20 March 2017

DOT1L revisited: fragment linking to picomolar binders

Last September we highlighted two papers out of Novartis in which various fragment-based methods delivered two separate series of low nanomolar inhibitors against the histone methyltransferase DOT1L, an epigenetic anticancer target. The next installment of this story, by Christoph Gaul and Novartis colleagues, has just published in ACS Med. Chem. Lett.

DOT1L uses the cofactor S-adenosylmethionine (SAM), but the previously identified fragments bind in a site adjacent to the cofactor’s binding site. Seeking to gain affinity the researchers decided to look for fragments that bind in the same pocket as SAM. SAM and ATP both contain an adenine ring, and plenty of fragment libraries have been built around the fact that ATP is a kinase cofactor. About 200 kinase-targeted fragments were tested in a high-concentration biochemical screen. Compound 2 was active, and crystallography revealed that it forms multiple hydrogen bonds with DOT1L. More importantly, crystallography also revealed that compound 2 could bind simultaneously with inhibitors occupying the site adjacent to the cofactor binding site.

Modeling suggested that it would be possible to link compound 2 with second-site binder compound 3. This proved successful, with compound 4 having low nanomolar affinity. For aficionados of linking, the free energy of binding for compound 4 is comparable to the sum of the free energies of binding for compounds 2 and 3. This level of additivity is similar to results on different targets at Ariad and Evotec. Interestingly, the two fragments (2 and 3) conform to a previous suggestion from Evotec that – to maximize the likelihood of success in fragment linking – one fragment should be polar and make multiple hydrogen bonds with the protein, while the second fragment should make largely lipophilic interactions.

But the researchers didn’t stop there. Adding a methyl group to the secondary amine on compound 4 improved affinity by more than an order of magnitude, and further tweaking the right-hand side of the molecule led to compound 7, with low picomolar affinity. This compound also showed low nanomolar activity in several different cell-based screens, and was completely inactive against a panel of 21 other methyltransferases. Selectivity against kinases is not shown, and given the origin of the left-hand fragment this could be an issue, potentially complicating the interpretation of biological assays. That said, polypharmacology can be useful, particularly in oncology.

Fragment linking has a reputation for being difficult, but this is another example that it can work. The high affinity of compound 7 is particularly remarkable considering the number of rotatable bonds. Of course, plenty of challenges likely remain: no data are provided for in vitro stability, let alone pharmacokinetics. Although the structure of compound 7 may raise eyebrows, it is worth remembering that even larger, more lipophilic molecules have become successful drugs.

13 March 2017

Fragment-linking on proteins: amide formation

Among fragment-linking approaches, protein-templated reactions have a special appeal. When two fragments that bind next to one another on a protein react, the protein essentially creates its own ligand. The reaction can be reversible, as in dynamic combinatorial chemistry, though the choice of chemistries tends not to be very drug-like, necessitating replacement of the linker for lead development to proceed. Irreversible chemistries, such as cycloadditions (“click chemistry”), give more stable linkers, but these still tend to be unusual. In a recent open-access paper in Angew Chem., Jörg Rademann and collaborators at the Freie Universität Berlin and Philipps-Univesität Marburg have extended the approach to amides – one of the most vanilla chemistries out there.

The researchers used factor Xa as a model protein. Using information in the literature they designed compound 15, found that it was a low nanomolar inhibitor, and obtained a crystal structure of the molecule in complex with the enzyme. Cleaving one of the amide bonds provides two fragments, compounds 16 and 17, each with very weak activity. Put another way, linking these two fragments leads to superadditivity of binding.

Chemists often make amides by “activating” a carboxylic acid and reacting it with an amine. We usually want this to happen quickly, but here the researchers wanted the reaction to only occur when the two fragments bind next to one another on the surface of the protein.

The team made a dozen activated esters of compound 1 and incubated 5 mM of each of these with 0.285 mM of amine 14, in the presence or absence of 14.5 nM factor Xa. (Each combination of reactants would produce compound 15.) After two hours protein was added to the no-protein controls and fluorogenic substrate was added to all the samples, which were tested for enzyme activity. Not surprisingly, inhibition was complete for particularly active esters (such as 4-nitrophenyl ester), whether or not protein had been present during the incubation. Similarly, the presence of protein also didn’t matter for particularly inactive esters (such as methyl ester): inhibition was minimal because very little product 15 formed. But for two Goldilocks esters (phenyl ester and 2,2,2-trifluoroethyl ester), inhibition was greater when protein was present during incubation.

The researchers used mass spectrometry to quantify the amount of compound 15 formed. For the 2,2,2-trifluoromethyl ester, after 1.7 hours the concentration of compound 15 was 10 nM in the presence of factor Xa, but only 0.15 nM in its absence. (The phenyl ester gave less selective results.)

Of course, as these numbers suggest, product formation could be limited by the concentration of the enzyme, possibly complicating detection. More significantly, even relatively unreactive esters could react with the dozens of nucleophiles on proteins, potentially inactivating or denaturing them. Still, this is an intriguing approach, and it will be fun to see whether it works in more challenging systems.

06 March 2017

Fragments vs BRPFs: A chemical probe

Bromodomains, a type of functional module within proteins, recognize acetylated lysine residues in other proteins to act as epigenetic “readers.” Humans have 61 of them spread across 46 different proteins, and figuring out what recognizes what and in which context has been a major undertaking for the past several years. Fragment-based approaches have proven very successful: Practical Fragments devoted the entire month of last July to bromodomains. In a new paper in J. Med. Chem., a large group of academic and industry investigators led by Paul Fish describe their successful efforts to find a chemical probe for the four members of the BRPF family.

The story actually starts with a fragment screen against a different bromodomain, PCAF, which we discussed last year. Compound 5b was a hit against that target, but bromodomain fanciers will recognize this as a privileged pharmacophore against the target class, and it turned out to be even more potent against BRPF1.

Crystallography confirmed that compound 5b bound in the acetyl-lysine pocket as expected, and also revealed a potential vector for growing, as exemplified by compound 6. Introducing a methyl group led to a 10-fold boost in affinity for compound 7, which as the researchers point out is near the limit of what you can expect for hydrophobic interactions. Further growing ultimately led to NI-42, with low nanomolar potency.

Of course, potency is one thing, but when you’re dealing with dozens of related proteins you really need specificity to understand the biology. Happily, NI-42 was quite selective for members of the BRPF family. Importantly, it has only 4.5 µM activity against BRD4, which can dominate cell phenoytpes when inhibited. The selectivity is actually remarkable given the close structural similarity of NI-42 to PFI-1, which hits BRD4 and which Teddy wrote about here. The researchers suggest that the difference between the N-H in PFI-1 and the N-methyl in NI-42 drives the selectivity – so one could argue that the probe actually makes use of two magic methyls.

