hits were competed with ritonavir, which binds in the active site. Ritonavir-competitive binders shared greater overlap with biochemical and RDA hits, while there was more overlap between ritnovair-uncompetitive binders and hits from methods such as ESI-MS, TSA, and MST that rely solely on binding. (This could also explain similar observations made earlier this year.)

If a picture is worth a thousand words, how many of the 11 hits that had previously yielded crystal structures would have been identified had they been tested in other methods? Here the numbers vary significantly, from 27% for ESI-MS and MST to 100% for NMR, though these statistics should be taken with a grain of salt since – for example – only 7 of the 11 crystallographically-confirmed hits could actually be tested in the NMR assay. Also, it is possible that some hits from these methods might have generated new crystal structures for fragments not identified in the initial biochemical screen.

One admirable feature of this paper is that the authors provide all their data, including structures and measured solubility numbers for each component of their library. This should provide an excellent dataset for a modeler to use in benchmarking computational methods.

All in all this is a thorough and important analysis and a sobering reminder that, even if a fragment doesn’t hit in orthogonal assays, that doesn’t necessarily mean it’s not a useful starting point. On the other hand, artifacts are everywhere, and paranoia is often justified. The art is deciding which hits are worth pursuing – and how.

05 October 2015

Uninteresting GPCR Fragment Work...meant as a Compliment!!

There are certain movies that when they are on TV, I can't not watch.  I call these Broken Leg Movies (as in if I were laid up with a broken leg what would I watch).  As I have said, Road House is one, Apollo 13 another.  Its about America's blase attitude towards the amazing feat of putting men on the moon.  It takes a potential horrific tragedy (For those off you who haven't seen it, let me say (**Spoiler Alert**) don't worry it has a happy ending.) in order for America to care about men in space.  Which of course is in direct contrast to Pigs in Space (with Swedish Subtitles)! 

One of the field changing technologies is Heptares' STAR technology (for creating stabilized, soluble GPCRs).   We have discussed it often on this blog.  Well, they are back with another paper, this time working the voodoo they do so well on a Class C GPCR.  Negative allosteric modulation of the mGluR has the potential for significant medical impact in a variety of diseases.  In a relatively well trod drug space (there have been several molecules in late stage trials), an issue appears to be the acetylenic moiety in these drugs (which appears to be manageable).  So, non-acetylenic molecules would be desireable.  

To attempt to ligand this molecule, they screened 3600 non-acetylenic fragments using a radio-labeled assay.  This is in contrast to previous work where they used SPR. From this screen, 178 fragments were tested in concentration-response curves leading to "a number of promising" hits, including the compound shown below. 
Cpd 5.  pKi=5.6, LE=0.36
This compound was advanced using the tools you would expect (especially from Heptares): modeling, X-ray crystallography, medchem, and so on.  The final molecule is an advanced lead with excellent mGluR selectivity and in vivo activity, clean tox, and so on. 

This is excellent work, but "yawn".  I think it might be interesting to hear why they went with the radioligand approach, as opposed to SPR.  You could quibble that 5 is too big to be a fragment, but really?  Papers like this are uninteresting, we know its going to work.  The science is excellent, but I want to see the triumph out of tragedy.  Not here.  I want to congratulate Heptares for making an achievement like this paper perfectly uninteresting.  And I mean uninteresting as the very best of compliments. 

01 October 2015

Aggregation alert

Practical Fragments has quite a few posts about PAINS, or pan-assay interference compounds. In part this reflects their sad prevalence in the literature, but it’s also fair to say that they are easy targets because many are readily recognizable.

But not all artifacts are so easily spotted, as discussed in a new paper just published in J. Med. Chem. by John Irwin, Brian Shoichet, and colleagues at the University of California San Francisco (see also here for Derek Lowe's excellent summary).

The researchers took on one of the most insidious problems, compound aggregation, in which small molecules form colloids that bind to and partially denature proteins, causing false positives in all sorts of assays. This can happen even at nanomolar concentrations of compound, and is all the more problematic at higher concentrations used in fragment screening and early hit to lead optimization. In many cases aggregates can be disrupted or passivated by including nonionic detergents such as Triton X-100 or Tween-80, but not all assays tolerate detergent, and some aggregates form even in the presence of detergent.

Worse, all sorts of molecules can form aggregates, including many approved drugs. Previous attempts to try to predict which molecules will aggregate have not been very successful. Colloid formation is essentially a phase transition, and like other such transitions (crystallization, for example) it is fiendishly difficult to predict what molecules will do this under what conditions. But if we can’t predict from first principles which molecules will form aggregates, can we at least draw empirical lessons?

