There and Back Again (and Back Again)

BACE is a very popular target (there are potentially 20 Billion reasons for it). As we noted in April, Pfizer has entered the contest (publicly now). Pfizer utilized a subset (340 fragments) of their recently described Fragment library (GFI) used X-ray as the discovery platform soaked in 4 at a time. At an intial concentration of 20 mM, 58 of the 85 mixes yielded useable diffraction data. They then repeated the screen at 2mM and 200uM to attempt to gather data on compounds which disrupted the crystal lattice at higher concentrations. All of this led to the discovery of one fragment (the spiropyrrolidine).
They then threw the biophysical and biochemical book at this compound to establish it as a bona fide inhibitor: Octet (1.4mM Kd, 0.31 kcal/mol/atom), STD-NMR, 1H-15N HSQC NMR, functional NMR assay to determine weak Kds, and a BACE inhibition assay (1.1mM). It was found that this fragment had excellent permeability and low potential PGP efflux.
They then did their SAR to optimize the core and develop "growth" vectors. They ended up improving potency by three orders of magnitude with seriously affecting the ligand efficiency nor the in vitro properties predictive of good brain penetration.
To me, this is the most interesting point in the paper. Does ontogeny recapitulate phylogeny for drugs? If you start with good properties, do you keep them? I know there is a good amount of debate on whether or not a fragment will keep its binding mode as it is optimized/expanded. What is the general thinking on properties? Do people screen (at least in silico) their libraries for things like permeability?

Finding cryptic pockets computationally

The mobility of proteins is a constant source of wonder. I enjoy looking at experimentally-determined structures of small molecules bound to proteins, and it’s even more fun when the protein undergoes dramatic conformational changes to accommodate the ligand. But what may be fun for a chemist is a considerable challenge for molecular modeling: it’s hard enough to dock small molecules to a rigid model of a protein, and all the more so when an apparently flat protein surface yawns open to reveal a new pocket. In a recent paper in J. Comp. Chem., Olgun Guvench and collaborators at the University of New England College of Pharmacy and the University of Maryland look specifically for such “cryptic” binding sites.

The researchers used the cytokine IL-2, which is known to have cryptic pockets. In fact, small molecule inhibitors have been found that target the IL-2 receptor binding site in part by binding to cryptic pockets in the cytokine. The apo form of IL-2 (ie, without any small molecule bound) was used as a starting structure in a computational technique called Site Identification by Ligand Competitive Saturation (SILCS). In this approach, multiple molecular dynamic simulations are carried out with the protein “soaked” in a virtual solution of water and ligand (in this case very simple molecules such as benzene, propane, or acetonitrile). The idea is to let pockets form and see if the ligands bind in the pockets.

Molecular dynamics simulations, in which individual atoms within a protein are allowed to move, run the risk that the protein will deviate too far from a stable structure and denature completely. This can be avoided by introducing various restraints to keep atoms from moving too much, but if the restraints are too severe the protein is too rigid and you won’t see pockets form.

Also, as many people are painfully aware, small molecules can form aggregates in aqueous solution, and the same thing can happen in virtual water. In SILCS, the virtual fragments are programmed to repulse each other, keeping the fragments more or less distributed in solution.

The researchers found that they could in fact identify the cryptic pockets in IL-2, either by using relatively loose restraints or by running multiple unrestrained simulations and simply discarding those in which the protein denatured dramatically. Only the hydrophobic fragments found the cryptic binding sites, perhaps reflecting the relatively hydrophobic nature of the pockets. Additional pockets were also found, though whether these are real or not is unclear.

It’s still a long way from simulations with propane to running molecular dynamics screening simulations on hundreds or thousands of unique fragments, but given the increasing speed of processing power, perhaps the gap will be bridged sooner than expected.