NI-42 also showed good cell permeability and target engagement in cells, adequate solubility, decent pharmacokinetics, and respectable oral bioavailability in mice. Although a screen of 211 cancer cell lines did not reveal stunning activity, cell models for other diseases are being evaluated. Also, the researchers have generated an inactive but closely related control simply by replacing both methyl groups with ethyl groups.

This is a lovely fragment optimization story. It is also a useful reminder that, as with phosphodiesterases and kinases, a nonselective fragment can ultimately yield a selective chemical probe.

27 February 2017

When do ligands change their binding modes?

Elaborating a fragment to improve its affinity relies on the assumption that the fragment will maintain its position and orientation during optimization. Although this is usually the case, exceptions are common, and when flips go unrecognized the resulting SAR can be confusing. Is it possible to predict which ligands are most likely to change their binding mode? This question is addressed in a recent J. Med. Chem. paper by Shipra Malhotra and John Karanicolas of the Fox Chase Cancer Center and the University of Kansas.

The researchers scoured the Protein Data Bank (PDB) to find pairs of molecules bound to the same protein where one ligand was a substructure of the other. (In most cases these were not actually from fragment-based efforts, and the two structures were often solved by different research groups.) This generated 297 pairs of crystal structures. Computational and manual analyses revealed 41 instances (14%) in which the larger ligand had a significantly different binding mode than the smaller ligand. Careful inspection revealed that these observations were probably not due to crystallographic artifacts or differences in experimental conditions. The researchers then examined well over a dozen parameters to look for correlations with changes in binding mode.

Size matters: for the 73 rule-of-three compliant smaller ligands, the binding modes were not conserved in the larger ligands 23% of the time. Binding modes changed 30% of the time when the smaller ligand was ~100 Da, but only 5% of the time when the smaller ligand was ~400 Da.

Potency also matters: as might be expected, weaker ligands were statistically less likely to preserve their binding mode. (Of course, as the researchers observe, potency often correlates with size.) More polar ligands, as assessed by clogP, were also less likely to maintain their binding modes.

Looking beyond molecular properties to those of the initial complex, ligands binding to a small pocket were less likely to maintain their binding modes. Also, ligands for which a large amount of solvent-accessible surface area was buried upon binding to the protein were more likely to maintain their binding modes.

Many other properties showed no statistically significant correlation with binding modes. These included ligand efficiency, fraction of the ligand buried, and various descriptions of the protein binding site, such as hydrophobicity and the fraction of polar or aromatic amino acid residues.

The open-access hot-spot finding software FTMap has previously been used to assess when ligands change their conformation, and it performed well on this set of molecules, although as it requires structures of both the larger and smaller ligands it has limited predictive value. The researchers also introduced another computational tool, RMAC (RMSD after Minimization of the Aligned Complex) which did even better.

This paper is a fun read, and there’s lots more than can be summarized here, including detailed analyses of specific examples. The researchers have done a great job collecting and synthesizing a huge amount of data. Admirably, all of the calculated properties are available in the supporting information. Of course – in view of the title of this blog – we have to ask, how practical is it? For any given fragment to lead program, it is still impossible to predict whether the binding mode will shift. But if you’ve got a particularly small, weak fragment binding to a little dimple on a protein surface, you should probably expect surprises.

20 February 2017

Many measures of molecular complexity

Molecular complexity is a fundamental concept underlying fragment-based lead discovery: fragments, being simple, can bind to more sites on proteins and thus give higher hit rates than larger, more complex molecules. The ultimate example of this is water, which – at 55 M concentration – binds to lots of sites on proteins. But although the concept is easy to describe, it is much harder to quantify: everyone can agree that palytoxin is more complex than methane, but by how much? And if complexity could be measured, could it help in optimizing libraries? This is the subject of a review by Oscar Méndez-Lucio and José Medina-Franco at the Universidad Nacional Autónoma de México published recently in Drug Discovery Today.

There are many ways to measure molecular complexity. Two of the simplest to calculate are the fraction of chiral centers (FCC) and the fraction of sp3 carbons (Fsp3). These range from 0 to 1, and larger numbers imply a higher number of unique molecules with the same formula.

More complicated methods to measure complexity abound, but many of these require specialized software. Two that are publicly available are PubChem complexity and DataWarrior complexity. In PubChem, complexity incorporates the number of elements as well as structural features such as symmetry, though stereochemistry is not explicitly considered, and aromaticity is scaled such that both benzene and cyclohexane have the same complexity – a sharp contrast to FCC and Fsp3. DataWarrior uses its own metric, though I couldn’t find the definition. (Ironically, though the software itself is open source, the paper describing it is not.)

So, do more complex molecules have lower hit rates? The researchers looked at several public databases of screening data for dozens of assays against thousands of molecules. Using each of the four metrics, they classified molecules as “simple,” “intermediate,” or “complex”. For FCC and Fsp3, simple compounds did appear to be more promiscuous, in line with theory and with previous findings. However, for PubChem and DataWarrior, the trends were not clear – and even reversed in some cases. The researchers note that the median complexity of molecules in each dataset may vary, and as Pete has also observed simple binning strategies can be misleading.

Do these different definitions of complexity even measure the same thing? The researchers plotted each pair-wise measurement of complexity for >400,000 molecules – for example, Fsp3 vs DataWarrior. Not only are there no universal correlations, those that do exist are conflicting. "For example," the authors write, “compounds with high FCC values are associated with low PubChem complexity values, whereas the same molecules have high DataWarrior complexity." 

Teddy has previously invoked Justice Potter Stewart and his famous “I know it when I see it” expression, and I think that just about sums up where things stand in terms of molecular complexity. From a practical standpoint this probably doesn’t matter; a complex molecule is not even necessarily more difficult to make, as evidenced by the ease of oligonucleotide and peptide synthesis. Still, it would be nice if someone could come up with a reliable measurement for such a fundamental property – or even demonstrate whether or not such measures are possible.

13 February 2017

Fragments in Cell(s)

Last year we highlighted work out of Ben Cravatt’s group at Scripps on screening covalent fragments in cells. Now, in a new Cell paper, his group and collaborators at the École Polytechnique Fédérale de Lausanne, Bristol-Myers Squibb, and the Salk Institute have gone further by screening for non-covalent fragments in cells.

