Fragments against PPI Hot Spots

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

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

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

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

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

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

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

Supplemental Information here.

Natural products as fragments

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

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

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

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

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

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

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

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

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

WAC vs other methods: all roads lead to good fragments

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

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

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

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

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

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

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

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

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

Fragment to Lead

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

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

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

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

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

The calm before the click in chitinase

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

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

 

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

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

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

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

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

Biophysics Conference (pt 3)

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

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

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

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

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

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

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

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

Substrate activity screening for phosphatase inhibitors

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

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

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

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

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

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

Biophysics with White Wine (pt 2)

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

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

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

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

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

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

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

Biophysics in the Alsace

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

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

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

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

 

Tomorrow I will update the Mechanistic Analysis session.  

Fragment merging revisited: CYP121

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

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

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

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

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

Tessera, Tessera Everywhere

A lot of papers come across the editorial desk here at Practical Fragments.  Most of them appear because of a keyword in a search, sometimes somebody says, "Hey did you see this?", and sometimes we miss them (so if you see one that you think is interesting, doesn't hurt to ping us).  I recently came across this paper.  Well, the first thing that struck me was my branding was working, my eminence (LOL) in the field is working its way into people's thought process; I mean seriously the first line of the abstract is the entire reason my company is called what it is.  So, with great interest I dove into the paper.  So, what is it about?  The authors describe a fragment library and its use in a chemogenomics approach against three diverse target classes: GPCRs, Ligand-gated ion channels, and a kinase.  

The authors propose that promiscuous hits are driven by desolvation.  Thus, they propose a new sub-field: Fragment-based Chemogenomics (which obviously is a subset of Fragonomics) which is:

"an approach to accurately characterize protein–ligand binding sites by interrogating protein families with libraries of small fragment-like molecules."

They constructed a library of 1010 fragments "inspired" by the Voldemort Rule: number of heavy atoms 22, log P < 3, number of H-bond donors 3, number of H-bond acceptors 3, number of rotatable bonds 5, number of rings 1.  They then applied some medchem filters followed by a scaffold diversity analysis. 81 novel scaffolds were purchased to supplement underrepresented scaffolds.  Very nicely, they also identified scaffolds that were over-represented and selected those with high "cyclicity".  Lastly, the removed any compounds that showed any aggregation or preciptitation in any of the (published or unpublished) biochemical/biophysical assays.  Honestly, I wish they were more explicit here. The chemical space seems to be well represented (not shown) with underrepresentation of small aliphatic ring systems.

Physicochemical Properties of Fragment Library

They then screened the library against their various targets and, as expected, were able to identify actives with different hit rates.

This figure shows selected (how selected and are they necessarily important ones?) properties of the actives for selective and non-selective hits.  [N.B.  I am pretty sure that the second graph in (b) should be MW, not LogP again.]  Why these properties and not all of them?  To me this smacks of hiding data that do not support the central thesis.  More than a half of their actives bind to one specific target; however, 44 bind to two targets, 12 to three targets, and 11 to four targets.  Interestingly, none of the actives bind to all targets, so while they tried for some promiscuity, they did not get anything truly promiscuous.  There is no correlation between hydrophobicity and non-selectivity which they conclude means for fragments, unlike lead-like molecules, non-selectivity is NOT driven by desolvation.  

They then discuss two different types of cliffs: affinity (where two similar molecules differ in their ability to have activity) and selectivity (where two similar molecules are active against different targets).  I must admit my naivete here, but these two cliffs appear to be what I know as SAR. 

I ended up wanting more out of this paper, but for a first attempt it lays the groundwork for future refinement.  Is it a new idea? No.  The whole reason I came up with the Fragonomics name in the first place was as a joke/rebuttal to the huge array of -omics that were underway at Lilly at the time: chemogenomics, genomochemics, and so on. I would hope that this paper is a prelude to a much larger analysis of all properties and their correlation to specficity and non-specificity, down to the level of side chain and scaffolds. 

**EDIT** Dan just pointed out that he already blogged this paper back in MAY!  That's a lesson for me to blog while jetlagged.  

Biophysics bonanza of fragments for pantothenate synthetase

As Teddy just mentioned, biophysics provides multiple methods to find fragments, and it's best to use several. They may not always agree, but fragments that hit in several assays are more likely to be real. Although this requires using different skills, using diverse methods does not take a village, as illustrated by a recent paper in Proc. Nat. Acad. Sci. USA by Hernani Silvestre, Tom Blundell, Chris Abell and Alessio Ciulli at the University of Cambridge.

