Imidazole lipophilicity revisited

It's almost three months since I arrived in Brasil to spend year in São Carlos at Universidade de São Paulo. One of the advantages of being back in academia is that access to literature is a lot better and I managed to dig up some cyclohexane/water partition coefficients for a couple of the compounds that featured last year's Lipophilicity Teaser. While octanol/water doesn't appear to 'see' the hydrogen bond donor that is unmasked by moving the methyl group, the cyclohexane/water partitioning system certainly does. Readers with an interest in Physical-Organic Chemistry might like to think about how tautomerism might affect partition coefficients. There are also a couple of lipophilicity-based items (logP versus logD for ADMET discussion and poll on correlation of pharmacological promiscuity) that are current in the FBDD LinkedIn group so why not drop by and join the fun there.

 

Literature cited

Abraham, Chadha, Whiting & Mitchell, Hydrogen bonding. 32. An analysis of water-octanol and water-alkane partitioning and the Δlog P parameter of Seiler. J. Pharm. Sci. 1994, 83, 1085-1100. DOI

Radzicka & Wolfenden, Comparing the polarities of the amino acids: side-chain distribution coefficients between the vapor phase, cyclohexane, 1-octanol, and neutral aqueous solution. Biochem. 1988, 27, 1664-1670. DOI

How not to do FBDD?

It’s not often that bloggers get responses from the authors of the articles that they’ve featured and it’s much rarer still for a reviewer of a manuscript to break cover and join the discussion. However, that is exactly what happened when an article on (Trimethoprim-resistant, Type II R67) Dihydrofolate Reductase got coned in the searchlights at Practical Fragments. Soon it had also been Pipelined as well and the matter would normally have ended there. At this point both the corresponding author and one of the reviewers of the manuscript got in touch with both Teddy and Derek, which prompted a follow up post at Practical Fragments and two in the Pipeline ( 1 | 2 ). I have a particular interest in the relationship between journals and blogs and thought it would be a good idea to see what all the fuss was about.

The article describes a fragment screen (using a biochemical assay) against the target and the synthesis and characterisation of two inhibitors based on one of the fragment hits. None of the IC₅₀ values measured for the fragment hits was less than 1.7mM and I was curious why the authors ran the assay at 30 x K_m for NADPH since this (presumably) meant that fragments needed to be screened at higher concentrations. Although many in the FBDD community are dismissive of the use of biochemical assays for fragment screening, I believe that in some situations they do represent a viable way forward, but only if appropriate precautions are taken. The authors do hint at attempts to deal with interference (e.g. reducing path length) but I’d like to have seen some evidence that they were fully aware of the problem and had taken the appropriate steps to recognise and, if necessary, correct for interference. Some discussion of intracellular co-factor and substrate concentrations would also have been helpful since it would help to place the IC₅₀ and K_i values for the structurally elaborated compounds in the appropriate context. Some readers may have encountered this issue when comparing assay results for ATP-competitive kinase inhibitors using different ATP concentrations.

Putting my concerns about the assay aside, I do have some issues about how the fragments were selected for assay. We screen fragments to ask questions about the structural requirements for binding and you don’t always need a large screening library in order to ask good questions. Once you’ve got hits, it’s important to follow these up with carefully chosen analogues to build SAR that can (hopefully) be mapped onto synthetically elaborated structures. This is especially critical when the hits are weakly active by a biochemical assay and neither crystal structures for bound fragments nor an orthogonal assay are available. Given the results presented in Figure 1 and Table 1, I was really surprised that they didn’t assay the unsubstituted 2-naphthoic acid. While assaying fragments 4a and 4c represents a step in the right direction for following up fragment 4, I’d be looking for something along the lines of the following scheme from researchers who were thinking in terms of molecular recognition:

In my view, the authors didn’t demonstrate any real insight in the way that they selected the compounds for their fragment screening library or how they explored fragment SAR. Although they had a protein structure available for the target, they don’t seem to have used this for selection of fragments for screening. The execution of the fragment screen and its follow up would be less of an issue had the authors managed to get crystal structures for fragment-protein complexes or discovered a series of highly active compounds with interesting SAR that killed bacteria. However, all the authors really have to show for having run this fragment screen is two inhibitors with two dianionic inhibitors of low ligand efficiency. I couldn’t help wondering why linking fragments led to such a catastrophic loss of ligand efficiency. I also couldn't help wondering why all the ligand efficencies were negative and in the wrong units since you'd have expected the reviewers (there were at least two of them) to pick this sort of thing up.