The researchers started by synthesizing a collection of just 14 “fully functionalized” fragments (FFF). In addition to the variable fragment (averaging 176 Da), each FFF probe contains a diazirine group, which, when exposed to UV light, generates a highly reactive species that can covalently react with proteins (or anything else) in close proximity. (In contrast, covalent fragments typically work via chemistries in which bonds form without requiring UV exposure.) The FFF probes in the current study also contain a "clickable" tag: an alkyne moiety that can react with azide-containing molecules using copper-catalyzed azide alkyne cycloaddition.

Cells were incubated with 20 µM of each fragment for 30 minutes, then exposed to UV light for 10 minutes on ice to capture non-covalent interactions. The cells were then lysed, treated with an azide-containing flurorescent dye in the presence of copper, and analyzed by gel electrophoresis to visualize those proteins with bound fragments. The results were striking: lots of proteins were labeled, and each fragment labeled a different set of proteins. This is what you would expect for low-complexity molecules, but it is nice to see reality match predictions.

Not content to look at gels, the researchers switched to mass spectrometry for a more global analysis using “stable isotope labeling with amino acids in cell culture” (SILAC). In this approach, one population of cells grown under normal conditions was treated with one of the FFF probes, while a second population of cells containing isotopically labeled proteins was treated with a control probe containing just a methyl group instead of the variable fragment. The resulting cell lysates could then be proteolyzed and analyzed by mass spectrometry; most peptides would show two peaks of similar intensities, one from each isotopically distinct population of cells. However, if an FFF probe bound preferentially to a protein compared with the control probe, the resulting peptide would be enriched.

Examining 11 FFF probes at 200 µM concentration allowed the researchers to identify an impressive 2000 different protein targets. Both soluble and membrane proteins were found, with expression levels ranging over 100,000-fold (i.e. the technique seems to work for both rare and abundant proteins). Remarkably, only about 17% had known ligands. There was also little overlap with the proteins targeted by the researchers’ previously described covalent fragments.

Where did the FFF probes bind? An analysis of 186 proteins whose crystal structures had previously been reported showed that about 80% of the modified peptides were close to a computationally predicted pocket, as might be expected.

Next, the researchers made or purchased analogs of some of their FFF probes. When added to screens, these decreased labelling of hundreds of targets; this competition assay both suggests the FFF probes make specific interactions while also providing more potent analogs. Two proteins – the enzyme PTGR2 and the transporter SLC25A20 – were studied in some detail. Probe 8 modified two peptides near the active site of PTGR2 and could be competed by compound 20. Compound 20 was also a modest inhibitor of the enzyme. Further modification led to compound 22, with nanomolar activity against the isolated enzyme and in cells. Since this protein previously lacked any good chemical probes, this could be useful.

This approach also lends itself well to phenotypic screening, so the researchers expanded their FFF probes to 465 members, increasing the size of the variable fragment portion to an average of 267 Da. They also made competitor molecules for most of these, which contained the fragment but not the alkyne or photoreactive group.

The researchers screened about 300 of their new FFF probes (at 50 µM each) to look for molecules that would increase the differentiation of mouse preadipocytes to adipocytes. This led to nine hits, one of which was active at 10 µM. SILAC experiments revealed the target of this to be a somewhat obscure membrane protein called PGRMC2. Subsequent experiments suggested that PGRMC2 is a positive regulator of adipogenesis, and that the identified compound is an activator.

This is a remarkable paper, and it is impressive that the researchers have described not just the approach but several success stories – each of which could well form a stand-alone publication. The covalent platform described last year has already led to a company - Vividion - which recently raised $50 million, and I’m sure the new approach will find use in both academia and industry.

06 February 2017

Beware self-reacting fragments

Long-time readers will know that I have a peculiar fascination for artifacts of all kinds, particularly when they provide learning opportunities. A lovely example by Gerhard Klebe (Philipps-Universität Marburg) and collaborators has just appeared in Angew. Chem. Int. Ed.

The researchers have long been using the aspartic protease endothiapepsin (EP) as a model protein: we previously discussed how they compared half a dozen fragment-finding methods against EP, more recently arguing that crystallography is the best of the bunch. The new paper focuses on Compound 1. This molecule was a hit in five out of six fragment screens, each employing a different method, and was among the top ten hits in four of the screens. It produced the highest thermal shift (+3.4 °C), strongly inhibited the enzyme in two different biochemical assays, and even showed a dissociation constant of 115 µM by isothermal titration calorimetry (ITC).

Crystallography, though, told a different story. The researchers obtained high resolution structures (initially 1.25 Å, and ultimately 1.03 Å!) These revealed that the bound ligand was actually compound 2, which is composed of three molecules of compound 1. A variety of experiments, including anomalous scattering, high resolution mass spectrometry (HR-MS), and MS/MS fragmentation supports this assignment.

So what’s going on? The team notes that, although compound 1 does not aggregate and is not a PAINS compound, high-level quantum mechanical modeling suggested that the chlorine is susceptible to nucleophilic displacement, which probably wouldn’t surprise many medicinal chemists. Rearrangement of the resulting dimeric molecule produces compound 4, which could then react with yet another molecule of compound 1 through a radical mechanism to produce compound 2.

Allowing compound 1 to sit in buffer or methanol provided support for this mechanism and allowed the isolation of compound 4 and other degradation products, though compound 2 itself could not be detected. The researchers suggest it is particularly reactive and only stable when surrounded by the protein.

I applaud the investigators for pursuing this fascinating bit of science. This is academic research in the best sense of the phrase.

This is also the kind of investigation that would fall outside the scope of most industrial researchers, where the mandate is to discover promising drug leads as quickly as possible. More somberly, this story could have ended in embarrassment or worse had the researchers been less rigorous. The difficulty (and unlikelihood) of such lengthy investigations is why triaging shortcuts such as PAINS filters have been introduced, and why scientists using these tools must still be cautious: even molecules that aren’t PAINS can act through pathological mechanisms.

This is also why I believe that arguments that PAINS filters are inadequately defined and should thus be discarded are misguided. Sure, some PAINS molecules are drugs, and any rubric can be improved. But the nice thing about fragment screens is that they often produce a plethora of hits to pursue. A flawed triaging scheme will jettison some pearls among the pebbles, but without triage, far more resources will be lost chasing will-o'-the-wisps.

30 January 2017

Fragments vs PRC2: ligand deconstruction

Ligand deconstruction is a strategy for early-stage drug discovery in which a known hit is dissected into component fragments and one or more of them is optimized. When successful, it can lead to new and improved chemical series. One such example was just published in J. Med. Chem. by Andreas Lingel and colleagues at Novartis.

The researchers were interested in finding inhibitors of the protein methyltransferase polychrome repressive complex 2 (PRC2). Although some drugs have entered the clinic against this anticancer target, all of these are competitive with the cofactor S-adenosylmethionine (SAM), and resistant mutants are already being detected. Thus, the team sought a molecule that would act through a different mechanism.