The researchers were interested in the enzyme pantothenate synthetase (Pts) from the bacterium that causes tuberculosis (a target we’ve covered previously here and here). They used a thermal shift assay to screen 1250 fragments from Maybridge, each at a whopping 10 mM concentration, resulting in 39 hits (3.1%) that stabilized the enzyme by at least 0.5 ˚C. Another 17% of the fragments slightly stabilized Pts, while 73% of the fragments destabilized the protein (a poorly understood phenomenon that does not seem to reflect specific binding).

Despite being relatively small, the fragment library had some scaffolds that were over-represented, and some of these were enriched among the hits, providing early SAR. Perhaps not surprisingly given the anionic character of the enzyme’s ATP cofactor and pantoate substrate, about half the hits were carboxylic acids.

All 39 hits were analyzed by WaterLOGSY and STD NMR, and only 17 showed evidence of binding, although the NMR experiments were done at a much lower concentration of fragment (0.5 mM). Competition experiments revealed that all except one of the 17 fragments bound at either the substrate or cofactor sites.

Next, isothermal titration calorimetry (ITC) was used to measure the dissociation constants of the 17 validated fragments. Measurements could not be obtained for three of the fragments; values for the rest ranged from 0.5 to 17 mM. There seemed to be a rough correlation between affinity and the extent of stabilization in the thermal shift assay, and binding was enthalpically-driven.

The 17 fragments were then soaked into crystals of Pts, resulting in 8 co-crystal structures. Most of the fragments that did not produce structures also had low affinities as assessed by ITC. Four fragments bound in a pocket normally occupied by the adenine ring of the cofactor ATP, while the other four bound in the substrate pantoate pocket. One of these bound quite deeply in this pocket and caused a conformational change in the protein. The researchers were able to obtain a structure of Pts bound to both this fragment and ATP.

There’s lots of nice data in this paper, and all the new structures have been deposited in the protein data bank. More generally, the “integrated biophysical approach” provides a practical template for applying FBLD. The paper has just four authors, and only two of them actually performed experiments according to the author contributions section. That short list provides more evidence that a very small but dedicated team can successfully find and validate fragments.

Biophysics in Drug Discovery

Folks,

Posting has been light since Dan has been traveling and I have been super busy.  The fun news is that I will be attending the Novalix conference on Biophysics in Drug Discovery this week.  The agenda is shaping up to be very exciting and I think of keen interest to the readership of this blog.  I will (WiFi-willing) be blogging daily about the conference and tweeting random thoughts about the talks. 

For those of you who will be there, I am always happy to meet up and chat.  There are a number of very interesting Fragment-focused talks, so I will be focusing on those. 

To have shape or not?

I consider the debate/discussion/civil discourse on what/where/when/why/how of 3D fragments to be one of the most interesting topics in the fragment field right now.  A paper has come out from the 3Dfrag consortium.  The first take away from this paper is we have a NEW acronym, FBHI (Fragment-based hit Identification).  Their central thesis is that fragments are too flat for certain target classes of targets, thus resulting in too low hit rates. This paper describes their efforts in pre-competitive space to prove/disprove this hypothesis.  It's fascinating.  

Using the nPMI as their metric for "3D-arity" they compare 1000 fragments representation "commercial" space to Zinc InMan Compounds fragmented by RECAP.  It is obvious that InMan has more 3D-arity, but the question is always, is this biased by target type of compound type (like natural products)?  But, to the greater point, will 3D fragments give better hit rates than 2D for certain target classes?  Many people point to the Hann complexity model a (MIP) and say no, because there is more potential "bad" interactions.  However, the authors point out if the NUMBER of sites of interaction is the same, this should not be the case.  Recent data from Evotec seems to support this they state. 

The 3Dfrag consortium's goal is to explore the role of "3D-arity" in fragment screening success.  As shown in this figure, they apply pretty standard selection rules for their collection, including the Pfizer Rule (medchemists inform on the final selection process).  

Interestingly, even after all of this, trying to select for "3D-arity", 70% of their final 200 molecules were largely flat (nPMI1+nPMI2< 1.1).  Making matters worse, 10% of the compounds could not be delivered by suppliers and 5% failed QC (by NMR). The nPMI and maximum similarity is shown below for 170 compounds.  For the maximum similarity, a right-shifted plot shows a high degree of internal similarity, while left-shifted would be more diverse.  So, it appears that their library is diverse (most compounds < 0.8).  Importantly, of the 170 compounds, they appear to fill a much wider region of 3D-space.   

