A slew of sites for fragments in HIV Reverse Transcriptase

The protein HIV-1 reverse transcriptase (RT) has been something of an Achilles heel for HIV; 13 approved drugs inhibit this enzyme! However, HIV is more adaptable than Achilles, and can develop resistance to drugs, creating a need for new molecules. With this in mind, Eddy Arnold and colleagues at Rutgers University performed an extensive fragment campaign against this target; their work was recently published in J. Med. Chem.

The researchers assembled a library of 775 fragments, 500 from Maybridge and most of the rest from Sigma-Aldrich and Acros. These were combined into 143 pools of 4 to 8 fragments, each at 100 mM in DMSO. Crystals of RT grown with the drug rilpivirine were soaked with each of the pools; rilpivirine stabilizes the protein and yields crystals that diffract to high resolution. The researchers also added 80 mM arginine and 6% trimethylamine N-oxide (TMAO) to the soaking solutions; arginine helped solublize some of the more hydrophopic fragments and improved electron density, while TMAO improved diffraction.

Overall, the researchers found 34 fragments that bound to HIV RT, a hit rate just over 4%. Interestingly, halogenated fragments seemed to give a much higher hit rate: 7 of 29 fluorine-containing fragments produced structures, as did 4 of the 17 brominated fragments and one of the two chlorinated fragments. I don’t recall seeing halogens previously over-represented among fragment hits, though last year we did write about halogen-enriched fragment libraries. The sample sizes reported here are small, but if the findings hold up in other studies, fluorine fetishism may be further justified.

But just as interesting as the composition of the fragment hits is the number of binding sites in the protein: 16, with names ranging from the descriptive (“NNRTI Adjacent” and “Incoming Nucleotide Binding”) to the concise (“399”) to the downright thuggish (“Knuckles”). In the case of three of these sites, some of the fragments also inhibited enzymatic activity.

There is a lot of nice information here, and eight co-crystal structures have been deposited in the protein data bank. Still, I am left a bit dizzy at the sheer number of sites. In fact, one fragment (4-bromopyrazole) bound to all of the 16 sites! What are we to make of this – is this a privileged fragment or a promiscuous binder? And as for the sites with no known functional activity, are these useful? What do you think?

Fragment Events in 2013 and 2014

If you missed Fragments 2013 and the Eighth Annual FBDD there are still a few more fragment events this year, and although we're not quite into summer it's not too early to start marking your calendar for 2014!

2013

June 19-21: CHI’s Thirteenth Annual Structure-Based Drug Design will be held in Boston, with several talks on FBLD.

September 3-5: LibPubMedia Conferences is organizing DrugDesign2013 in Oxford, UK, with a focus on fragment- and ligand-based drug design.

September 23: Teddy and I will be teaching a three-hour short course on FBLD in Boston as part of CHI’s 11th Annual Discovery on Target. 

2014

April 23-25: CHI’s Ninth Annual Fragment-Based Drug Discovery will be held in San Diego. You can read impressions of this year's meeting here and here, last year's meeting here, the 2011 meeting here, and 2010 here.

September 21-24: Finally, FBLD 2014 will be held in Basel, Switzerland. This marks the fifth in an illustrious series of conferences, the last of which was in San Francisco in 2012. I believe this will also be the first major dedicated fragment conference in continental Europe. You can read impressions of FBLD 2010 and FBLD 2009.

Know of anything else? Add it to the comments or let us know!

Fragment Mixes for NMR

The number of fragments in a mixture for NMR screening has been the subject of a poll.  Some people have stated that they go much higher than 10 fragments (of course for 19F it is totally different).  What many people who are interested in doing ligand-observed NMR screening, it is daunting to figure out how to compute the mixes.  This paper addresses the issue.  Unlike many current approaches which use the spectra and then deconvolute them, this approach encodes the spectra into "fingerprints" and uses a Monte Carlo algorithm to minimize signal overlap.  The paper itself delves deeply into a lot of computer-ese gobbledygook (e.g. "the knapsack problem, one of the typical, non-deterministic polynomial time (NP-complete) problems") that I don't find interesting at all.  What I do find interesting is that they are targeting mixtures of 5 fragments. Other than that, they go into serious detail about their algorithm and what version was best.  They worked with 342 fragments from their in-house library.