PRC2 is actually a complex of four different proteins. The SET domain of the protein EZH2 contains the catalytic machinery, but a protein called EED stabilizes the protein complex and is necessary for activity. EED also recognizes trimethylated lysine residues on histone substrates, allosterically activating methyltransferase activity.

A high-throughput biochemical screen identified compound 1, which has low micromolar activity and is noncompetitive with the SAM cofactor and substrate peptide. Subsequent NMR and crystallography experiments revealed that compound 1 binds to EED, with the tertiary amine binding in the same pocket that normally recognizes trimethyllysine. However, compound 1 is quite complex, with three stereocenters. Thus, the researchers sought to deconstruct it to something simpler. They began by chopping off two of the rings – an unconventional disconnection but one supported by crystallography, which revealed that the terminal rings were not closely associated with the protein.

The resulting fragment 2 was down more than an order of magnitude in potency but had improved ligand efficiency. Crystallography confirmed that it binds in a very similar fashion to compound 1. Initial SAR was conducted around the methoxybenzyl moiety, which is buried within the enzyme. Most changes were not tolerated, but compound 9 did show somewhat improved activity.

Next, the researchers sought to optimize the positively charged portion of the molecule. Replacing the amine with a guanidine improved the affinity but at a cost to cell permeability. This led to a search for less conventional replacements, ultimately yielding the 2-aminoimidazole moiety in compound 16. Not only did this regain the activity of the initial molecule, it also shows good permeability and cell activity. Crystallography revealed that it too binds in a similar fashion to the original hit.

This is a nice example of fragment-assisted drug discovery (FADD), in which concepts from FBDD were used to simplify and optimize a hit from HTS. There is of course much more to do with this series, not the least of which is figuring out exactly how the molecules actually inhibit PRC2. Trimethyllysine-containing peptides that bind to EED normally activate the enzyme, yet the small molecules that bind to the same site somehow allosterically inhibit activity. Despite multiple crystal structures, the researchers frankly acknowledge that they were “not able to decipher the molecular basis for this phenomenon.” A number of conformational changes occur when EED binds to ligands, and perhaps these propagate through the protein complex. A picture may be worth 1000 words, but we may have to wait for the movie to learn the full story. 

23 January 2017

New tricks for old methods: STD NMR

According to our last poll, ligand-detected NMR is the most popular method for finding fragments. And among the several ligand-detected NMR techniques, the most popular appears to be saturation transfer difference (STD) NMR. The basic concept behind this approach is to selectively irradiate a protein, which then transfers its magnetization to any bound ligand, thus “saturating” (reducing the signals) for the ligand. Subtracting this spectrum from a reference spectrum reveals which ligand (if a mixture) or individual protons within a ligand are in close proximity to the protein.

Although STD NMR is fast and easy to run, it does have drawbacks. One is the fact that it requires pure protein: if there are other proteins in solution, it will be impossible to tell whether the small molecule binds to the protein of interest or to something else. This shortcoming has been overcome in a paper published recently in J. Biomol. NMR by Tamas Martinek and collaborators at the University of Szeged, the Hungarian Academy of Sciences, and the University of Debrecen.

In a normal STD experiment, the protein protons that are irradiated are far upfield (often around -0.5 ppm) – a region not relevant to most small molecules. These protons then transfer the magnetization throughout the protein and ultimately to any bound small molecules. In order to choose a specific protein, the researchers add an 15N-labeled antibody selective for the protein. They can then selectively irradiate the 15N-labeled antibody, which transfers its magnetization to the bound protein and from there to any bound ligand. They call this approach monoclonal antibody-relayed 15N-group-selective STD, or mAb-relayed 15N-GS STD.

To demonstrate the approach, the researchers observed the binding of 2 mM lactose to galectin-1 (Gal-1) using an 15N-labeled antibody against Gal-1. Lactose binds to Gal-1 with a dissociation constant of 0.155 mM, which is a relevant affinity for fragment screening. Gal-1 was present at 20 µM and the antibody was present at 10 µM, both of which are reasonably low. Control experiments established that both Gal-1 and the antibody were necessary, and the experiment was successful even in a cell extract.

So, as Teddy would ask, is this approach practical? You need an 15N-labeled antibody against your target, and it is important that the antibody is specific and does not compete with your ligand (ie, that it is non-neutralizing). Also, the amount of time required to acquire the spectra appears to be more than an hour. If this could be reduced, would 15N-GS STD assume a useful niche in the NMR toolbox?

16 January 2017

Enthalpy revisited – and retired

The relative importance of enthalpy versus entropy for protein-ligand interactions has been a subject of considerable attention. In a 2009 post we suggested that it might be worthwhile to focus on fragments that bind predominantly enthalpically, and in 2011 we highlighted a paper suggesting that enthalpic binders may be more selective than entropic binders. But the universe has a way of confounding pet models – as we acknowledged in 2012 (twice). The best way forward is often with lots of data, which is exactly what we have in a new paper in Drug Disc. Today by György Keserű and collaborators at the Hungarian Academy of Sciences, Astex, and AstraZeneca.

The data in this case are sets of 284 protein-ligand interactions with thermodynamic binding data from the literature, 782 from Astex, and about 230 from AstraZeneca. Commendably, these data are provided in 103 pages of supporting information.

In order to analyze the data, the researchers developed a new metric, the Enthalpy-Entropy Index:


If IE-E = 0, it means that enthalpy and entropy both contribute equally to the free energy of binding; if IE-E > 0 it means that enthalpy dominates, and if IE-E > 1 it means that enthalpy needs to overcome an unfavorable entropy. Similarly, negative values mean that entropy dominates – completely so when IE-E < -1. (Note that, unlike enthalpy efficiency, this is a dimensionless ratio, which should please our friends over at Molecular Design.)

As it turns out, the vast majority of fragments bind to their targets with favorable enthalpy, and almost all of those that don’t are charged compounds in which desolvation of the charged bit could entail an enthalpic cost. The researchers also examined a set of 94 neutral fragment-sized and 44 larger molecules binding to 17 targets and found that, statistically speaking, enthalpy plays a more important role in the free energy of binding for fragments than for larger molecules. But things can change quickly: in one case, adding just two non-hydrogen atoms to a molecule improves the affinity by more than 4000-fold and changes the IE-E from -1.5 to +0.5.