One of the tools generated by the consortium is 3Dfit, a webtool that is free and allows fragments to be evaluated.  They generate up to 9 conformers (blue to red, low to highest energy) that are then plotted on the PMI triangle.  They also calculate a radar plot of molecular properties:

Molecules outside the consortium's GUIDELINES are flagged for consideration (see (b)).  The consortium has now undertaken chemistry to generate additional 3D fragments.

As the authors state:

We believe that this strategy will build a library with broader coverage of biologically relevant chemical space compared with current fragment collections and the identified hits will offer higher-quality start points for medicinal chemistry projects.

They will now experiment and try to validate (or invalidate) this hypothesis.  But, as they point, these sorts of Pre-Competitive Initiatives have difficulty getting buy in from partners willing to devote time, resources, assets to this.  They are looking for more people to participate.  I have no skin in this game, but I would love to see people step up and help this initiative prove/disprove a crucial hypothesis in Fragments.

Fluorinated fragments vs FAAH – functionally

Fluorine NMR is a topic that has come up several times on Practical Fragments (see here, here, and here). As readers will recall, the ¹⁹F nucleus can be readily detected with a properly equipped NMR spectrometer. The isotope has a wide range of chemical shifts, and sensitivity to the local environment makes it easy to detect whether fluorine-containing fragments bind to a protein. But you don’t need a dedicated fluorine-containing library: in a new paper in ChemBioChem, Claudio Dalvit and coworkers at Fondazione Istituto Italioano di Tecnologia describe using fluorinated substrates to screen a membrane enzyme.

The researchers use an approach they call n-fluorine atoms for biochemical screening (n-FABS). A substrate or cofactor is labeled with fluorine, and when this is processed by an enzyme, the resulting change in chemical structure affects the ¹⁹F chemical shift, which is easily detected by NMR. Either substrate or product (or both) can be observed, and a decrease in product can be attributed to inhibition of the enzyme.

The researchers were interested in the protein fatty acid amide hydrolase (FAAH), a membrane-bound enzyme that hydrolyzes lipids such as endocannabinoids. Of course, membrane-proteins are tough to screen using fragment-based approaches, and the fact that this enzyme processes lipophilic substrates makes things even more challenging. The researchers synthesized several fluorine-containing substrates, but most of these turned out to be insoluble or formed aggregates, even at low micromolar concentration and even in the presence of detergent. Ultimately they were able to make one substrate that was soluble at 30 micromolar, sufficient for screening.

Next, the researchers assembled a library of fragments. Although these did not need to contain fluorine for the n-FABS assay, the researchers chose to focus on fluorine-containing fragments anyway, perhaps so they could use other NMR methods to confirm binding. Of 160 commercial fluorine-containing fragments purchased, 113 showed solubility ≥ 0.1 mM, purity ≥ 75%, and no aggregation. These were combined into 23 pools of 5 and screened for inhibition in the n-FABS assay at 200 micromolar of each fragment. Pools that showed >15% inhibition were deconvoluted to find the active fragments; some contained more than one hit. This process led to a remarkably high hit rate of 16.5%. The IC₅₀ values of all 19 of these hits were then determined using n-FABS and they showed quite a range, from quite potent (3 micromolar) to low millimolar.

These are nice results and there are clear opportunities for advancing some of the fragments, but I must admit I was left wanting more. The n-FABS assay is essentially an inhibition assay, and of course there are all kinds of things that can show inhibition without proper binding. However, since all the fragments do contain fluorine, it would be straightforward to actually measure direct binding using NMR; it would be very interesting to see how many fragments show up in both assays. Perhaps we will see this in a follow-up study.

MIP and MDP

Dan and I are were at the CHI Discovery on Target meeting last week.  It is highly focused on target validation and early stage hit generation.  This is NOT a chemistry conference, although there were plenty of chemists and chemistry talks; the target audience is biologists.  As such, it was a great arena to be teaching about fragments and educating a whole different phyla of FBDD consumers.  It was also nice to meet people and have them say, "Oh, I love the blog."  Of course, I just say, try commenting, that's manna to bloggers.  The nice thing is people generally understand FBHG, unfortunately I think they generally misunderstand it.  Why?

I think part of the problem is that the Most Impactful Papers (MIPs) in the field are also the Most Destructive Papers (MDP) in the field.  So, what are the MIP for this field?  For me, the criteria are pretty straight forward: one or two papers that are seminal to understanding the field.  As you may already be guessing, my list of MIP intersects my MDP.  