The process by which the authors used the output of the fragment screen to ‘design’ the elaborated molecules is especially deserving of comment. The imidazole carboxylic acid was selected as a starting point for elaboration because it was more selective with respect to the human enzyme. Personally, I’d be wary of placing too much weight on selectivity given the possibility of interference in these assays but I’d certainly be prepared to put my concerns to one side if the elaboration process had led to some really active compounds that were indeed selective. The idea of linking two carboxylic acids appears to have been inspired by the structure of Congo Red. Dyes typically need to stick to the organic material that they are used to color and in the early days of high throughput screening, we’d see a lot of dyes like this coming up as active. I was surprised that the authors chose to link from the carbon between the imidazole nitrogen atoms because the fragment with a methyl substituent at this position showed no inhibition at 10mM and this is precisely the sort of result that is invoked to support the assertion that fragment 4 is selective with respect to the human enzyme. However, I’d like to get back to the structure of Congo Red...

Congo Red is actually a pretty rigid beast and can only exist in extended conformations with each half of the molecule tending to coplanarity. If you’re going to use Congo Red as a template for design then it really would be a good idea to check that your designed molecules really do overlay with the template. It really isn’t rocket science. At this point, it really should be mentioned that the authors had got to their inhibitors without any of what I’ll call geometric modelling. This might have been more acceptable if the compounds had been more potent, more numerous and actually killed bacteria. Reading this article caused me to dream up new efficiency metric for medicinal chemistry journal articles in which the sum of the pIC₅₀ values for all the compounds in the article is divided by the number of pages in the article...

This is probably a good point to note that the authors do not appear to have made any real use of the crystal structure of the target either to select fragments or in the design of the elaborated molecules. Therefore, I was somewhat surprised to see over two pages of the article devoted to docking study of the inhibitors. They appeared to have some difficulty in reproducing the (crystallographically determined) binding mode of the NADPH ligand using the docking software so a reader could legitimately ask why she or he should believe the proposed binding modes for the other ligands. One common feature of the predicted binding modes is that the molecules are ‘folded into a U shape’ and you can see these in Figure 4. Bending the molecules like these into ‘U shapes’ costs energy and many (most) docking programs don’t account for conformational energy costs particularly well. Also, Congo Red (which appears to have provided the basis for the dicarboxylic acid inhibitors) can’t bend into a ‘U shape’ so if these inhibitors really are binding in the proposed manner then they can’t really be functioning as the mimics of Congo Red that they were 'designed' to be. Also the authors note that some other researchers have proposed ‘a linear mode of binding’ (whatever that means) in which the molecules do not bend into ‘U shapes’. I couldn’t help wondering why they’d not bothered to dock Congo Red...

Neither of the inhibitors showed a measurable effect on bacteria even when tested at 1mM and the authors suggested ‘that these compounds do not penetrate into E. Coli’. This may well be the case but potency also needs to be seen in the context of the physiological concentrations of both substrate and co-factor. The authors claim selectivity for the compounds 8 and 9 with respect to human DHFR although the maximum test concentrations used were both less than ten-fold above the relevant IC₅₀ values for the R67 enzyme.

The authors presented cytotoxicity data for compounds 8, 8a, 8b and 9 and noted that all except 8b showed weak cytotoxicity. It’s worth noting that the IC₅₀ (34 µM) for 8a in the cytotoxicity assay is almost two-fold lower than the IC₅₀ for 8 against the target enzyme. Compounds 8 and 9 may only be weakly cytotoxic but they still appear to kill the 3T6 cells in the cytotoxicity assay more efficiently than they kill bacteria. Apparently 8b has some antiproliferative effects against a number of parasites although it is not clear what relevance this has to R67 DHFR inhibition since this compound shows no measurable activity against this enzyme..

That brings me to the end of my review of this article and it’s now time to sum up. The authors conclude their article by stating:.