The paper does an excellent job describing the challenges of collecting high-quality isothermal titration calorimetry (ITC) data. In a typical experiment, the heat measured with each injection is the same as “would fall on an A4 sheet of paper in 1 second when illuminated by a 40 Watt bulb placed nearly 5 kilometers away.” Errors can be caused by inaccurate concentrations, heat of dilution, and changes in buffer concentration or protonation state. An analysis of replicate measurements at Astex found that, while most of the replications were within 1 kcal/mol of each other, some were off by nearly 3 kcal/mol. However, these larger values were all associated with different forms of the protein, and so may not be considered true replicates, though they do indicate how changes in the protein far from the active site can have an effect on what is often considered (erroneously) a local interaction.

This also emphasizes the fact that, as the researchers note, “the measured binding enthalpy is a net value and the dissection of the individual contributions might be ambiguous.” Or, as Pete has previously stated, “the contribution of an individual protein-ligand contact is not strictly an experimental observable”.

From a molecular recognition standpoint I find all this quite interesting and even intuitive in a hand-wavy sort of way. As the researchers suggest, fragments, being small, have minimal surface area with which to make (often but not always entropically-driven) hydrophobic interactions. Instead, much of the binding energy comes from hydrogen-bond interactions, which are (again often but not always) predominantly enthalpic. Moreover, since the entropic cost of locking down any ligand onto a protein is on the order of 3-5 kcal/mol, fragments are already fighting against entropy, and this is exacerbated by low affinity.

But from a practical perspective, my earlier suggestion to focus on enthalpic fragments may have been simplistic: if you’ve found a fragment, its enthalpy of binding is almost certainly favorable, and even if it’s not, this could change with the slightest tweak. So unless we see something truly new, don’t expect many new posts on this topic.

11 January 2017

Cussed curcumin

Teddy’s retirement from the blog has cut down on the number of PAINS-shaming posts, and truth be told there are so many candidate papers that they could easily swamp fragments, which I suspect would drive away most of the readership. That said, I did want to highlight an exhaustive Perspective about a particularly diabolical natural product just published today in J. Med. Chem. by Mike Walters and collaborators at the University of Minnesota, Brigham and Women’s Hospital, and the University of Illinois (and also covered in a news story in Nature.)

We’ve previously discussed some of the types of artifacts that can plague small molecule screens: aggregation, covalent adducts, redox cycling, fluorescence, photoreactivity, and more. Curcumin is a jack of all trades in that it is capable of all of the above. It’s also unstable even at neutral pH, and can decompose into other reactive species. It is the quintessential chemical con artist: if you have an assay, curcumin will probably be active in it.

The new paper is a thorough investigation (18 pages, with 164 references) of the chemistry and biology of curcumin, covering in gruesome detail all the many ways it can deceive. After discussing the history and physicochemical properties (and liabilities), several literature case studies where curcumin is proposed as having biological activity are explored and thoroughly demolished; one of these has been retracted but continues to be cited uncritically years later.

One might expect that something which hits so many assays would be toxic. This turns out not be the case: curcumin is present at 1-6% in tasty turmeric and only seems to show any adverse events at very high doses – several grams per day. The reason, the researchers show, is that curcumin’s pharmacokinetics are lousy, with oral bioavailability of less than 1%. This is a very literal example of the cliché “garbage in, garbage out.”

Sadly, these properties have not dampened interest in testing curcumin in people. The researchers identify 135 registered clinical trials, only eight of which have reported study results, with 49 either recruiting or not yet recruiting. The few examples where results have been reported are not particularly encouraging.

Typing curcumin into PubMed pulls up close to 10,000 papers, with more than 150 published in J. Med. Chem. alone. Will this devastating exposé help? For honest and diligent researchers, it should serve as a flashing warning to be extremely careful with any data gathered using curcumin. Unfortunately, some in the scientific community may not care as long as they are able to pump out papers. Indeed, according to Wikipedia, at least one prominent curcumin researcher had to retract several papers because of questionable “data integrity”. And there may be still darker motives: type curcumin into Google and the top results are ads touting the stuff. There’s money to be made, and even more if you slap on some scientific lipstick.

And despite specific J. Med. Chem. author guidelines to be cautious about “interference compounds” and “provide firm experimental evidence in at least two different assays that reported compounds with potential PAINS liability are specifically active and their apparent activity is not an artifact”, the journal recently published a paper fully devoted to the synthesis and SIR of rhodamine derivatives, with no consideration of mechanism nor mention that they can be problematic. (Indeed, the researchers do not even bother to include detergent in their enzymatic assay!)

All of which is to say that it’s easy to publish crap. But hopefully now, more people will recognize it as such.

09 January 2017

Fragments in the clinic: verubecestat

Of all the fragment-derived drugs in the clinic, perhaps none is so closely watched as verubecestat (MK-8931), Merck’s BACE1 inhibitor in phase 3 clinical trials for Alzheimer’s disease (AD). With tens of millions of cases worldwide, few other diseases in the developed world are as simultaneously widespread, expensive, and terrifying. And despite billions of dollars thrown at the problem, failure rates are nearly 100%. A recent open-access paper by Jack Scott, Andrew Stamford, and collaborators at Merck and AMRI in J. Med. Chem. provides an excellent overview of this latest contender.

We first wrote about Merck’s BACE1 program almost exactly seven years ago, describing how an NMR screen had provided a weak hit that was optimized to nanomolar inhibitors of the enzyme. However, the molecules could fairly be called molecularly obese. This led the researchers to trim back portions of the molecule, losing affinity but gaining cell-based activity and permeability, ultimately resulting in compound 5 (below) – which is itself a fragment. The current paper describes the optimization of this molecule.

Growing compound 5 and expanding the heterocyclic ring led to compound 7, with low nanomolar biochemical and cell-based activity. The iminopyrimidinone core was becoming increasingly crowded from an intellectual-property standpoint, so the researchers replaced this with the iminothiadiazinane dioxide in compound 9, which modeling suggested should have a similar conformation – a result confirmed by crystallography. However, the alkyne moiety appeared to be metabolically unstable. More importantly, compound 9 was only 47-fold selective against the enzyme cathepsin D (CatD). An earlier Lilly BACE1 inhibitor with a similarly modest selectivity had failed due to toxicity possibly associated with CatD, and the researchers were keen to avoid a similar fate. This led them through additional rounds of optimization, ultimately resulting in verubecestat.

In addition to having low nanomolar biochemical and cell-based activity against BACE1, verubecestat is >45,000-fold selective against CatD, has good pharmacokinetics, is orally bioavailable, and is highly soluble (1.6 mM!) It does not inhibit CYP enzymes and has good brain penetration. Rule-checkers might be surprised at this later point given the high calculated polar surface area (115 Å2), a fact the researchers attribute to an intramolecular hydrogen bond between the amide and the pyridine nitrogen, effectively masking these moieties from the point of view of membranes.