Another (impractical) NMR Screening Method

NMR has a checkered history in drug discovery. In the 90s, it promised to deliver structures just like X-ray. Strike 1! After that, especially after the advent of SAR by NMR, it promised to deliver boatloads of hits from screening. Strike 2! After that, pharmaceutical NMR worked hard to make sure that it was impactful and value-added. It found niches in which it thrives, e.g. a variety of -omics. In drug discovery, NMR still needs to realize it is living with two strikes. How can NMR survive and even thrive? Quite simply. NMR needs to provide rapid, robust, and easily understandable data to medchemists that leads to decisions. Data that results in no action has no value.

In this paper, Salvia et al. present a ligand-based NMR screening method using "long-lived states (LLS)" of the ligand to boost the sensitivity of ligand-based screening. This new method provides 25x better signal-to-noise than established (T1rho) methods and uses less protein. One of the benefits of this method is it allows NMR to study interactions as tight as 100nM and up to 1 mM.

The graphical abstract (above) shows that while this method is very similar in concept to other ligand-based methods (TOP: equilibrium between NMR differentiated states) it requires much more work than these other methods (Bottom: titrations of ligands). The data (Below) does generate quite satisfying curves, and as noted by the authors, are in agreement with previously published values.
I think this work, while an interesting application of Long Lived States, has really no practical value to the screening world. The strength of the binding can be too strong, making the bound lifetime too long, and thus there is a practical floor for Kd. Of course, because it is based upon kinetics, it can be very different for every system.

If you want to determine Kds for a complex < 10uM there are better, far more robust methods (SPR, for one). The amount of time and effort required to generate Kds from this method seems to run contrary to the tenets I described above (rapid, robust, and (most importantly) easily understandable). To me, the title of the paper simply does not deliver. This method is NOT a screening application. A screening application is one experiment (NMR or otherwise) from which you can determine whether a compound is binding or not, ideally from a mixture of compounds.

I would be curious to see in the comments if anyone (especially our NMR savvy readers, you know who you are) think that this method has practical applications.

Fragments vs membrane proteins with SPR

Membrane proteins such as GPCRs account for something like half of all drug targets, but they present a serious challenge for fragment-based approaches. This is partly because the biophysical methods usually used for fragment screening often don’t work for membrane proteins, and partly because, in the absence of structural information, it’s hard to know what to do with fragment hits. But there is progress. We’ve highlighted a couple papers that use functional screens or TINS to find fragments against membrane proteins, and in a recent issue of Biochem. Pharmacol. U. Helena Danielson and colleagues at Beactica and two academic institutes show how surface-plasmon resonance (SPR) can also be applied.

The researchers were interested in GABA_A receptors, a class of ion channels involved in multiple physiological functions. The receptors normally form hetero-pentamers, but for simplicity the researchers used a homo-oligomeric receptor, consisting of five β3 subunits. Each β3 subunit carried a tag containing eight histidine residues that could be recognized by antibodies immobilized to the surface of the SPR chip. The GABA_A receptors were detergent-solubilized; control channels contained antibodies and detergent with no receptors. The resulting GABA_A-modified chips were quite stable; the researchers report being able to run roughly 200 samples over the course of 20 hours with a single chip. (The specific detergents and conditions are critical, so if you’re interested in pursuing this yourself the experimental section is invaluable.)

A set of 51 histaminergic and 15 GABAergic ligands were tested for binding, resulting in nearly two dozen hits with dissociation constants (K_Ds) between 13 and 300 micromolar. Some of these are exceptionally small: for example, histamine, with a molecular weight of 111 g/mol and just 8 heavy atoms showed a K_D of 98 micromolar, which is consistent with published results using different methods. A number of other ligands were also identified, some for the first time, though other previously reported ligands did not repeat in this system.

It will be fun to see the screening results of a larger, unbiased library. Of course, finding fragment hits against a membrane protein is only the first step to developing drugs – one still needs to figure out how to improve potency, most likely in the absence of structure. But, to paraphrase Churchill, at least this paper and related research represent the end of the beginning.