Most Impactful Papers: 

The Rosebowl of Fragment Papers: SAR by NMR.  This is the paper that showed that NMR was not bound by doing structures, but was a viable screening paradigm.  It started the whole "Fragment" thing. 

• The Rationale Behind it All: The Leach and Hann Molecular Complexity paper.  If I had one paper to give to someone to explain why you should use fragments, this is it.  The first three graphs should be in every introduction to FBHG. 
• The Voldemort Rule: The Rule of Three paper.  This paper has defined what a fragment is for a decade. 
• Fragments get a name (that's never used): Fragonomics.  OK, self-referencing is not cool, so this should really be Dan's paper, the first review on FBDD. 
• Pfizer lifts the curtain: Pfizer's fragment library paper. I love this paper because it gives a great overview of how Big Pharma put its fragment library together (think laser pointers!). 

This is obviously a very short and incomplete list, and totally my opinion.  Let me know what you think MIP are in the comments.

So, what are "destructive" papers?  Those are the papers that require me to spend a lot of time explaining why what people understand is not really a good general, practical approach.  

Most Destructive Papers:

• I think THE most destructive paper is also the most impactful: SAR by NMR.  How can that be you say?  Easy.  Because of this paper, the vast majority of people who "have heard" of fragments think that you need to label protein to do use NMR for fragments.  While, target-based screening is really powerful, I don't think it should be the first thought for screening, but is more impactful on active follow up.  I can here the counterarguments coming, but WAIT there's more.  They used linking, rather than growing.  I think most people would agree that this is the "Serendipity" approach.  I think this territory is well trod on this blog.  Lastly, warheads. 
• While it had its place the Rule of Three paper has also become destructive.  This is a "hot" topic, but my main problem is the slavish devotion to an empirical "Rule".

What are your thoughts?  

I am also cross-posting this on my site http://www.quantumtessera.com/325/ and the LI group to see if maybe a different venue will generate more comments.

Programming Note

Dan and I will be co-teaching our award-winning (maybe not, but I bet our mother's think we are special) FBDD Short Course at CHI's Discovery On Target meeting.  It was standing room only in San Diego (probably because there was a shortage of chairs of something), but I bet you could squeeze in if you asked nicely.  If you are in Boston (Go Red Sox!), drop us a note, it would be great to catch up. 

Fragments vs wild-type GPCRs by SPR

Membrane proteins such as G-protein coupled receptors (GPCRs) represent a large fraction of drug targets. These are mostly overlooked by the fragment community, for two reasons. First, assays for low affinity binders are difficult to develop. Second, the proteins usually lack structural information useful for advancing fragment hits. Earlier this year Heptares provided a lovely solution to both problems by generating stabilized mutant GPCRs, which could be screened using surface plasmon resonance (SPR) and characterized crystallographically. In a new paper in ACS Med. Chem. Lett., Iva Navratilova, Andrew Hopkins, Robert Lefkowitz, and a multinational team at the University of Dundee, Duke University, and the University of North Carolina Chapel Hill report using SPR to screen fragments against a wild-type GPCR.

The researchers chose the human β2 adrenergic receptor, which has served as a model GPCR for a variety of biological and biophysical studies. They expressed this with a His₁₀ tag on the C-terminus and used a conventional nickel chip to immobilize the protein in the presence of detergent. The immobilized protein was able to bind to a known agonist and antagonist with dissociation constants similar to those reported in the literature, suggesting that it was folded correctly.

Next, 656 fragments were screened against the protein at 50 micromolar each. Using a surface containing β2 adrenergic receptor blocked with a known high-affinity, slowly dissociating agonist as a reference, the researchers looked for fragments that bound selectively to the surface containing the unblocked protein. A total of 81 fragments were then examined more closely in dose-response curves, yielding five confirmed hits, with dissociation constants ranging from 17 nM to 22 micromolar.

All five of these hits were tested in a conventional radioligand competition assay, confirming their binding. Interestingly, four of the five ligands were N-arylpiperazines, a class of molecules that the Heptares team also found as ligands for the β1 adrenergic receptor. When tested against this GPCR most were not selective, but one did show some selectivity for β2 adrenergic receptor against a panel of 27 GPCRs.

The fragment hits were then tested for activity in a cell-based assay, and all of them inhibited signaling. This illustrates a general complication with binding (versus functional) assays: with simple enzymes, once you’ve found a binder, it is probably either an inhibitor or has no effect. With GPCRs, a binder could be an agonist, an antagonist, a partial agonist, an inverse agonist, a neutral antagonist, or something else entirely; you need to go into cells quickly to figure out what you’ve got.