‘This work provides inspiration for the design of the next generation of inhibitors.’.

and I believe this statement is as inaccurate as it is immodest. I thought that the execution of the fragment screen, in particular the exploration of fragment SAR, was weak. The ‘design’ didn’t hang together (e.g. linking benzimidazole from C2 where methylation led to decrease in inhibition by fragment) and may have been overly-influenced by some of the authors’ interest and previous experience in the synthetic chemistry of linked benzimidazoles. The docking studies appeared to have been done after the compounds had been ‘designed’ and synthesised and in my view, contributed absolutely nothing. I suggest the authors, reviewers and editor of this article all read (and make sure that they understand) the recent J. Med. Chem. guidelines on computational medicinal chemistry. All of this would have been less of an issue if the authors had actually discovered a novel series of R67 DHFR inhibitors that actually killed bacteria. However, the two compounds, which they optimistically describe as a class, that they’ve discovered are unexciting from the pharmaceutical perspective. Perhaps if they'd pursued the fragments a bit more imaginatively and done some real design they might have come up with something more exciting that was actually anti-bacterial. However, that is speculation and I believe that the Journal's readers and subscribers deserve better than this.

To be quite honest, I wouldn't normally review an article like this and only did so because of the reaction(s) generated by the original blog posts and my interest in the relationship between blogs and journals. A specialist scientific journal needs to shape the debate that drives its field forward and create an environment in which articles are discussed publicly. The authors of those articles should be active participants in the discussion. I'll finish with one of the comments from Derek's second post:

'I agree the online discussion about the article is great, but it does need to be more tightly linked to the original publication. No reason why the original publishers can't allow public comments.'

Earth calling Journal Editors, the ball's now in your court and we'd all really love to hear what you've got to say.

Literature cited.

Bastien, Ebert, Forge, Toulouse, Kadnikova, Perron, Mayence, Huang, Eynde & Pelletier, Fragment-Based Design of Symmetrical Bis-benzimidazoles as Selective Inhibitors of the Trimethoprim-Resistant, Type II R67 Dihydrofolate Reductase. J. Med. Chem., 2012, 55, 3182–3192 | DOI.

Mayence, Pietka, Collins, Cushion, Tekwani, Huang & Eynde, Novel bisbenzimidazoles with antileishmanial effectiveness Bioorg. Med. Chem. Lett. 2008, 18, 2658–2661 | DOI

Stahl & Bajorath, Computational Medicinal Chemistry. J. Med. Chem., 2011, 54, 1–2 | DOI.

Scoring non-additivity

Getting the most from target-ligand interactions is the essence of structure-based design and many of us believe that fragment-based methods represent an excellent way to do this. Recently, I came across an article that looked at cooperativity and hot spots in the context of scoring functions. Given that the Roche group appear to be trying to establish themselves as authorities on molecular recognition, (see review of this article) I decided to take a closer look.

We often think of molecular recognition in terms of contacts between molecules with each contact contributing (sometimes unfavourably) to the change in free energy associated with formation of complex. Although this is an appealing picture (and one we use in scoring functions as well as some QSAR approaches to affinity prediction) it is worth remembering that the contribution of an individual contact to affinity is not strictly an observable or something that you can actually measure.

Reading the article, I encountered the following in the introduction:

‘Classical experiments by Williams et al. on glycopeptides antibiotics¹⁸ and on the streptavidin-biotin complex¹⁹ have shown that binding causes these systems to be more rigid and thus enthalpically more favorable. The loss of binding entropy caused by the reduced motion is more than compensated by the gain in enthalpy achieved through tighter interactions.’

I would challenge the assertion that making a system more rigid necessarily results in a decrease in its enthalpy. If you took a complex and rigidified it in the wrong way you would actually increase its enthalpy to the extent that it exceeded that of the unbound complexation partners so it’s probably worth taking a closer look at rigidity in the context of binding before moving on. When people try to ‘explain’ affinity in terms of structure it usually results in a lot of arm waving which typically increases when entropy and enthalpy enter the equation. One way to think about entropy is in terms of constraints. As you increase the extent to which a system is constrained you decrease its entropy. When a ligand binds to a protein it becomes constrained in a number of ways. Firstly, it has constrained to move with the protein and introducing this constraint results in a loss of translational entropy. Secondly, binding restricts the conformations available to the ligand and we can think of the ligand as being constrained to exist in a subset of the conformations in which it could exist before binding. We can think of the protein as constraining the conformational space of the ligand by modifying the potential energy surface of the ligand. Entropy changes associated with binding quantify changes in the degree to which the system (protein, ligand and solvent) is constrained. As a system becomes more constrained it moves less and it is more correct to say that the additional degree of constraint ‘causes’ the reduced motion than to say that the reduced motion ‘causes’ the reduction in entropy.

