FBDD and Networking

Reading an account of the session at the ACS on application of computational methods to FBDD, reminded me that it would be a good time to raise awareness of networking groups in this area. Both this blog and Practical Fragments allow readers to comment on posts although this tends not to happen with the frequency that it does at In the Pipeline, probably reflecting the huge readership, frequent updating and diverse content of what I consider to be the best drug discovery blog by a long way.

People interested in FBDD may already belong to a number of relevant LinkedIn groups. The groups offer some advantages over blogs for getting discussions going in that anyone can start a discussion and group members get alerted by email whenever somebody makes a new comment. I’ll list some of these below in case there are some that you’ve not yet heard about.

Fragment Based Drug Discovery (This group is linked by both FBDD blogs)

Label Free Assay Technology Group (It is the assay that makes FBDD possible. The weaker the binding that you can measure reliably, the more powerful your assay)

Structural Biology (X-ray Crystallography, NMR Spectroscopy, Electron Microscopy) (Generally you’re going to need crystal structures to take fragment hits forward)

Job opportunities in Computational Chemistry and Biology, Xray Crystallography, Fragment Based DD

Recently, I submitted the same item for discussion at a number of LinkedIn groups. I invited group members to share their views on the most appropriate technologies for detecting fragment binding. I learned about some new ways to configure SPR experiments and the use of Tm-shift assays. Most of the discussion was in the Structural Biology group (see discussion) although there was helpful input from the relatively new Label Free Assay Technology Group (see discussion) so thank you to all the participants. It was also great to see a couple of familiar faces from my days in Big Pharma, including a co-author from an article that a number of us wrote back in 2007

Interference, PAIN and cysteine pathologies

Dan provided some useful comments on the last post and I think it’s better to respond with a post since this makes everything more visible the readers of both our blogs. I agree with Dan’s point that there are pitfalls, such as compound aggregation, in addition to interference that Adam and colleagues describe in their article. In an ideal situation one would always have the ability to measure weak affinity directly. Protein-detect NMR is one of my personal favorites but you do need labelled protein and, if you want to get full value for your money (labelled protein is not cheap), you’ll also need resonance assignments. The SPR technology is widely applicable and like the protein-detect NMR will provide a direct measurement of affinity (and a whole bunch of other stuff). Isothermal titration calorimetry (ITC) represents another option although I believe that the technique is relatively sample-hungry and more limited than the other two techniques in the weakness of binding that can be measured. Also you do need heat so to speak even though the experiment is isothermal.

Nevertheless you can get to the point of having crystal structures with bound fragments using only a biochemical assay to measure potency. Given that you may well be screening at concentrations one or two orders of magnitude above what is ‘normal’ in HTS, it does make sense to use the approach that Adam and colleagues describe even if you’re going to follow up with SPR or NMR. I do sometimes wonder if the promiscuous behaviour of some inhibitors is due to this sort of interference rather than aggregation. One intriguing question is whether aggregates can ‘inhibit’ by changing spectroscopic and fluorimetric properties of assay mixtures rather than by interacting with proteins. At least there’s usually the option of running assays with added detergent to check for aggregation.

I won’t say much right now about the structural nasties that Jonathan Baell and Georgina Holloway have identified as PAINS since I’ll be visiting Jonathan at WEHI next Friday. I became acquainted with some of these unsavory structural types during my time in Big Pharma and do not believe that their PAINfulness is specific to the AlphaScreen technology that the WEHI researchers are using. Back in those days we had the Decrapper and a program called Flush...

Dan mentioned the Practical Fragments post on a Cruzain Screen so I thought I’d finish with a couple of papers that show how things can get unstuck when you’ve got a catalytic cysteine with a malicious streak. In the dock is none other than PTP1B, a target that is much-loved by disease area strategists and much-hated by screening groups. I’m not going to review the articles or even comment on them right now. Just read them in the correct order and perhaps we can pick up this theme later.