The researchers assembled a set of >12,600 known aggregators and put together a very simple model that assesses how similar a molecule of interest is to one of these aggregators (using Tanimoto coefficients, or Tcs). Aggregators have a wide range of physicochemical properties, with ClogP values from -5.3 to 9.8, but 80% have ClogP> 3.0. The team hypothesized that a molecule sufficiently similar to a known aggregator – and also somewhat lipophilic – would have a higher probability of being an aggregator than a molecule chosen at random.

To test this idea, the team took  a batch of 40 molecules and tested them for aggregation. Among those most similar to known aggregators (Tc ≥95%), 5 of 7 molecules were confirmed as aggregators. This fell to 10 of 19 for the next set (Tc 90-94%), 3 of 7 after that (Tcs 85-89%) and only 1 of 7 for the least similar (Tcs 80-84%). Thus,Tc ≥85% was chosen as the cutoff.

Next, the researchers examined molecules that had been reported as active in some sort of biological assay, and found that 7% were ≥85% similar to a known aggregator and had ClogP> 3. Ominously, this rate is an order of magnitude greater than the number of commercially available compounds that also fit these criteria. More damning, most of this enrichment has occurred since 1995, when high-throughput and virtual screening really went mainstream. In other words, the past couple decades have seen a sizable enrichment of potential aggregators in the literature.

All of this is fascinating, but what really makes this paper significant is that the researchers have made all their primary data available, and also built a simple to use website called “Aggregator Advisor”. Just draw your molecule or paste a SMILES string to generate a report. For example, entering gossypol tells you that this molecule has previously been reported as an aggregator. (With two catechol moieties, it’s also a PAINS.) Perhaps not coincidentally, it shows up in more than 1800 publications.

Of course, as the researchers note, “just because a molecule aggregates, under some conditions, in the same concentration range as it is active, does not establish that its activity is artefactual.” Indeed, 3.6% of FDA-approved drugs are known aggregators. Still, particularly if your hit has only modest activity (0.1 µM or worse), similarity to a known aggregator should at least make you cautious.

The researchers are at pains to emphasize that their model is “primitive and subject to false negatives and false positives.” Thus, any hits need to be tested to see if they behave pathologically in any given assay. More importantly, a molecule that comes up as a negative should not be presumed to be innocent.

All these caveats aside, Aggregator Advisor is very easy to use. It’s certainly worth running the next time you find an interesting molecule – whether in your lab or in the literature – particularly if there was no detergent in the assay.

28 September 2015

NMR poll!

Among fragment-finding techniques, nuclear magnetic resonance (NMR) ranks near the top. Protein-detected methods, like HSQC/HMQC-based SAR by NMR, helped usher in fragment-based drug discovery as a practical endeavor. More recently, ligand-detected methods such as line broadening (or CPMG), STD, and WaterLOGSY appear to have gained the edge. There are also more boutique methods, such as ILOE and spin labeling. And of course, some people proudly embrace their fluorine fetishism.

So what’s your favorite flavor? Now's your chance to weigh in on our latest poll (on the right). The first question asks whether you use NMR, and the second asks which methods you use. PLEASE ANSWER BOTH QUESTIONS - the free version of Polldaddy doesn't track individuals, so we need the answer to the first question to know the total number of respondents.

And, as always, your comments are welcome.

22 September 2015

Crystallography as a primary screen: the case of HisRS

X-ray crystallography plays a starring role in fragment-based lead discovery. But, with a few exceptions, it is rarely the primary screen. One of these exceptions was reported recently in Acta Crystallogr. D by Wim Hol and coworkers at the University of Washington, Seattle.

The researchers were interested in the histidyl-tRNA synthetase (HisRS) from Trypanosoma cruzi, the parasite that causes Chagas disease. They had previously constructed a library of 680 fragments in pools of 10, designed such that the fragments within each pool had different shapes to facilitate crystallographic screening. Crystals of HisRS•His (ie, the enzyme in complex with its histidine substrate) were soaked with the pools, such that the final concentration of each fragment was 1.5 mM. Fifteen of these pools showed new electron density, and these in turn yielded 15 different fragment hits when the pools were deconvoluted. Two additional fragments from the deconvolution process showed weak density, though these were not further pursued.