I do wonder whether it would be possible to screen at higher concentrations to look for weaker ligands, particularly for more challenging GPCRs for which no small molecule ligands are known. Still, the fact that SPR works as well as it does for a native GPCR is quite impressive. I suspect that we’ll see more and more fragment screening by SPR on membrane proteins. Whether folks will be comfortable optimizing fragment hits in the absence of high-resolution structures, though, remains to be seen.

Rule of five versus rule of three

Metrics (such as ligand efficiency) and rules (such as the rule of three) seem to be some of the more controversial topics around here. If you aren’t experiencing metric-fatigue, it’s worth checking out a recent (and free!) “Ask the Experts” feature at Future Med. Chem., in which four prominent scientists weigh in on the utility of the rules of five and three.

Monash University’s Jonathan Baell (of PAINS fame) notes that, as of early 2013, the original 1997 Lipinski et. al. rule of five paper (and the 2001 reprint) had been cited more than 4600 times! Baell holds that, of the properties covered by the rules – molecular weight, lipophilicity, number of hydrogen-bond donors (HBD), and number of hydrogen-bond acceptors (HBA) – the property lipophilicity is probably the most important. Although he agrees that rules can be too strictly applied, he also asks:

What sum value is represented by the dead-end investment that the world never saw because of application of a Ro5 mentality?

I think this is a good, often-overlooked point. It is easy to find examples of drugs that violate the rule of five or programs that were killed by rule-bound managers with limited vision, but, as GlaxoSmithKline’s Paul Leeson says, “there is massive unexplored chemical space within the Ro5, which is available to innovative chemists.” Why not put much of the focus here?

Of course, readers of Practical Fragments are probably thinking as much about the rule of three as the rule of five, and one of the main criticisms of that rule, particularly by Pete Kenny, has been the fact that it is not clear how to define hydrogen-bond acceptors: do you count all nitrogen and oxygen atoms, including for example an amide –NH? I think the common-sense answer would be no, and Miles Congreve, the first author on the original rule of three paper, seems to agree. He also notes that the number of hydrogen bond acceptors seems to be less important in general than the number of hydrogen bond donors, which is negatively correlated with solubility, permeability, and bioavailability.

Given last year’s poll on the maximum size of fragments people allow in their libraries, it looks like most people are already capping molecular weight well below 300 Da, which skews the other parameters toward rule of three space. That said, Congreve does warn that commercial fragment libraries “contain too many compounds that are close to 300 Da, rather than containing a distribution of compounds in the range of 100 – 300 Da,” a statement borne out by by Chris Swain’s analyses. Of course, the larger you get, the more possibilities there are, and the optimal property distribution of a fragment library is still a matter of debate.

Ultimately I think many people will agree with Leeson, who says that “there are probably sufficient metrics in the literature today,” and with Celerino Abad-Zapatero, who notes that “additional rules will not be the answer in the long run.” On this note I promise no more posts on metrics or rules – for at least a month!

More thoughts on the Astex-Otsuka marriage

Teddy already highlighted the planned $866 million acquisition of Astex by Otsuka, and I thought I’d add a bit of context. Astex Therapeutics was founded in 1999, just three years after publication of the Abbott SAR by NMR paper that arguably launched widespread interest in fragment-based lead discovery. From the outset, Astex focused heavily on crystallography, which was somewhat unusual at the time; Vicki Nienaber’s seminal SAR by Crystallography paper only came out in 2000.

Astex researchers have made many practical contributions to FBLD, from the (sometimes controversial) rule of three to the LLE_AT metric to the Astex Viewer familiar to anyone who has seen a presentation from the company. More than 100 publications have come from Astex, including one of the earliest comprehensive reviews of the field. And the company has also delivered: of 28 fragment-derived compounds to make it into the clinic, Astex has had a role in nearly a quarter, including AT13387, AT7519, AT9283, JNJ-42756493 (with J&J), LEE011 (with Novartis), AT13148, and AZD5363 (with AstraZeneca and ICR).

In terms of price, $866 million is indeed a tidy sum, more than the up-front Daiichi Sankyo paid for Plexxikon (though a bit under the total deal value of $935 million) and more than an order of magnitude higher than the $64 million Lilly paid for SGX back in the dark days of 2008. Even with close to a billion dollars on the table, some are calling the price too low, with one analyst suggesting Astex is worth $13 per share rather than the $8.50 offered by Otsuka.