However, I don’t want this post turning into Thermodynamics 101 and should get back to the featured article. The scoring functions used to assess potential binding modes (poses) typically include a weighted sum of the contacts between between protein and potential ligand. Setting these weights appropriately is the biggest problem in development of scoring functions of this nature. The authors of the featured article attempt to go beyond an additive model by using subgraph network descriptors to capture cooperativity. Many of the ligands, protein structures, measured potencies and affinities used in this study appear to be proprietary. Personally, I found it difficult to figure out exactly what the authors had done and I would challenge anybody to repeat the work. An editorial (admittedly in Journal of Medicinal Chemistry rather than Journal of Chemical Information and Modeling) is very clear on this point and the authors might want to check it out before submitting further manuscripts on this theme:

‘Computational methods must be described in sufficient detail for the reader to reproduce the results’

Molecular interactions form the basis of this work and the authors ‘propose a comprehensive set of attractive and repulsive noncovalent interactions’ for which the atom types are defined using SMARTS notation. Since the authors don’t actually tell us what SMARTS patterns they use to define the interaction types, Tables 1 and 2 raise more questions than they answer. Does the hydrogen bond interaction include charged donors or acceptor and, if so, are ions in contact also considered to be hydrogen bonded? What is the difference between a dipolar interaction and a hydrogen bond? Why do dipoles interact with cations but not anions? If the cation-π interaction is so favourable, why is the anion-π interaction considered as insignificant? You can claim

‘a newly defined comprehensive set of noncovalent contacts that encode state-of-the-art knowledge about molecular interactions’

but if you’re not going to tell us how you do the encoding, readers are going to be left with the impression that the statement is as inaccurate as it is immodest.

Using a single pairwise interation coefficient to score all hydrogen bonds severely limits the accuracy of an interaction-based scoring function, especially if hydrogen bonds involving charged groups are to be included. Whilst cooperative effects are relevant to the scoring of hydrogen bonds, the inherent variability of individual hydrogen bonds is likely to be as least as important. One way to make hydrogen bonds pay their way in aqueous media is to balance the strengths of donor and acceptor. If an acceptor is ‘too weak’ for the donor, the former is unable to fully ‘compensate’ the latter for its ‘lost solvation’. I’ve discussed this in the introduction to a 2009 article on hydrogen bonding if you’re interested in following this up a bit more and you might also want to look at these measured values of hydrogen bond acidity and basicity measurements for diverse sets of donor and acceptors.

Although values of hydrogen bond acidity and basicity measured for prototypical (e.g. single donor or acceptor) model compounds are useful in molecule design, they don’t tell the whole story. The environment (e.g. at bottom of narrow hydrophobic pocket) of a hydrogen bonding group in a protein can greatly affect its recognition characteristics. It may yet prove possible to model hydrophobic enclosure accurately in terms of cooperative contacts between protein and ligand but I'll not be holding my breath. There does seem to be a view that the contribution of hydrogen bonds to affinity will be small and my former Charnwood colleagues assert that a neutral-neutral hydrogen bond will contribute no more than a factor of 15 (1.2 log units). However, at Alderley Park (where we never used to pay too much attention to directives from ‘Camp Chuckles’) we managed to find a couple of examples where replacing CH in a pyridine ring with nitrogen resulted in potency increases of 2 log units. I guess the jury is still out on this one.

The authors of the featured article used their interaction types and subgraph network descriptors to develop a new scoring function which they call ScorpionScore (S_Scorpion). They assembled four training sets and

‘maximized the Spearman rank correlation coefficient for affinity data sets I–III and minimized deviations in absolute affinity differences for training set IV in parallel, with each data set being weighted by 25%’.

It was not clear why they split the data that comprise the sets I-III in this manner but it will have the effect of slightly emphasising the contribution from set I, which is smaller (31) than the other two sets (II: 46; III: 42). It would have been helpful to explicitly state the function that they’d optimized and I’d also like to have known exactly how they removed

‘highly correlated (Spearman rank correlation ρ > 0.8) terms from the set’.

The performance of the Scorpion scoring function is summarised in Table 7 of the article. There only seems to be an advantage in using the network terms for training sets I (Neuraminidase) and IV (Activity Cliffs). Significantly, the performance of S_Scorpion is no better than a simple count of heavy (non-hydrogen) atoms for data set III (Diverse) which one might argue is the most relevant to virtual screening against and arbitrary target. The results presented in Table 7 quantify how well the different scoring schemes fit the training data and one would expect that using the network terms (S_Scorpion) will be better than not using the network terms (S_pairwise) simply because more parameters are used to do the fitting. The authors do compare S_Scorpion with other scoring functions (Tables S4 – S6 in the supplemental material) although they did not make the crucial comparisons with S_pairwise in this context. In my opinion, the authors have not demonstrated that the network terms significantly increase the performance of the scoring and I would question the value of the interaction network diagrams (Figures 8 -16).