Strikingly, all 15 fragments bound to the same site, a site not observed in the absence of the fragments. This is a narrow groove described by the researchers as a “document sleeve,” and in fact all the hits are single six-membered aromatics or double aromatics with few or no aliphatic substituents. Most of the fragments are also quite small, with the majority having just 8 or 9 non-hydrogen atoms. Although all the fragments bind in roughly the same plane, there is considerable variation in the positions of substituents, and some of the fragments appear to bind in multiple orientations.

Next, the researchers tested their hits in orthogonal assays. Only one fragment showed thermal stabilization of the enzyme, and only three showed any inhibition in a functional assay (at most 39% at 2 mM of fragment). Thus, these are very weak binders.

The fragment-binding pocket is a few Ångstroms away from the histidine substrate, making linking the two ligands feasible. In preparation for this step, the researchers acetylated the amino group of the most potent fragment. This caused little change in the functional activity, but crystallography revealed that the fragment’s binding orientation had flipped around, such that the acetamide group pointed away from the histidine and towards a cysteine residue. Attempting to turn lemons into lemonade, the researchers added electrophiles to try to interact with the cysteine residue. Some of these molecules had measurable IC50 values (on the order of 1 mM), and crystallography of one of these showed covalent bond formation between the fragment and the targeted cysteine. This cysteine residue is found in the T. cruzi enzyme but not the human version, and indeed these molecules appear to be more active against the parasitic HisRS.

This is a nice example of fragment screening by crystallography that illustrates one of its main challenges: crystallography is capable of detecting extremely weak binders that may prove difficult to advance. Still, the researchers have taken some promising initial steps, and it will be fun to see what they come up with.

21 September 2015

Monday Morning Non-Blogging

I know you all come here looking for pithy breakdowns of recent publications in the fragment world.  Today Dan and I are both in Boston at CHI's Discovery on Target conference.  We are teaching our fragment course today, which is really focused on PPIs and Biophysics for this crowd. So, blogging will be limited this week (maybe). 

16 September 2015

Fragments vs ERK2

Extracellular-regulated kinase 2 (ERK2) is one of just two known substrates of the kinases MEK1 and MEK2, themselves the subjects of considerable clinical efforts to treat cancer. In a paper just published online in Bioorg. Med. Chem. Lett., Daniel Burdick and colleagues at Genentech describe how they have used FBLD to tackle ERK2.

A library of just 635 fragments was screened against the protein using STD NMR, yielding 54 hits, and SPR, yielding 78 hits. Thirteen of these came up in both assays, and compound 1 had the second-highest ligand efficiency. Not surprisingly, X-ray crystallography revealed that this purine binds to the hinge region of the kinase. The electron density also showed something else binding nearby, which the researchers interpreted as an imidazole molecule left over from the protein purification. Thus, they set out to grow their fragment in this direction.

The purine moiety of compound 1 was not well-suited for growing towards the imidazole, and purines have also been picked over extensively by numerous groups, so the researchers used scaffold-hopping to develop compound 3. This turned out to have acceptable affinity and dramatically improved ligand efficiency. Growing led to compound 14, and structural characterization of a related molecule confirmed that the added heterocycle bound in the same region as the originally observed imidazole.

Next, the researchers grew in a different direction, ultimately leading to compound 39, with low nanomolar potency. Although no cell activity or selectivity data are reported, the authors note that the series underwent further optimization that will be reported in future.

This is a nice, concise description of fragment-based lead discovery and optimization that incorporates multiple biophysical methods, structure-based drug design and modeling, and creative medicinal chemistry. It is not clear whether targeting ERK2 has advantages over MEK or RAF, but work like this is precisely what is needed to generate chemical probes to answer this question.

14 September 2015

Is this still a thing? And why?

As regular readers here know, we often discuss metrics because everyone uses them.  Last year, agent provcateur Pete Kenny unleashed a broadside against those who defended metrics.  This seems to be  like the corpse flower that blooms once a year, stinks the place up, yet everyone runs to go see it.  Well, recently Pete posted in the LI group: "Ligand efficiency validated fragment-based design?"and asked whether or not people agreed with the statement.  This of course has inspired a wave of comments.  I disagreed with the statement, but not for the "metrics suck" argument.  I strongly urge people to go read the thread, unless of course you have something better to do.  