PTP1B: Read this first

PTP1B: Read this second

Literature cited

Baell & Holloway, New Substructure Filters for Removal of Pan Assay Interference Compounds (PAINS) from Screening Libraries and for Their Exclusion in Bioassays. J. Med. Chem. 2010, ASAP | DOI

Liljebris et al, Synthesis and biological activity of a novel class of pyridazine analogues as non-competitive reversible inhibitors of protein tyrosine phosphatase 1B (PTP1B). Bioorg. Med. Chem. 2002, 10, 3197-3122 | DOI

Tjernberg et al, Mechanism of action of pyridazine analogues on protein tyrosine phosphatase 1B (PTP1B). Bioorg. Med. Chem. Lett. 2004, 14, 891-897 | DOI

Interference correction in biochemical assays

Surface Plasmon Resonance (SPR) was in focus recently both here and across at Practical Fragments. However, now I’d like to take a look at using biochemical assays to identifying fragments that bind to targets of interest. Biochemical screens can typically be run in high throughput and are compatible with automation for high throughput screening, which makes it easy to do follow up screening with analogs. Furthermore the hits identified by biochemical assay are actually inhibiting rather than just binding. A criticism of biochemical screens is that they measure binding indirectly and are prone to interference. Sometimes they are used as a pre-screen to reduce the number of compounds that need to be evaluated in a lower throughput biophysical assay. However there are things that you can do to make your biochemical assay more reliable and meaningful. And maybe even more fun.

The article that I’ve chosen to take a look at in this post is by Adam Shapiro and some other colleagues from my days in Big Pharma. Before I met these folk, most of my fragment work had been around libraries for NMR screening and I learned from them how it is possible to correct for some of the interference from test samples in biochemical assays.

Inhibition is typically detected in a biochemical assay by quantifying changes in light absorption, fluorescence or luminescence. In high throughput applications ‘assay components are added serially to wells without any filtration or washing steps’ which means ‘that the test sample remains in the well during the optical measurement and can interfere with it’. This means that compounds that absorb in the UV or visible range and that fluoresce or quench fluorescence can all lead to changes in the readout parameter without actually binding to the target protein. Other less obvious causes of interference include insolubility of test compound (turbidity can lead to detection of highly polarised scattered light) and meniscus deepening which decreases path length. Compounds are typically assayed at relatively high concentrations in fragment screening, making it especially important to recognise and account for assay interference in these applications.

In addition to providing a useful discussion on the causes of interference, the article describes a practical approach to correcting for it by running ‘artefact assays’. These involve running additional plates in which wells contain the same test samples but no target protein. The wells in the artefact assay plate also need to contain whatever is responsible for generating the signal (e.g. reaction product) and a baseline can defined by preparing wells without test samples. The authors describe in some detail how they apply the corrections and since this is only a summary of the article, I’ll leave it to you to go and check their article out. However, I would like to conclude by noting that the authors also suggest criteria for deciding to reject data because interference is too great for meaningful correction.

Literature Cited

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

Surface Plasmon Resonance

General Reviews

Rich & Myszka, Grading the commercial optical biosensor literature – Class of 2008: ‘The Mighty Binders’ J. Mol. Recognit. 2010, 23, 1-64 Link | Review

Application to Fragment Screening

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

Perspicace et al, Fragment-Based Screening Using Surface Plasmon Resonance Technology, J. Biomol. Screen. 2009, 14, 337-349 DOI | Review

Binding Pathologies

Giannetti et al, Surface Plasmon Resonance Based Assay for the Detection and Characterization of Promiscuous Inhibitors, J. Med. Chem. 2008, 51, 574-580 DOI | Review

Ligand protein interactions by SPR

I have now been in Melbourne for about a month and have found the city very much to my taste. I’m visiting some friends to help out with some fragment stuff and have already been wreck diving (on the HMAS Canberra) and watched the Australian Open and a rather one-sided ODI between Australia and the West Indies. On the science side of things, I was able to gatecrash Surface Plasmon Resonance (SPR) course, hosted by the Biomolecular Interaction Facility at CSIRO, Parkville, and taught by Rebecca Rich and David Myszka of the University of Utah. Not the whole course, I might add, because the participants spent the second day of the course in the lab and I’m sure there was a clause in my visa agreement that stipulated that I was not to enter a laboratory except as an observer accompanied by a responsible adult.

SPR has always represented a bit of a gap in my knowledge base so this was always going to be a great opportunity. As well as being experts in this field, Rebecca and David present their material with great clarity, enthusiasm, charm and humour. I particularly liked David’s take on the Maxwellian Demon (these molecules don’t have eyes).

When using SPR to screen ligands, the protein is typically immobilised on the surface of the sensor chip. The term ‘immobilised’ is actually a bit of a misnomer and ‘tethered’ would actually be a more appropriate term. The SPR technology can be used to look at diverse types of interaction over a wide range of affinities and kinetic parameters (e.g. on and off rates) can also be measured.