However, after doing the initial POC on these they did not have a library big enough to test for scalability so they generated some virtual libraries: 500, 1000, 3000, and 5000 fragments.  Similar to discussion going on elsewhere, they clustered their fragments as strongly aromatic, strongly aliphatic, or balanced shown here.  As would be expected, library size and peak distribution did not affect the algorithm, but number of fragments per mixture did. As shown here, for the optimized libraries there is less overlap as you increase the number of fragments per mix (for 5 fragments it was ~0% to about 10-20% for 8-10 fragments).  This is a potentially huge increase in efficiency, simply increasing the number of compounds per mix from 5 (our poll found 5-7 to be the median number in mixes) to 10 would half the number of spectra that need to be acquired; hence lowering the potential cost to companies (especially if they are outsourcing (shameless self-promotion)). 

I have spoken to the authors and while, unlike the Beatson, their tool will not be available online, it is being incorporated into an upcoming release of Mnova's software. [Full disclosure: I have a business relationship with Mestrelab.]  Of the other software available, I believe only AMIX (Bruker) has built in screening tools, but I am not sure entirely as I have never used AMIX.  NMRpipe would be the one to be most likely to also have such tools, but their availability would be based upon the kindness of strangers.  Frankie D (Mr. NMRPipe) is at Agilent (nee Varian) now, so maybe vNMRJ will become more utile.  That last major software package from ACDLabs is not geared to this kind of work AFAIK. Additionally, this approach of course is just as "easily" applied to 19F, which could mean a mean increase of compounds from 10-15 to 25-30 routinely. 

I of course will update this if information on other software becomes available in the comments or via email.

[UPDATE #1: Ben Davis (Vernalis) pointed out CCPN has tools for this.  
Anna Vulpetti (Novartis) points out that python scripts for 19F have been published.
Arvin Moser (ACD) points out that ACD does offer screening tools.]

30% of all Posts...

NOTE: Blogger blew up my post when I published it.  I have fixed what I can.  Blogger keeps on blowing up this post after I edit it.   I have removed what I think may have been causing some of the problems.  This post should be considered in "Wiki-ese" as a fragment.   Thankfully, the summary was unaffected. 

As I recently said, GPCRs are a theme around here, so this post will talk about work published last year by the folks at ZoBio and Heptares.  [In terms of full disclosure, I had a business relationship with ZoBio until recently.]  This work is also on STaRs, the stabilized GPCRs developed by Heptares.  I noted my concerns with this approach here.  These two papers focus on A2A GPCRs, while previous posts here were on A1A, A3A, and B1A GPCRs.

In the ACS Chemical Biology paper, the authors are using TINS to screen an antagonist-stabilized A2AR StAR.   The immobilized protein showed a ~50% greater retention in activity after 5 days at 4C compared to the native protein in membranes (60% vs. 30% binding competency).  So, immobilized stabilized protein is more stable than non-immobilized, non-stabilized protein.  They then took a subset (531 compounds) of the ZoBio fragment library picked for maximal chemical and shape diversity and screened using OmpA as the reference protein. As shown in the bucketing below the vast majority of the compounds cluster around a T/R of 1.  This indicates that they have a slight preference for the target or the reference. The used a T/R cutoff of less than 0 e.g

. aromatic and aliphatics.  Additionally, the use of the logarithmic plot for the bucketing obscures the spread around T/R=1.  I have never seen a discussion from the creators of TINS discussing the relative error of the method and how to best evaluate the screening data.  In this case, they chose a 0.7 cutoff because there appears to be a discontinuity in the data there.
They followed up on these (see Table 1 in the paper) as orthosteric hits by observing if they can inhibit binding of an inverse orthosteric agonist in a radiolabeled assay using WT A2AR in HEK membranes; five fragments inhibited binding by >30% at 500uM (see below). 