To me, this is not about the validity of metrics.  [Let me add here, that I prefer the "LEAN" metric (pIC50/HAC) because it can be done in your head on the fly.]  I think people have a good understanding of what they do, their limitations, and their strengths.  I disagreed with the statement because of the use of the word "validated".  In the development world, we talk about our assays very specifically: they are qualified or validated.  A validated assay is one that has been shown to be accurate, specific, reproducible, and rugged for the analyte in the concentration range to be measured.  Put plainly, this means that if you expect to measure analyte X at 5 uM, you have to show that for all samples it will be measured in you can identify it, measure 5 uM accurately, and do it every time.  That's a validated measurement.  When you are qualifying an assay, the bar is much lower.  An assay is considered qualified if it has been demonstrated to be "fit for purpose".  Fit for purpose means that it will do the job, but you haven't beat the sugar out of it to make sure it is "valid".  To me, ligand efficiency is fit for purpose of driving medchem decisions; it is qualified for that purpose, but not validated (N.B. I am not saying "not valid".) 

08 September 2015

Dry solutions for crystallography

To some, X-ray crystallography may be a rather dry topic. However, the process generally entails lots of liquids. In particular, the commonly used practice of crystal soaking entails transferring protein crystals to a new solution containing dissolved ligands, which is both tedious and can cause crystals to shatter or dissolve. A new paper by Jean-Francois Guiçhou at Université de Montpellier and collaborators in Acta Cryst. D aims to streamline the process, and so lower barriers for obtaining structural information that could guide drug design.

Rather than manually transferring crystals to new solutions, the researchers pre-coated crystallization plates with ligands and then grew protein crystals in them. They first dissolved the ligands, transferred these to the wells, and allowed the solvent to evaporate. Although they tested a variety of solvents, including acetone, tetrahydrofuran, ethanol, acetonitrile, 2-propanol, water, and DMSO, only the last two proved suitable; most of the rest wicked up the well, spreading over too large of a surface (though methanol has been used by Beryllium, née Emerald). DMSO is, of course, the most commonly used solvent for storing small molecules, and so should work for most ligands. DMSO is not very volatile, but only 1 µl was used per well, and putting the plates in a fume hood for a week left behind dry ligand.

To make things easier still, the researchers used special crystallization plates that could be put directly into an X-ray beam (in situ crystallography), further diminishing the amount of manipulation required. The technique was tested against four different proteins: the old standard hen egg-white lysozyme and the drug targets cyclophilin D, PPARγ, and Erk-2.

For lysozyme, the water-soluble fragment benzamidine was used, and the resulting structures showed the fragment binding in a similar manner as previously described. So did structures of PPARγ bound with the high affinity ligand rosiglitazone. Cyclophilin, though, was not as successful: of nine fragments attempted, only one produced a structure. In contrast, three fragments produced structures using conventional approaches. ‘Dry’ crystallization was more successful with two more potent (micromolar or better) cyclophilin ligands. Interestingly, dry crystallization succeeded with one ligand that had previously been characterized only by co-crystallization; even week-long soaking experiments had not worked.

Finally, Erk-2 was screened against 14 ligands designed as hinge-binders with low solubility in water. Crystals were obtained with five of the ligands, and four were large enough to generate good-quality structures.

Overall this seems like a convenient approach, though it does seem prone to false negatives. What do the crystallographers out there think – is this a practical solution?

03 September 2015

ATAD2 Again...Now with a good tool.

Epigenetics is big.  We keep on beating that drum.  Just to prove it, today's paper is on a target we have talked about before: ATAD2.  That previous paper was unsatisfying: leading to my summary: "if you throw enough fragments at a target you can find a few that bind."  Today's entry  from GSK has produced the first micromolar inhibitors of ATAD2.  

As noted previously, ATAD2 is "undruggable" or at least VERY difficult to find chemical matter against.  To add to the difficulty,  the BET activity needs to be minimized.  With that in mind, they set a high threshold of activity (pIC50 greater than7) and 100 fold selectivity against BRD4 (a representative BET domain).  The ATAD2 site is more polar and flexible than BET.  The authors felt that this would be exploitable to create selective molecules.  To address ATAD2 they started with Ac-K mimics from previous BET work.  They supplemented this with diverse cores not represented.  One such array (which I read as libraries, somebody correct me if I am wrong) was based on the cpd 1,
Cpd 1
which is similar to the chemotypes discussed last year.   A crystal structure of 1 was solved, confirming that it bound as expected.  