A couple potential liabilities stood out. First, one metabolite is an aniline, and anilines can be mutagenic. Reassuringly, an Ames test on this particular aniline showed no mutagenicity. Also, verubecestat is a 2.2 µM hERG inhibitor, and inhibitors of this channel can cause cardiac arrhythmias. However, this concentration is significantly higher than the highest expected in humans, and studies in primates revealed no safety issues. All of which is a useful reminder that, in our business, rules are at most guidelines, and data is king.

The paper also includes some human data demonstrating that the compound is safe at doses up to 550 mg (!) and causes a dose-dependent reduction in β-amyloid levels. With the results of the first Phase 3 trial expected later this year, we could know soon whether this is a billion dollar molecule or yet another massively expensive failure. If the former, verubecestat could be one of those transformational advances in drug discovery that comes along once in a generation. But even if fails, this is the clearest test yet of the amyloid hypothesis. And fragments made it possible.

02 January 2017

Fragment events in 2017

What better way to start the year than with a list of upcoming conferences? Here's what we know about so far.

March 5-7: The UK may have voted to leave the EU, but that's not stopping the Royal Society of Chemistry from holding Fragments 2017 in Vienna, Austria. This is the sixth in an illustrious conference series that alternates years with the FBLD meetings. You can read impressions of Fragments 2013 and Fragments 2009. Registration is already open!

April 25-26: CHI’s Twelth Annual Fragment-Based Drug Discovery, the longest-running fragment event, will be held in San Diego at a brand new venue. You can read impressions of last year's meeting here; the 2015 meeting herehere, and here; the 2014 meeting here and here; the 2013 meeting here and here; the 2012 meeting here; the 2011 meeting here; and 2010 here. Also as part of this event, Ben Davis and I will be teaching a short course on FBDD over dinner on April 25. Early registration is open until January 27.

June 6-9: Although not exclusively fragment-focused, the fourth NovAliX Conference on Biophysics in Drug Discovery will have lots of relevant talks, and is a good excuse to get to Strasbourg, France. You can read Teddy's impressions of the 2013 event herehere, and here. Registration is open now.

July 23-28: Finally, Australia is coming into its own as a destination for fragment experts, many of whom will be participating in a symposium (on July 27) that is part of the Royal Australian Chemical Institute's Centenary Congress in Melbourne. The entire event should be huge - think ACS with wombats - so if you've been looking for yet another reason to travel Down Under, this is it. Early registration is open through April 23.

It looks like the year is largely front-loaded thus far - know of anything else? Add it to the comments or let us know!

26 December 2016

Review of 2016 reviews

This year is finally coming to an end, and as we've done for the past four years, Practical Fragments will highlight some of the reviews that we didn't cover previously.

In terms of what we did cover, there were several excellent events, including the eleventh annual CHI FBDD Conference in San Diego, an inaugural meeting in Houston, and of course the first-ever major fragment event in Boston, FBLD 2016.

The twentieth anniversary of SAR by NMR was also commemorated by the eighth book devoted to FBLD, as well as a massive two volume work on lead generation. We also covered a special issue of Molecules and reviews on clinical candidates and library design.

Another review on library design was published recently in Drug Disc. Today by Ian Gilbert, Paul Wyatt, and colleagues at the University of Dundee. The researchers have built a set of 356 diverse compounds consisting of “capped” scaffolds, such that any hits could be rapidly expanded. Undergraduates did much of the actual library assembly, learning skills such as parallel chemistry and how to work with polar compounds. There is lots of nice detail in this paper, including on library storage conditions.

Practical Fragments often highlights successful fragment to lead programs, and these were the focus of a Perspective in J. Med. Chem. by Christopher Johnson (Astex) and collaborators: all 27 cases published in 2015 in which the affinity of a fragment was improved at least 100-fold to a 2 µM or better lead. Many of these were covered in Practical Fragments, including BTK, DDR1/2, ERK2, MELK, Mtb TMK, PKCθ, RET, FactorXIa, MMP-13, BCATm, PDE10A, soluble epoxide hydrolase, tankyrase, ATAD2, MCL-1, RAD51, XIAP/cIAP, and mGluR5. The paper also draws general conclusions about target types, molecular weights, cLogP values, and LE.

Targeting tuberculosis (TB) is the subject of two reviews from University of Cambridge researchers, one in Drug Discov. Today by Vitor Mendes and Tom Blundell and one in Parasitology by Anthony Coyne, Chris Abell, and colleagues. Fragment-based approaches have been more or less successful against several TB proteins, including pantothenate synthetase, CYP121, BioA, EthR, and thymidylate kinase, while other targets – such as shikimate kinase and CYP144 – have proven more difficult.

July was bromodomain month at Practical Fragments, and this target class is the subject of a review in Drug Discov. Today: Technol. by Dimitrios Spiliotopoulos and Amedeo Caflisch at the University of Zurich. The focus is on computational fragment screening methods, with examples for BRD4 and CREBBP. And while we’re on the topic of computational methods, Olgun Guvench of SilcsBio has a brief review in Drug Discov. Today on computational functional group mapping.

Rounding out target-focused reviews, Paramjit Arora and colleagues at New York University focus on protein-protein interactions (PPIs) in a Trends Pharm. Sci. paper. This covers multiple approaches to finding PPI inhibitors, including fragment-based, and also touches on hotspots and structure-based design.

It is impossible to imagine FBLD without biophysics, and this is the topic of an authoritative review in Nat. Rev. Drug Disc. by Jean-Paul Renaud (NovAliX), Chun-wa Chung (GlaxoSmithKline), U. Helena Danielson (Uppsala University), Ursula Egner (Bayer), Michael Hennig (leadXpro), Rod Hubbard (University of York) and Herbert Nar (Boehringer Ingelheim). In addition to covering all the major techniques, the paper does a great job of delving into some of the more obscure and emerging methods, providing an excellent discussion of the throughput and requirements for each technique as well as the kinds of information obtained. Although the review is broader than FBLD, the application of biophysical techniques to fragments is a major theme. The researchers also remind us that, “contrary to the belief that all drug discovery challenges are best solved through the introduction of new technologies, substantial advances can also be driven by innovative application.”

Individual biophysical techniques also received plenty of attention over the year, including three on NMR. The first, by Alvar Gossert and Wolfgang Jahnke (Novartis) in Prog. Nucl. Magn. Reson. Spectrosc., is a 44-page practical guide to identifying and validating protein ligands. This contains a wealth of information on most of the NMR methods you will ever likely encounter; it includes a handy chart summarizing the molecular weight and concentration limits for each technique, suggested workflows, and thorough discussions of potential pitfalls. The review may appear daunting to the novitiate – it is replete with equations and pulse sequences – but the writing is clear. In the end, much comes down to the concept of the “validation cross”, a rubric for assessing the integrity of both ligand and protein, and evaluating binding effects on both ligand and protein.