Of course, the Astex pipeline is not entirely fragment-based; a merger with SuperGen in 2011 brought in a marketed product (decitabine) as well as other clinical compounds. Still, from what Otsuka has said publicly, it does appear that the FBLD technology was a major driver: it is the first item mentioned under the heading “Objectives of the Acquisition.”

As Derek Lowe pointed out over at In the Pipeline, Japanese firms have a good track record of not breaking or shuttering acquired companies; last I checked Plexxikon was still going strong. Hopefully this will hold true for Astex as well. Practical Fragments offers congratulations and wishes continued success to everyone involved.

What Do Fragments Get you?

Parroting In the Pipeline, what do fragments get you?  886 Million dollars, that's what!  As pointed out by the buying company:

"Astex's unique fragment-based drug discovery technology [Ed: emphasis added] and clinical oncology research and development capabilities, born out of the passion of its researchers, exemplify our corporate mottos and belief in "Sozosei (Creativity) and Jissho (Proof through Execution). I would like Otsuka Pharmaceutical to continue to respect Astex's uniqueness and leverage it to bring further growth for Otsuka Pharmaceutical."

Congratulations to the folks at Astex, the next time you see them at a conference, make sure they pick up the check.

Another NMR Tool...

There are many things which aid in the successful prosecution of fragments.  Most people would agree that structural information is one of those things.  However, in many cases there is no structure, nor any hope of obtaining one.  Many different methods have been developed to try to address this gap.  Oftentimes they are impractical, sometimes they are useful.  In this paper, Gregg Siegal, Marcellus Ubbink, and co-workers from his academic lab present a new NMR-based structural tool.  [Editor's Note: I used to have a business relationship with Gregg's commercial side.]  So, is this a practical or impractical tool?  You can skip down to the bottom for the answer, or keep reading and follow me down the rabbit hole.

Their approach is not to generate high-resolution structures, but low resolution models of how initial fragments bind to the target. To accomplish this, the use pseudocontact shifts  (PCS)induced by paramagnetic ions.  To those of you whose eyes just glazed over, let me explain.  We typically only use diamagnetic atoms in NMR, because paramagnetic atoms cause line broadening, sometimes to extinction.  For ease of explanation, the PCS is similar to any dipolar coupling, it is a way to relax between atoms, like the NOE, but with a longer distance dependence r^-3 (PCS), vs. r^-6 (NOE).  However, with good decisions like the choice of the ion, the placement of the ion, and so on, you can get subtle effects on your ligand, rather than wiping it out. In the end, you need to know a few things: the actual fraction of ligand bound, the structure of the target (or a good homology model), and the PCS tensor (see below).  This work used rigid, paramagnetic ion binding tags attached to the target via engineered disulfide linkages (CLaNP).

 In total, they made three different tagged proteins and used Yb3+ as the paramagnetic ion and Lu3+ as the diagmagnetic ion. 

This data represents a mixture of bound and free ligand, so using the experimentally determined Kd and the known concentrations of ligand and target, the % bound ligand can be determined.  This can then be converted into PCS of only the bound state. 

However, the authors then tried to calculate the tensor, which is necessary to calculate the orientation of the PCS tensor.  When compared to the orientation of the ligand determined by NOE, there was an 4.7 A RMSD.  This approach only gives the relative location of the binding site.  When they formally calculated the PCS tensors they were able to get a better match of the PCS-derived orientation compared to the NOE-derived, but still not perfect agreement.  That is expected for different methods which can be considered orthogonal.  There ends up being a lengthy discussion of the shortcomings of this method and why it could be possibly better than NOE-based methods, in particular it does not need labeled protein.  However, I would argue if you are not producing your protein in E. coli it is likely being made in insect cells or mammalian cells.  In the case of insect cells, why would you wait two months, to get ligand orientation information on an initial hit?  The project has come and gone on the initial screen hits by that time.

While this is a interesting approach academically, it is really impractical.  Why?  As the authors state, this method is best for ligands with high micromolar to low millimolar affinity.  This positions it firmly in the very early stages of FBHG.  You need to have the structure of the target, or a good homology model.   You need to generate multiple mutants (they do state you can get by with only two positions, but three is better).  You need to do some seriously involved computation; something that is not routine at all.  This would be a much better tool if it could be robustly used at late hit expansion/early lead generation, but that doesn't seem likely.  So, you have what is largely an academic tool for generating models of ligand-target binding with fragments, but not something that would be routinely used.