This data in conjunction with the TINS data shows the compounds bind reversibly with a 1:1 stoichiometry.  These fragments also inhibited A1AR, which would not be unexpected for such small molecules.  However, 3 of these compounds have poor LEAN values >0.3.  This is particularly poor for GPCR targeting compounds.

Four additional fragments either one or the other of the inverse agonist or agonist used.  The two most potent AM appear to have some subtype specificity (A2AR over A1AR).  When they tried to test these compounds in a cell-based assay, toxicity was observed so no data could be collected.

In summary, the authors show that TINS is productive in finding fragments that bind to GPCRs.  However, they have to rely on standard biochemical assays for follow up.  It would have been nice to see at least one other method used to verify the active fragments, like SPR.  What I really like is that they did the biochemical assays against WT, which does not necessarily alleviate my concerns about screening against a mutant.  I would have really liked to see a comparison of the biochemical data in the STaR and WT.

So, while people say 30% of marketed drugs target GPCRs, I can assure you 30% of all of our posts are not about GPCRs.

Reversibly covalent fragments vs kinases

A big problem with small fragments is that they usually have low affinities for their targets; there is only so much binding energy you can pack into a dozen or so heavy atoms. Indeed, it wasn’t until the rise of sensitive biophysical methods such as NMR that fragment-based lead discovery really took off. But what if you could increase the affinity of fragments themselves?

One way to increase affinity is by introducing a covalent bond between the fragment and the protein: an irreversible covalent bond will, by definition, keep a fragment from ever dissociating from the protein. However, with this type of interaction, it may be difficult to distinguish between fragments with different inherent binding energies: iodoacetamide will alkylate every exposed cysteine residue, even though acetamide itself may have no inherent binding affinity. What you really need is a reversible covalent bond: something just strong enough to improve the affinity for the target, but still allow you to define structure-activity relationships among different fragments. This is the principle behind Tethering, which relies on (reversible) disulfide bonds between fragments and the amino acid cysteine. In a recent communication in J. Am. Chem. Soc., Jack Taunton and coworkers at UCSF apply a different chemistry to discover potent and selective kinase inhibitors.

Among the 518 human protein kinases, there are many non-conserved cysteine residues. Indeed, several advanced clinical candidates target a cysteine found just outside the ATP-binding site of certain kinases. These candidates are potent molecules in their own right, with irreversible covalent “warheads” attached to permanently knock out the kinases.

The UCSF researchers instead started with simple fragments (molecular weights between 96 and 250 Da) found in non-covalent kinase inhibitors. Each fragment was derivatized with a cyanoacrylamide moiety that could form a reversible covalent bond with cysteine residues, and these were screened against three of the eleven kinases that contain a cysteine residue at a certain spot within the active site. Remarkably, all showed activity against at least one of the kinases, though there were very different selectivities. Mutation of the targeted cysteine residue dramatically reduced affinity in all but one case, as did removal of the cyanoacrylamide.

Crystal structures of two fragments bound to the C-terminal domain of the kinase RSK2 were determined. In the case of compound 1, the indazole made the expected interactions to the so-called hinge region of the kinase. Interestingly, though, in the case of fragment 8, the azaindole moiety does not bind to the hinge. Instead, the ketone moiety serves as a hydrogen bond acceptor. Overlaying the two fragments suggested that adding an aromatic substituent to the indazole could improve affinity, a hypothesis that was nicely validated by compound 11. Addition of another small moiety gave compound 12, with improved selectivity over the kinases NEK2 and PLK1.