I’ll finish off by making some comments on the (mostly proprietary) assay results used to develop S_Scorpion. The authors state:

‘It is important to note that we optimize against a combination of training sets, in which for each set ligand affinities were determined with the same assay and for the same protein.’

I would certainly agree that mixing results from different assays is not a good idea when attempting to relate structure to activity. However, it is also important to note that the term ‘affinity’ strictly refers to K_d and this is not the same quantity as IC₅₀ (which generally depends on substrate and co-factor concentrations). For example, you can account for intracellular ATP concentrations by running kinase inhibition assays at high ATP concentration. However, there is another issue that one needs to consider when dealing with IC₅₀ values and I’ll illustrate it with reference to the compounds shown in the figure at the top of this paragraph which I’ve pulled from row 13 of Table 5. The pIC₅₀ values shown in this figure are certainly consistent with cooperative binding of the two chlorine atoms. For example, the effect of adding a 2-chloro substituent to 1 (ΔpIC₅₀ = 5.6 – 4.4 = 1.2) is a lot smaller than that observed (ΔpIC₅₀ = 8.0 – 5.8 = 2.2) when 2 is modified in an analogous manner. When trying to quantify cooperativity it is very important that all IC₅₀ values are measured as accurately as possible and the assay often needs to have a high dynamic range. Our perception of cooperativity for these compounds depends very much on the pIC₅₀ for compound 1 being measured accurately and assay interference increasingly becomes an issue for weaker inhibitors. My main reason for bringing this up is that it may be possible to quantify the degree of interference in order to make the appropriate corrections and this blog post may also be of interest.

As stated earlier, I do not believe that the authors have shown that network descriptors add anything of real value. For the largest training set(II: PDE10), the both S_Scorpion and S_pairwise are outperformed by a single parameter (number of heavy atoms). Some readers may think this assessment overly harsh and I'd ask those readers to consider an alternative scenario in which QSAR models with different numbers of parameters were ranked according to how well they fit the training data. Thinking about a scoring function as a QSAR model may be instructive in other ways. For example, QSAR models that are optimistically termed 'global' may just be ensembles of local models and to equate correlation with causation is usually to take the first step down a long, slippery slope.

Literature cited

Kuhn, Fuchs, Reutlinger, Stahl & Taylor, Rationalizing Tight Ligand Binding through Cooperative Interaction Networks. J. Chem. Inf. Model. 2011, 51, 3180–3198 DOI

Bissantz, Kuhn & Stahl, A Medicinal Chemist’s Guide to Molecular Interactions. J. Med. Chem. 2010, 53, 5061–5084 DOI

Stahl & Bajorath, Computational Medicinal Chemistry. J. Med. Chem., 2011, 54, 1–2 DOI

Kenny, Hydrogen Bonding, Electrostatic Potential, and Molecular Design. J. Chem. Inf. Model., 2009, 49, 1234–1244. DOI

Abraham, Duce , Prior , Barratt , Morris & Taylor, Hydrogen bonding. Part 9. Solute proton donor and proton acceptor scales for use in drug design. J. Chem. Soc., Perkin Trans. 2, 1989, 1355-1379 DOI

Abel, Wang, Friesner & Berne, A Displaced-Solvent Functional Analysis of Model Hydrophobic Enclosures. J. Chem. Theory Comput. 2010, 6, 2924–2934 DOI

Davis & Teague, Hydrogen Bonding, Hydrophobic Interactions, and Failure of the Rigid Receptor Hypothesis. Angew. Chem. Int. Ed. 1999, 38, 736-749 DOI

Bethel et al, Design of selective Cathepsin inhibitors. Bioorg. Med. Chem. Lett. 2009, 19, 4622–4625. DOI

Shapiro, Walkup & Keating, Correction for Interference by Test Samples in High-Throughput Assays. J. Biomol. Screen. 2009, 14, 1008-1016 DOI

JCAMD 25th Anniversary Issue

The editors of the Journal of Computer-Aided Molecular Design commissioned a number of Perspectives on the state and future of the field to commemorate the journal's 25^th anniversary. They have made this content open access for a limited period (I believe 3 months) so go check it out while the access is still open.