Additional arrays were made around this core and tested in a TR-FRET assay.  30,000 compounds gave a 0.25% hit rate.  Confirmation was performed by HSQC NMR.  A subset of compounds interacted at the Ac-K site based upon comparison to compounds with known binding modes.  In this case, the peak that shifted upon binding were the same.  I would like to know if this was by visual inspection of spectra or if it was accomplished using PCA, or similar method.  It probably doesn't matter, but intrigues the NMR jock in me.

In rounds of medchem and X-ray confirmation, they were able to drive the potency against ATAD2 to the single digit micromolar.  The ligand efficiencies were maintained right around 0.30. Compound 57 (R=4-Me) and 60 (R=4-OMe) had the "best balance of ATAD2 and BET activity".  These compounds were also active in a cell-based assay known to be sensitive to BET inhibitors.  However, there is no selectivity.  ATAD2/BET pIC50 for 57 was 1.1 and 60 was 1.0. So, despite the selectivity threshold they developed, these compounds are not selective.  Despite that, I think this paper shows that the aphorism Undruggable =Undone is true.

01 September 2015

Polypharmacology for Kinases

We've been a roll with epigenetics and PPIs lately.  So, its a nice break when a kinase paper comes out.  But, in keeping with the theme of hard targets, today's paper is about a tyrosine kinase.  We've started to see more and more FBDD on TKs.  One problem is that TKs can acquire resistance to drugs, quickly eliminating their therapeutic usefulness.  One way around this is to use polypharmacology: "optimized inhibitory profiles for critical disease-promoting kinases, including crucial mutant targets."  In this work, they are targeting RET and VEGFR2 dual inhibitor using a in silico/fragment approach.  

Compound design was largely based upon homology modeling the "DFG-out" RET structure utilizing the VEGFR2 structure as a template.  Their Kinase Directed Fragments (KDF) are shown in Figure 1.
Figure 1.
Their fragment design rationale makes some interesting comments.  They state that a "hinge binding" fragment alone can aggregate at high concentrations needed to achieve activity in a biochemical screen.  So, their fragments have an additional moiety that interact with the lipophilic or ribose pocket.  
Accordingly, KDFs have larger molecular weights and are generally more active than the fragments contained in traditional libraries, permitting screening in the micromolar range.
I would say the first statement is conjecture and the second untrue.  17 heavy atoms is squarely in the regime of what people consider "fragment" sized.  I think instead the authors are using the wrong tool for the job.  Using a biochemical screen to find fragment actives is akin to hammering a nail with a screwdriver.  Sure, you can do it, but why would you?  

Rather expectedly, they identified compound 1 as a promising starting platform.  Of course, the criteria for selecting this compound are kept highly secret.  It did "effectively" inhibit RET at 100 (63%) and 20 uM (28%) in the presence of 190 uM ATP [Km for RET 12uM].  It had VEGFR2 activity of 59% at 100 uM. 
Add caption
Modeling allowed them to generate the compounds showed in Figure 2.
Figure 2. 
Pz-1 had activity less than 1 nM against RET, RET(V804M/L)[a gatekeeper mutant],  and VEGFR2.  This equipotency was also demonstarted in cell-based assays.  Against a panel of 91 other kinases at 50 nM, Pz-1 had significant activity against 7 others (TRKB, TRKC, GKA, FYN, SRC, TAK1, MUSK).  So, in the end using primarily modeling and a biochemical assay they were able to generate a polypharmacological TK inhibitor.  I leave it to those more well versed in the biology whether or not those 7 other kinases pose a potential problem.  I however would argue that they generated an agent with polypharmacology against 9 kinases not 2. 

24 August 2015

Fragment-Based Drug Discovery

This is the straight-to-the-point title of a new book published by the Royal Society of Chemistry, edited by Steven Howard (Astex) and Chris Abell (University of Cambridge). It is the second book on the topic published so far this year, and it is a testimony to the fecundity of the field that the two volumes have very little overlap.

After a brief forward by Harren Jhoti (Astex) and a preface by the editors, the book opens with two personal essays. The first, by me, is something of an apologia for Practical Fragments and the growing role of social media in science (and vice versa). If you’ve ever wondered how this blog got started or why it keeps going, this is where to find out. The second essay is by Martin Drysdale (Beatson Institute). Martin is a long-time practitioner of FBDD, dating back to his early days at Vernalis (when it was RiboTargets) and he tells a fun tale of “adventures and experiences.”

Chapter 1, by Chris Abell and Claudio Dagostin, is entitled “Different Flavours of Fragments.” With a broad overview of the field it makes a good introduction to the book. There are sections on fragment identification, including the idea of a screening cascade, as well as several case studies, some of which we’ve covered on Practical Fragments, including pantothenate synthetase, CYPs, RAD51, and riboswitches.