Compound 12 was tested against a panel of 26 kinases, 12 of which have active-site cysteine residues, and was found to be selective for RSK2 against all but NEK2 and PLK1 (and even then, the compound was more than 40-fold selective for RSK2). Crystallography confirmed the binding mode, complete with covalent bond to the cysteine residue, and mass-spectrometry of the denatured protein confirmed that the covalent bond is reversible.

One of the attractive features of the cyanoacrylamides is that they are quite stable, and in fact compound 12 showed respectable cell-based activity against RSK2 as well as the closely related C-terminal domain of the kinase MSK1, for which no inhibitors had previously been reported.

All in all this is a nice approach that should be broadly applicable not just to kinases but to a wide variety of targets. At least some of this technology has been licensed to Principia Biopharma, so it will be fun to watch this story progress.

Fragments in Living Cells

GPCRs seem to be popular around here lately.  In this paper, a group of researchers in the UK developed a report-fluorescent assay to be used on living cells to screen fragments.  Recent advances have been made in structural studies of fragments (see Receptos and Heptares) and with this new assay, the entire suite of experiments for prosecuting FBHG exist for GPCRs.  As the authors point out, and I mentioned here, mucking around with GPCRs with things like detergent stabilization removes ancillary proteins which can provide allosteric interactions. As the authors state:

It is now acknowledged that GPCRs can adopt multiple active conformations as a consequence of protein-protein interactions that can lead to the activation or attenuation of different signaling pathways.  Furthermore, different agonists appear able to bias signaling in favor of a particular downstream pathway, including those that do not involve heterotrimeric G proteins . It is also clear that the binding affinity of antagonists can vary depending on the signaling pathway and agonist that is being studied. These data suggest that intracellular signaling proteins can elicit marked allosteric influences on the binding of both agonists and antagonists to a particular GPCR and as a consequence the cellular context in which binding affinities are measured will have a major impact on drug screening strategies. [Emphasis mine].

To build their assay, they used an existing fluorescent existing amine congener that was commercially available and ensured that it had competitive, antagonistic properties against A3AR (Adenosine-A3 receptor).  It exhibited many desirable characteristics for a high-content screening: including high affinity and slow off-rate.  It was able to quantify agonist displacement from A3AR.  

To test for the ability to detect weak binders, they deconstructed the high affinity A3AR antagonist (1) into its component fragments (2-7) [the affinity values are pKi].  They then acquired a 248 subset of the Maybridge fragment library that was Voldemort Rule compliant.

  They found 38 confirmed actives, with the compounds shown below as the top 6:

To me, the interesting part of this paper is NOT the subsequent SAR and the novel compounds that resulted, but instead the fact that there is now an assay for A3-and A1AR that can reliably detect fragments and support SAR studies.  Of course, one major caveat here is something that I learned from a venerable GPCR chemist at Lilly: the natural ligands of GPCRs are fragments, so of course fragment screening works.  I think most people who work in fragments would be very happy with 1 microM actives from a screen; I think most GPCR chemists would not.  I would also imagine that ligand efficiency is even more important when working in this class of compounds. 

Fragment-based chemogenomics

An article with an intriguing title appeared recently in Drug Discovery Today: Small and colorful stones make beautiful mosaics: fragment-based chemogenomics. Iwan de Esch and colleagues at VU University Amsterdam and IOTA Pharmaceuticals define chemogenomics as:

The discovery of new connections between chemical and biological space leading to the discovery of new targets and biologically active molecules.

Thus, “fragment-based chemogenomics” is:

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

Like the “small, colorful stones” (or tesserae, though presumably not quantum) in a mosaic, fragments can be used to build up an understanding of protein-ligand interactions.

The authors start by constructing a fragment library consisting of 1010 compounds, most of which follow the rule of 3 (or, as Teddy would have it, the Voldemort Rule). An upper limit of 22 non-hydrogen atoms was used, and it looks like the lower limit was 7 atoms (vote on your own lower limit in the box at the right!), with a mean molecular weight of 211 Da. Most of the fragments were originally made as synthetic intermediates, but 117 were purchased specifically to add diversity to the library.