Fragment lead identification by SPR

FBDD is a maturing field and one sign this maturation is the publication of a volume of Methods in Enzymology devoted to the subject. The article in this collection that most interested me was the review by Anthony Giannetti on the use of Surface Plasmon Resonance (SPR) in Fragment Lead Generation. The review is described as a ‘comprehensive walk-through’ and in-depth treatment of topics such as target immobilization and buffer/compound preparation justifies this description. I’m still working my way through some of the data analysis sections...

The target is tethered to a surface in SPR and this is usually referred to as ‘immobilization’, which is an unfortunate term, albeit the one that is most commonly used in the literature. Vendors of competing assay technologies (who would naturally prefer you to use their technology instead) often present this as a weakness of SPR. One concern is that tethering will compromise the ability of the target to bind ligands and the review does cite a couple of articles which compare affinities measured with SPR to those measured using methods such as isothermal titration calorimetry.

The system in an SPR assay is heterogenous, which is another way of saying that the concentration of protein is not uniform, particularly in the direction perpendicular to the surface to which it is tethered and this creates some interesting possibilities. Tight binding occurs when the value of the ligand K_d is lower than the concentration of the protein to which it binds. We typically configure assays for measuring affinity and potency so that ligand concentration is significantly greater than protein concentration. This means that ligand binding does not affect the concentration of unbound ligand and the math is a whole lot easier if you can make this assumption. If, however, the protein concentration in your assay is 1nM and you want to measure the potency for a compound with an IC50 of 0.01nM you’re going have a problem because you’ll need the compound at a concentration of 0.5nM in order to occupy half the binding sites. In enzyme inhibition assays, the concentration of the enzyme limits sets an upper limit on the potency that you can measure and this may be an issue for attempts to estimate the maximum potency of ligands.

In a heterogeneous system, things are not quite as simple because concentration is less clearly defined and you need to think in terms of quantities (in molar terms of course) of protein and ligand. Localising a small amount of protein on the chip surface rather than having a larger amount of protein distributed evenly throughout the sample volume means less depletion of the reservoir of unbound ligand when 50% of binding sites become occupied. Also in the SPR assay, the solution of ligand flows over the chip, making depletion of unbound ligand even less of a problem.

Tight binding is not usually a problem when screening fragments and the main reason for bringing up the subject was to get you thinking a bit about assays. There are a number of technologies for detecting the binding of fragments and quantifying the affinity with which they bind. This raises a couple of questions. Firstly, to what extent do we need new screening technologies for FBDD? Secondly, which weaknesses in the current methodology should be addressed with the highest priority?

Literature cited

Giannetti, From experimental design to validated hits: A comprehensive walk-through of fragment lead identification using surface plasmon resonance. Methods Enzymol. 2012, 493, 169-218. DOI

Anyone for tennis?

So I'm back in Melbourne, one of my favorite cities, and am currently amusing myself by looking for transition states which some of you will know are merely first order saddle points on potential energy surfaces. I actually find reactivity a lot more interesting than binding so it's great that my hosts are interested in these problems. Of course there's a lot more to do in Melbourne than searching for imaginary forces and negative frequencies and last week I went to watch the Australian Open.

Before I get to the tennis, I'll mention the Research Works Act which can be seen as protectionism (of academic publishers) and readers of this blog will know that I have some issues with journal editors. Well it turns out that protectionism is everywhere even in what you would think would be the fully competitive environment of the Australian Open. When you attend the Open, any bag you take in is (quite rightly) inspected and they'll be looking carefully for particularly dangerous items. Like lenses with a focal lengths greater than 200mm. Why are these considered so dangerous, you might ask? Well the danger posed by such contraband is that it might allow its owner to take a really good picture. While the organisers of the Open want you to enjoy the tennis and buy lots of food and drink, the organisers of the Open do not want you to take good pictures. Anyway here's a picture of the official photographers whom the organisers of the Open are 'protecting' from me and my Pentax K200.

The photos are from the match between Maria Sharapova and Angelique Kerber and I'd hoped that it would turn into an epic struggle of Kurskian proportions. As it transpired, Kerber's Panzerfausts were no match for Sharapova's Katyusha batteries. I couldn't help wondering if Maria had modelled her famous 'vocals' on the unique music of Stalin's Organ.