Two additional reviews, both from William Pomerantz and colleauges at the University of Minnesota, focus specifically on protein-observed 19F NMR. The first, a Perspective in J. Med. Chem., is a good general introduction. Despite being the 13th most abundant element on our planet, only five natural products are confirmed to contain fluorine. Introducing this element into proteins – as has been done in more than 70 cases – can be a useful approach for discovering new ligands. And if you want to start doing this yourself, a paper in Nature Protocols provides practical details.

Turning to other biophysical techniques, surface plasmon resonance (SPR) continues to be very popular, and is reviewed by Alain Chavanieu and Partine Pugnière in Expert Opin. Drug Discov. The paper provides a good general overview on using SPR for FBLD, covering the theory, history, various screening strategies, comparison to other methods, recent applications to a variety of different targets, and a suggested workflow.

Calorimetry is less commonly used for fragment screening, even though it can provide thermodynamic data. Michael Recht and collaborators at the Palo Alto Research Center and Zenobia discuss both enthalpy arrays as well as more conventional isothermal titration calorimetry (ITC) in a Methods Enzymol. chapter.

But while biophysics is important, FBLD would be nowhere without chemistry. In MedChemComm, Stefan Kathman and Alexander Statsyuk (then Northwestern, now University of Houston) review one chemical approach, covalent tethering. This touches on the original reversible (thermodynamically-controlled) disulfide tethering approach developed back at Sunesis but is primarily focused on irreversible (kinetically-controlled) methods. The paper does an excellent job summarizing challenges, potential pitfalls, design rules, and recent successes. As of early this year the Statsyuk lab had sent their 100-member covalent fragment library to nine different research groups, three of which had already identified hits. The review ends with some provocative questions, and it will be fun for practitioners to answer them as covalent approaches garner increasing attention.

Another chemical technique we’ve touched on is substrate activity screening (SAS), and this is reviewed in ChemMedChem by Pieter Van der Veken and collaborators at the University of Antwerp. All published examples are summarized, including the modified approach developed by the Van der Veken lab; some unpublished data are also discussed. The paper also includes a good general section on the subtleties and complexities of transforming substrates into inhibitors.

Finally, if all this is a bit too much, a good general review on FBLD was published in Pharmacol. Ther. by Martin Scanlon and colleagues at Monash University. This concise but thorough paper covers theory, history, library design, hit finding and characterization, and select clinical success stories. The longest section is devoted to chemical strategies for elaborating fragments, and includes some of the less commonly used methods such as target-guided synthesis, Tethering, and off-rate screening.

And that’s it for this year. Thanks for reading, and especially for commenting. Take care, do important work, and may 2017 be better than we can reasonably hope.

19 December 2016

Fragments vs Lp-PLA2: A new hope

A few months ago we highlighted work out of Astex and GlaxoSmithKline describing the fragment-based discovery of inhibitors of lipoprotein-associated phospholipase A2 (Lp-PLA2), an inflammatory disease target. Although low nanomolar compounds were identified, they had high clearance in rats. In a new J. Med. Chem. paper the team – led by Alison Woolford of Astex and Vipul Patel of GlaxoSmithKline – describes a completely new series of molecules with better pharmacokinetic properties.

Recall that the researchers had previously solved the crystal structures of 50 fragments bound to the “canyon-like” active groove of Lp-PLA2. Hydantoin 3 was one of these, and although it had no detectable activity in a biochemical assay, it did make contacts with residues in the catalytic site of the enzyme. A virtual screen of 16,000 related compounds identified 33 potential hits, and crystallographic and biochemical screening of these led to compound 5, with low micromolar activity.

The researchers were able to trim back the cyclohexyl group and remove one of the carbonyls with only a modest loss in affinity. They could also take advantage of extensive structural information from other fragment hits. For example, adding a nitrile from another fragment produced compound 13, with improved affinity.

Next, the researchers turned to the left side of the molecule, adding substituents to make a stacking interaction with a tryptophan residue in the protein – an interaction seen previously with a uracil fragment. Simple aromatic rings worked, but aliphatic heterocycles such as amines and sulfones were even better, with compound (S)-23 being among the best.

Although compound (S)-23 has only high-nanomolar potency in a biochemical assay, it is equipotent with darapladib in a whole plasma assay – despite the fact that darapladib is a picomolar inhibitor in the biochemical assay. The researchers attribute this difference to the fact that darapladib, which reached phase 3 trials, is a poster child for molecular obesity, while (S)-23 comes in with a svelte molecular weight below 400, very low plasma protein binding, and a solubility of at least 3.5 mM. The molecule is also permeable, does not inhibit CYP450s, is selective against the closely related PLA2-VIIB, and has low clearance in dogs. The clearance is higher in rats, but a closely related compound is better and also has high oral bioavailability.

This paper provides another example of finding a fragment with no detectable activity and advancing it to an attractive series. It illustrates the power of crystallography to reveal useful fragments as well as the importance of crystallography during lead optimization. Darapladib failed in two massive phase 3 clinical trials for cardiovascular disease, which probably poisoned GlaxoSmithKline's appetite for Lp-PLA2. Still, if future biological discoveries suggest new indications for this target, molecules from this series may provide a path back into the clinic.

12 December 2016

Fragments vs COMT revisited

Catechol O-methyltransferase (COMT) metabolizes neurotransmitters such as dopamine and is a validated target for Parkinson’s disease. In theory other diseases could be treated with COMT inhibitors too, but most of these contain – like dopamine itself – catechol moieties, and thus have lousy pharmacokinetics and poor brain penetration. Catechols in general are best avoided, and in a recent paper in J. Med. Chem. María Sarmiento and colleagues at Roche have found an alternate scaffold.

COMT is a magnesium-dependent enzyme, and the catechol binds to the magnesium ion. The researchers decided to target the pocket that binds the cofactor, S-adenosyl-L-methionine (SAM). They screened 6000 rule-of-three compliant fragments at 200 µM using surface plasmon resonance (SPR). First they examined wild-type enzyme in the absence of magnesium and SAM, and they also counter-screened against six variants containing mutations in the SAM binding site to exclude fragments that bound elsewhere. Even after this specificity profiling 600 hits remained. Dose-response curves whittled the number down to 200, all of which were examined using ligand-detected (CPMG) NMR. Hits from CPMG NMR were further studied using protein-detected (1H/15N HSQC) NMR. Finally, all 600 of the hits from the initial SPR screen/counter-screen assays were tested in an enzymatic assay. Only four fragments made it through all of these filters, three of which were pyrazoles such as compound 1.