There is of course a slight catch. The experiments need to be performed carefully and this was a recurring theme in the lectures (and presumably in the practical sessions as well). Now it turns out that much of the SPR literature is perhaps based on experiments that have been performed less than perfectly and, as a public service, Rebecca and David have reviewed and graded the SPR literature of 2008. GRADED? Yes, GRADED, and there were some Fs! Of course David is just the person to do the grading since he sports whiskers of which a Victorian (historical context rather than geographical) head master would be justifiably proud and it is easy to imagine him summoning the hapless transgressors to his study.

A grading exercise like this is unlikely to win you many friends and the authors are realistic to accept that it is likely to reduce the likelihood of either being elected to the National Academy of Sciences although hopefully they will never have to employ the services of professional food tasters when they attend SPR conferences. Putting on my computational chemistry hat, I couldn’t help thinking that the QSAR and Virtual Screening fields might benefit from a similar treatment...

There are a number of articles describing the use of SPR to screen fragments against target proteins and the one I’ve chosen to take a look at is from some folk at Roche. One of the authors of this work is David Banner, whose talk at RSC BMCS 2009, I greatly enjoyed, not least because he made no reference to ligand efficiency except, if I recall correctly, to say that he would not be referring to it.

The Roche group screened a library of 2226 compounds against chymase at 200 micromolar and found 80 hits so clearly SPR technology can provide the throughput required to run a fragment screen. The compounds were screened against an inactive (zymogen) form of the protein as a check for non-specific binding. The authors also described cross-competition experiments which could be used to determine whether two fragments were binding at the same or different sites and it is worth remembering that you need to be able to measure binding very directly to get this sort of information. It would have been really interesting if the results of the cross-competition assays had been integrated with crystallography since 12 co-crystallised complexes showed fragments binding in the active site.

Both stoichiometry and kinetics of binding can be determined by SPR making it an appropriate technique with which to observe interactions between badly behaved ligands and proteins. In an excellent (A-graded by Rebecca and David) article, another Roche group exploit SPR to classify some of these binding pathologies. It is particularly good reading for anyone who has worked up results from high throughput screens but that is not a place I particularly want to go to right now since it’s getting rather late at night and I really don’t want to have nightmares about pathological fragments.

Literature cited

Rich & Myszka, Grading the commercial optical biosensor literature – Class of 2008: ‘The Mighty Binders’ J. Mol. Recognit. 2010, 23, 1-64 Link

Perspicace et al, Fragment-Based Screening Using Surface Plasmon Resonance Technology, J. Biomol. Screen. 2009, 14, 337-349 DOI

Giannetti et al, Surface Plasmon Resonance Based Assay for the Detection and Characterization of Promiscuous Inhibitors, J. Med. Chem. 2008, 51, 574-580 DOI

Screening libraries: Sampling Chemical Space

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I am currently in Rapa Nui (aka Easter Island) and it seemed fitting to continue the series on compound library design from here since the first two posts have been from less commonly visited places like Asuncion and Tierra del Fuego. In the previous post I discussed 2D molecular similarity and showed how this can be used to define diversity and coverage, two important compound library characteristics. In general, compounds in a library need to be mutually diverse in order to provide good coverage although high diversity does not guarantee optimal coverage.

In this post, I’ll take you through an approach to library design called ‘Core and Layer’ (CaL). Although we used this to select compounds for generic fragment libraries and more specialised NMR screening libraries, the method is quite general and I have used it to design a compound library for black box cell screening and to select compounds to complement high throughput screens. The software tools (Flush and BigPicker) used to apply CaL were created at Zeneca by Dave Cosgrove and are described in our article in some detail. Although you might think that the tools were developed in order to apply the CaL method, things actually happened the other way round and it was the availability of the software that led to CaL being adopted as an approach to library design.

Figure 1 shows a schematic view of CaL. The core consists of the compounds currently in the library at any point of the design process and a layer is a set of compounds that have been selected to be diverse with respect to the core. Once a layer has been selected, it is added to the core and the combined set of compounds becomes the new core. The process of selecting layers goes on until you’re either happy with the library or you run out of patience.