The match was not without its lighter moments and Maria aborted one serve to entertain us with a dance move from Swan Lake that would not have disgraced the Bolshoi. However, it was soon clear that poor Angelique was not having a good day in the office and, after she bashed yet another ball into the net, I was not going to contradict her. On the bright side, at least she didn't try to blame the Romanians or start raving about Steiner's division.

As had always seemed probable, the match concluded in two sets and at the post-match interview, Maria demonstrated her familiarity with the principles and practise of High-Throughput Screening.

Rule of 3 takes some flak

You might remember my rantlet about the Rule of Three (Ro3) at the beginning of the year. Well it seems that others consider Ro3 to be over-restrictive and this JMC article will be as welcome in some parts of Cambridge as a staffel of Ju87 Stukas would have been back in 1940.

The authors of this work describe screening of a library of 364 fragments against the aspartyl protease endothiapepsin and crystal structures of 11 hits bound to the target protein. The library was designed “without strictly applying the rule of 3” and, as it turns out, “only 4 of the 11 fragments are consistent with the rule of 3”. Not exactly a ringing endorsement for Ro3 or a compelling incentive to buy a Ro3-compliant fragment library.

Hopefully one point that you’ll have taken away from my earlier post is that those who gave us Ro3 don’t say a whole lot about how they define hydrogen bond donors and acceptors so it can be difficult to say whether some fragments are Ro3-complaint or not. I’m guessing that Ro3’s proposers may not be be using the same definitions that are used to apply the Rule of 5 (Ro5). However, I really don’t know and don’t really care.

Now you can see the problem. Do any of the 7 fragment hits (I refuse to call them frits since that term is more usually associated with ceramics than Drug Discovery and an association with ceramics is something that you’ll want to avoid for fragments) from the endothiapepsin screen that are reported to be inconsistent with Ro3 actually fail to comply with the rule? Let’s take a look at the structures of two fragments for which binding to target was observed crystallographically. You’ll notice that I’ve retained the structure numbering from the article.

There are two nitrogens and three oxygens in 041 so with Ro5 hydrogen bonding definitions this fragment would not be Ro3-compliant. Personally, I wouldn’t count the amide nitrogen as an acceptor but that’s my choice. The cyclic ether oxygens in 041 will be very weak hydrogen bond acceptors and even three or four of these together will pack less of a punch than the typical amide carbonyl oxygen. One can make similar observations about 291 and I’d actually be more worried about the reactivity of the aniline portion of this molecule although this is not the place for that discussion. Fragment 255 has 3 nitrogens and one oxygen which translates into 4 hydrogen bond acceptors if you use Ro5 definitions. However, I would not count either the amide nitrogen or the bridging nitrogen of the fused heteroaromatic ring as acceptors so with my definitions the fragment would be Ro3-compliant.

The assay and crystallisation were carried out at a pH of 4.6. This means that the heteroaromatic nitrogens of 255 and 291 are likely to be protonated to a significant extent under experimental conditions. It’s interesting that the solubility measurements were run at a pH of 7.4 because basic fragments such as 041, 255 and 291 should be even more soluble in assay and crystallisation buffers. It would be a different story for acids but I didn’t see any of those so I guess no harm done.

It’s good to see the output of a fragment screen being published in this manner and the crystal structures for a number of fragments bound to this target represent a welcome addition to the protein knowledge base. Given that I’ve never been a fan of Ro3, I do like to see others questioning the rule although reading this paper gives the impression that Ro3 has never before been questioned. I also believe that they could have addressed the issue of hydrogen bonding definitions rather than simply jumping to the conclusion that Ro5 definitions (all nitrogens and oxygens are acceptors) were being used by those who gave us Ro3.

On a final note, you might wonder why I keep banging on about Ro3 when it’s something that I’ve never used to select fragments. It’s a good question and this is a good place to answer it. My own view is that the way many researchers have blindly adopted the rule is merely a symptom of a much bigger malaise in Drug Discovery research. Pharma appears to be dans la merde but the response of its leaders is typically to increase the frequency with which the Titanic’s complement of deck chairs is shuffled. Is this really a good time for those who represent the best chance of a future for the industry to be switching off their critical thinking skills?

Literature cited

Köster et al, A Small Nonrule of 3 Compatible Fragment Library Provides High Hit Rate of Endothiapepsin Crystal Structures with Various Fragment Chemotypes. J. Med. Chem. 2011, in press. DOI