Two years ago Teddy highlighted a paper from Takeda also focused on COMT, and there too pyrazoles predominated – an observation that didn’t escape the Roche researchers. In fact, compound 1 in the current paper is almost identical to compound 5 in the Takeda paper. Substructure searching and screening led to compound 4, which is identical to compound 7 in the Takeda paper. Whether COMT is really this choosy when it comes to fragment hits, or whether this reflects similarities in fragment libraries remains an open question.

But happily there’s more. The researchers used an iterative structure-guided fragment-growing approach to improve affinity. This ultimately resulted in compound 24, which is competitive with SAM and has mid-nanomolar activity. The solubility could be improved, and no other biological data are presented, but at least this paper demonstrates that it is possible to find potent inhibitors of COMT that are not phenols or catechols. 

05 December 2016

Molecules special issue:
Developments in Fragment-Based Lead Discovery

Last December the first-ever Pacifichem symposium on FBLD was held in Honolulu. Two of the organizers, Martin Scanlon and Ray Norton, invited participants to submit manuscripts to a special issue of Molecules, which has now published.

The collection starts with a very brief Foreword by me describing the Symposium itself. The first actual paper, from Qingwen Zhang and collaborators at the Shanghai Institute of Pharmaceutical Industry, WuXi AppTec, and China Pharmaceutical University, focuses on kinase inhibitors. The researchers examine fragment-sized substructures of 15 approved drugs that inhibit kinases and use these to design a high-nanomolar inhibitor of the V600E mutant form of BRAF, which modeling suggests should bind to the protein in the “DFG-out” conformation.

Next comes a fragment-finding paper from Thomas Leeper and collaborators at the University of Akron and the University of North Carolina, Chapel Hill. The researchers were interested in finding inhibitors of the glutaredoxin protein (GRX) from the pathogen Brucella melitensis, which causes brucellosis. An STD NMR screen of 463 fragments (each at 0.5 mM in pools of 5-7) resulted in 84 hits, though 75 also hit human GRX. Subsequent experiments including chemical shift perturbation and modeling identified a mM binder with modest selectivity over the human enzyme. Next, the researchers introduced several covalent warheads (including a rather exotic ruthenium analog), one of which led to improved affinity, though the stoichiometry was not determined.

The remaining papers are all reviews, starting with one on native mass spectrometry (MS) by Liliana Pedro and Ronald Quinn at Griffith University. This provides a good historical, theoretical, and practical overview of the technique generally, as well as various applications for fragment-screening. It also covers most of the published examples and discusses both the strengths (such as speed and low protein consumption) as well as the weaknesses (false positives and false negatives) of native MS.

NMR is up next, with a paper by Pacifichem organizer Ke Ruan and colleagues at the University of Science and Technology of China, Hefei. This provides a concise but detailed description of library design, ligand- and protein-detected fragment screening, structural model generation, and hit to lead optimization.

Protein-directed dynamic combinatorial chemistry (DCC) is tackled by Renjie Huang and Ivanhoe Leung, both at the University of Auckland. In addition to summarizing the theory and various literature examples, the authors do an excellent job covering the pros and cons of different types of chemistries and analytical techniques.

Next comes a review by Begoña Heras and collaborators at La Trobe University and Monash University on the subject of bacterial Dsb proteins, which are essential for disulfide bond formation in virulence factors. The review covers the biology as well as several approaches to finding inhibitors, some of which we’ve previously covered (here and here). There is much more to do: as the researchers conclude, “the development of Dsb inhibitors is still in its infancy.”

Finally, Ray Norton and colleagues at Monash University discuss applications of 19F NMR for fragment-based lead discovery. In addition to covering fluorine-containing fragments, the researchers also discuss using fluorine-containing probe molecules and – even more unusual – fluorine-labeled proteins, in this case using 5-fluorotryptophan. The paper includes previously unpublished results on how these latter two approaches can be used to understand protein-ligand interactions.

One nice feature of this journal is that it is open-access, so if you are lucky enough to be back in Hawaii this December you can pull up the papers on your smartphone while lying on the beach.

28 November 2016

How do cryptic pockets form?

Earlier this year we highlighted crystallographic work out of Astex showing that secondary ligand binding sites on proteins are common; in addition to an active site, an enzyme may have several other pockets capable of binding small molecules. Many of these secondary sites are present even in the absence of a ligand. But there are also “cryptic” binding pockets that only appear when a ligand is bound. These are the subject of a new paper in J. Am. Chem. Soc. by Francesco Gervasio and collaborators at University College London and UCB Pharma.

Cryptic pockets are appealing in part because they can salvage an otherwise unligandable target: a featureless flat surface involved in a protein-protein interaction may crack open to reveal a crevasse capable of binding small molecules. Finding these pockets computationally, though, is difficult. In the current paper, the researchers performed molecular dynamics simulations on three different proteins with known cryptic pockets, and the pockets remained mostly closed over hundreds of nanoseconds. Increasing the temperature didn’t help, and even when the simulations were started with structures of the protein-small molecule complexes (with the small molecules removed), the pockets quickly slammed shut. Further calculations suggested that the open forms of the proteins are thermodynamically unstable.

The nice thing about computational approaches is that – unlike Scotty – you can change the laws of physics. In this case, the researchers changed the simulated water molecules to be more attractive to carbon and sulfur atoms in the proteins. (They call this SWISH, for Sampling Water Interfaces through Scaled Hamiltonians). This caused the known cryptic sites to open up during molecular dynamics simulations, even in the absence of ligand.

Next, the researchers added very small fragments (such as benzene), and found that these caused the cryptic pockets to open even further. The researchers speculate that this might reflect how cryptic pockets form in the real world: a ligand could worm its way into a transient pocket, stabilizing it and exposing more room for another ligand (or a different part of the first ligand) to bind.

Of course, just because something shows up in silico doesn’t make it real; how do you avoid false positives? Once the researchers found cryptic pockets using “enhanced” water, they reran simulations using standard parameters to see which pockets remained. The researchers found that subtracting the “density” of fragments bound in a conventional molecular dynamics simulation from the density of fragments in a SWISH simulation causes minor, irrelevant pockets to disappear for their three test proteins, leaving only the known cryptic pockets. Running this subtraction experiment on the protein ubiquitin caused a couple weak superficial pockets to disappear, consistent with the absence of cryptic pockets in this protein.

SWISH is an interesting approach, and I look forward to seeing how it compares with other programs, such as Fragment Hotspots and FTMap. It would also be fun to apply SWISH prospectively to therapeutically important but currently undruggable targets to see whether it is worth taking another look at some of them.