You’re probably thinking that this is a very tedious and time-consuming way to build a compound library and might ask whether it would be better to select a maximally diverse set of compounds in a single step. However, there are advantages in building up a library in this manner. In library design, all compounds are not equal and CaL allows you to bias compound selection in a highly-controlled manner. I’ll discuss fragment selection criteria in some detail in future posts in this series so please just assume for now that there are some fragments that you would prefer to have in your library than others. The initial core consists of a sampling of your favourite fragments and as you add layers the compounds in them become progressively less attractive. Another feature of CaL is that it provides a solution to the problem of selected compounds proving to be unavailable as can be the case when trying to source relatively large samples from commercial suppliers.

I think this is a good place to stop as it’s dinner time in Rapa Nui. CaL is an approach to biased sampling of chemical space but it doesn’t tell us about which regions of chemical space should be sampled preferentially. In the next posts of this series I’ll take a look at what makes one fragment better than other. On the travel front, I fly into Auckand in a couple of week’s time for a month and a half in New Zealand and expect to be around Melbourne for the first four months of the New Year. Feel free to get in touch if you’ve got fragment stuff that you’d like to discuss.

Literature cited

Blomberg et al, Design of compound libraries for fragment screening. JCAMD, 2009, 23, 513-525 DOI

Screening Libraries: Diversity & Coverage

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I’m guessing that this may be the first blog post on screening library design to be written in Tierra del Fuego. The weather is currently rather unpleasant although less so than an hour ago when the snow was horizontal.

I introduced screening library design in the previous post with a generalised view of the work flow for fragment based lead generation. When selecting compounds for screening it can be helpful to think in terms of a chemical space in which all possible compounds (real or virtual) can be found. Now you’ve just got to sample the regions of chemical space that you like and you’ve got your library.

Life of course is not so easy. The main problem is that, despite the occasional claim to the contrary, nobody has found a convincing set of coordinates with which to describe chemical space usefully. You can sort of describe organic molecules by size and polarity without having worry about minor irritations like conformational flexibility, ionisation and tautomers. However, molecular recognition also depends on the shapes of molecules which, even for rigid species, are not so easy to turn into coordinates. Especially when you want these coordinates to be predictive of biological activity.

All is not lost since structurally similar molecules often have similar biological properties. One way that the similarity of a pair of compounds can be quantified is by comparing their molecular connection tables (the structures that you would write down on a piece of paper) and for this reason we sometimes talk about 2D similarity. There is no need for 3D molecular structures when you calculate molecular similarity in this way which means that there is no need to deal with conformations. Molecular fingerprints are used frequently to calculate similarity and the idea behind this is that the fingerprints encode the presence or absence of structural features in molecules. Many shared features suggest that two molecules are likely to be very similar. I’ll not go into the details of fingerprints in this post although you’ll be able to find some detailed discussion in our screening library design article.

The downside of 2D molecular similarity measures is that they are unlikely to reveal any but the most trivial shape match or pharmacophoric (e.g. oxadiazole replaces ester) similarity between molecules. This is not too much of a problem in library design because you’ll often to want select both molecules if they are based on different scaffolds, even if they can both orient their hydrogen bonding groups in a similar way. Once you’ve found some active compounds it becomes a very different game because now you’ll be looking for less obvious similarities between these actives, either to extract structure activity relationships or to define search queries.

So even though we don’t have a set of coordinates that defines chemical space in a way that is predictive of biological properties of molecules, we can still use molecular similarity to sample from a collection of compounds. Figure 1 illustrates how this sampling works and will give you an idea of what we mean by the terms diversity and coverage. The key thing to remember when looking at Figure 1 is that similar compounds are close to each other so there is an inverse relationship between distance and similarity. The stars are selected to cover the chemical space occupied by all the molecules and a star can’t cover its neighbourhood effectively the compounds in it are too far away

Although I needed put the molecules in particular positions (i.e. give them coordinates) to generate the graphic, you only need the distances between molecules to select representative subsets. In our paper we described in house software which can be used to do this and the two programs (Flush and BigPicker) are actually quite complementary to each other. Left to its own devices, BigPicker tends to select compounds with no near neighbours and we typically use Flush to ensure that the compounds that BigPicker is selecting from all have sufficient number of neighbours.

This is probably a good point at which to leave things. In the next post, I’ll describe the Core and Layer approach to selecting compounds for screening. This method is not specific to fragment libraries and in fact I’ve used it in work up of high throughput screening output and selection of compounds for cell-based assays.

Literature cited

Blomberg et al, Design of compound libraries for fragment screening. JCAMD, 2009, 23, 513-525 DOI

Grant & Pickup, A Gaussian Description of Molecular Shape. J. Phys. Chem. 1995, 99, 3503–3510 DOI