Having assembled the library, the authors then screened it against six targets: 4 GPCRs, 1 ion channel, and 1 kinase. Most of the assays involved displacement of a radioligand; hits were found against all of the targets, with hit rates ranging between 1 and 10%. Perhaps not surprisingly, different proteins preferred fragments with different physicochemical properties: histamine receptors selected polar, positively charged fragments (like histamine itself), while the kinase preferred rigid, hydrophobic, neutral fragments.

Although none of the fragments hit all six targets, a good proportion bound several (up to four). These tended to be larger than average, though no more hydrophobic, in contrast to results from other studies (see here and here).

Some of the most interesting results describe activity “cliffs,” ways to classify SAR observations for the GPCRs. An affinity cliff consists of two closely related fragments, one of which is active, the other of which is not, while a selectivity cliff consists of two fragments, one of which is active for a set of proteins, while the other is selective for one or more. The researchers show several examples where small changes – the introduction of a single heavy atom, the contraction of a ring, or the reversal of an amide bond – ablates activity.

Although crystallographically-enabled fragment optimization is now possible for GPCRs, the activity-guided SAR described here should be accessible to more researchers working on a wider range of proteins, and should prove powerful for tackling targets where structures are still elusive.

Nature recently declared that “‘omics bashing is in fashion,” but I do think there is something here. Whether or not it deserves its own ‘ome is open to debate, so feel free to weigh in!

Fragments vs GPCRs

G protein-coupled receptors, or GPCRs, have been one of the most fruitful areas of drug discovery. Humans have 390 of them throughout the body (plus many more in the nose, where they are essential for smelling), and almost a quarter of new drugs approved in the past decade target GPCRs. Despite these successes, there are plenty of “difficult” GPCRs that have resisted drug-discovery efforts. Since GPCRs are membrane-bound proteins, crystallography has until very recently been all but impossible, making structure-based design and fragment approaches correspondingly more difficult. In a recent issue of J. Med. Chem., John Christopher and colleagues at Heptares Therapeutics describe their success against one member of this target class (see also here for In The Pipeline’s coverage).

Fragments have been screened against other types of membrane proteins using surface plasmon resonance (SPR) and TINS, but one of the particular challenges of GPCRs is that they are generally quite unstable and conformationally flexible. Heptares solves this problem by making a small number of targeted mutations to increase receptor stability and lock the conformation. In this case, the researchers targeted the human β₁-adrenergic receptor (β₁AR); both agonists and antagonists of β-adrenergic receptors are approved drugs.

Approximately 650 fragments were screened using SPR against the stabilized human β₁AR as well as another GPCR, the adenosine A_2A receptor. Selective hits were identified against both targets; among the β₁AR-selective hits were compounds 7 and 8, both with impressive affinities and ligand efficiencies.

Co-crystal structures of various ligands bound to turkey β₁AR (which is identical to its human counterpart in the ligand binding domain) had previously been solved, and molecular modeling of the fragment hits led to the purchase of a set of analogs, which were then tested in a radioligand membrane binding assay. Happily, compounds 19 and 20 both bound with improved affinity over the parent fragments. Crystal structures of these new molecules in complex with turkey β₁AR were also determined, revealing that they do not completely fill the ligand-binding pocket, and suggesting additional modifications to further improve potency and alter their pharmacology.

There are still many unanswered questions. Phenylpiperazines such as these are unusual ligands for β-adrenergic receptors, but they are known to bind other GPCRs, so selectivity will need to be investigated thoroughly. Also, the researchers don’t say whether their molecules are agonists or antagonists, though they suggest the later. Some of this work was publicly presented as early as 2010, so presumably there is plenty more data beyond what’s reported here.

All that said, this is a nice milestone in fragment-based ligand discovery, and it will be fun to see how crystal structures play a role in understanding (and drugging!) this important class of targets.