The next two chapters deal with two of the key fragment-finding methods. Chapter 2, by Tony Giannetti and collaborators at Genentech, GlaxoSmithKline, and SensiQ, covers surface plasmon resonance (SPR). This includes an extensive discussion of data processing and analysis, which is critical for improving the efficiency of the technique. Competition studies are also described, as are advances in hardware, notably those from SensiQ. This is a good complement to Tony's 2011 chapter.

Chapter 3, by Isabelle Krimm (Université de Lyon), provides a thorough description of NMR methods, both ligand-based (STD, WaterLOGSY, ILOE, etc) and protein-based (mostly HSQC). The chapter does a nice job of describing techniques in terms a non-specialist can understand while also providing practical tips on matters such as optimal protein size and concentration.

Chapter 4, by Ian Wall and colleagues at GlaxoSmithKline, provides an overview of FBLD from the viewpoint of computational chemists. The chapter includes some interesting tidbits, such as the observation that fragment hits that yield crystal structures tend to be less lipophilic but also contain a smaller fraction of sp3 atoms and more aromatic rings. The researchers note that the current fashion for “3D” fragments is yet to be experimentally validated. They also include accessible sections on modeling, druggability, and integrating fragment information into a broader medicinal chemistry program.

The remaining chapters focus on specific types of targets. Chapter 5, by Miles Congreve and Robert Cooke (both at Heptares) is devoted to G protein-coupled receptors (GPCRs). This includes descriptions of how to screen fragments against these membrane proteins using SPR, TINS, CE, thermal melts, and competition binding. It also includes a detailed case study of their β1 adrenergic receptor work (summarized here). Congreve and Cooke assert that, although many of the GPCR targets screened to date have been highly ligandable, technical challenges only now being addressed have caused this area of research to lag about a decade behind other targets. They predict a bright future.

Rod Hubbard (Vernalis and University of York) turns to protein-protein interactions in Chapter 6. After describing why these tend to be more challenging than most enzymes and covering some of the methods for finding and advancing fragments, he then presents several case studies, including FKBP (one of the first targets screened using SAR by NMR), Bcl-2 family members (including Bcl-xL and Mcl-1), Ras, and BRCA2/RAD51. He concludes with a nice section on “general lessons,” which boils down to “patience, pragmatism, and integration.” As Teddy recently noted, this can lead to substantial rewards.

Allosteric ligands have potential advantages in terms of selectivity and addressing otherwise challenging targets, and in Chapter 7 Steven Howard (Astex) describes how fragments can play a role here. This includes how to establish functionality of putative allosteric binders, as well as case studies such as HIV-1 RT, FPPS, and HCV NS3. Astex researchers have recently stated that they find on average more than two ligand binding sites per protein, and this chapter includes a table listing these (including 5 binding sites each on bPKA-PKB and PKM2).

The longest chapter, by Christina Spry (Australian National University) and Anthony Coyne (University of Cambridge) describes fragment-based discovery of antibacterial compounds. After discussing some of the challenges, the authors report several in depth case studies including DNA gyrase, DNA ligase, CTX-M, AmpC, CYP121, and pantothenate synthetase, among others. At least one fragment-derived antibacterial agent entered the clinic; hopefully more will follow.

Chapter 9, by Iwan de Esch and colleagues at VU University Amsterdam, focuses on acetylcholine-binding proteins (AChBPs), both as surrogates for membrane-bound acetylcholine receptors and as well-behaved model proteins on which to hone techniques (see for example here, here, and here). Since AChBPs have evolved to bind fragment-sized acetylcholine, these proteins can bind tightly to small ligands; 14-atom epibatidine binds with picomolar affinity, for example, with a ligand efficiency approaching 1 kcal mol-1 atom-1.

And Chapter 10, by Chun-wa Chung and Paul Bamborough at GlaxoSmithKline, concisely covers epigenetics. Bromodomains are well-represented, including a table of ten examples (see for example here, here, here, here, here, and here). Happily, although some of these projects started from similar or identical fragments, the final molecules are quite divergent. However, the authors note that much less has been published on histone-modifying enzymes, such as demethylases and deacetylases, perhaps reflecting the challenges of achieving specificity with what are often metalloenzymes.

Finally, this is the 500th post since Teddy founded Practical Fragments way back in the summer of 2008. Thanks for reading, and special thanks for commenting!