You Probably Already Knew This...

Academics can spend time and resources doing, and publishing, things that people in the industry already "know".  This keeps the grants, the students, the invitations to speak rolling in.  It also allows you to cite their work when proposing something.  This is key for the FBHG community.  There are many luminaries in the FBHG field, and we highlight their work here all the time. Sometimes, they work together as a supergroup.  Sometimes, Cream is the result.

Brian Shoichet and Gregg Siegal/ZoBio have combined to work together.  In this work, they propose to combine empirical screening (TINS and SPR) with in silico screening against AmpC (a well studied target).  They ran a portion of the ZoBio 1281 fragment library against AmpC.  They got a 3.2% active rate, 41 fragments bound.  6 of these were competitive in the active site against a known inhibitor.  35 of 41 NMR actives were studied by NMR; 19 could have Kds determined (0.4 to 5.8 mM).  13 fragments had weak, but uncharacterizable binding; 3 were true non-binders. That's a 90% confirmation rate.  34 of 35 were then tested in a biochemical assay.  9 fragments had Ki below 10 mM.  Of the 25 with Ki > 10mM, one was found to bind to target by X-ray, but 25A from the active site.  They then did an in silico screen with 300,000 fragments and tested 18 of the top ranked ones in a biochemical assay.  

So, what did they find? 

"The correspondence of the ZoBio inhibitor structures with the predicted docking poses was spotty. "  and "There was better correspondence between the crystal structures of the docking-derived fragments and their predicted poses."

So, this isn't shocking, but it is good to know.  This is also consistent with this comment.  So, the take home from this paper is that in silico screening can help explore chemical space that the experimentally much smaller libraries miss.  To that end, the authors then do a a virtual experiment to determine how big a fragment library you would need to cover the "biorelevant" fragment space [I'll save my ranting on this for some other forum].  Their answer is here [Link currently not working, so the answer is 32,000.]

Another NMR Tool...

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

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

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

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

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

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

Fragments vs membrane proteins with TINS

Fragment-based ligand discovery owes much of its success to the rise of biophysical techniques such as NMR, crystallography, and – more recently – surface plasmon resonance. These have allowed the discovery of fragments against a wide range of proteins, but one notable exception has been membrane proteins, the targets of more than half of marketed drugs. In a recent issue of Chemistry and Biology, Gregg Siegal and colleagues take a crack at this diverse group of proteins.

The researchers, from Leiden University, ZoBio, and elsewhere, use an NMR-based technique called target immobilized NMR screening, or TINS. In this method, a protein is immobilized onto a solid support. A reference protein is also immobilized; this reference is usually a well-characterized protein that does not bind to many small molecules. Each protein is then put into its own compartment of a two-compartment flow-cell, and this is inserted into an NMR spectrometer. Mixtures of fragments are then flowed through both chambers: those that interact with protein show a reduction in the amplitudes of their NMR spectra. By choosing fragments that show such a reduction for the target protein and not the reference protein, fragments that bind to the target can be differentiated from those that bind to proteins in general. After each NMR experiment, the fragments are washed away and replaced with a new set of fragments. TINS has been applied to a number of soluble proteins, as reviewed here. Remarkably, the immobilized protein samples often remain stable through hundreds of screening cycles.

Membrane proteins are notoriously difficult to crystallize or characterize by NMR. Moreover, it is often difficult to obtain enough protein to work with. However, since TINS relies on a decrease in signal from the fragment rather than a signal from the protein itself, Siegal and colleagues tested whether they could use the technology to discover fragments that bind to membrane proteins.

The researchers chose a protein called disulphide bond forming protein B (DsbB), which is found on the inner membrane of E. coli and other Gram-negative bacteria and may be important in virulence factor folding. One of the challenges of membrane proteins is keeping them properly folded, and the researchers used two different approaches to do this, either detergent micelles or “nanodiscs,” lipid bilyaers surrounded by a scaffold protein. Using less than 2 milligrams of DsbB, the researchers used TINS to screen a set of 1071 fragments in groups of about 5 each, with each fragment present at 500 micromolar concentration, a process that took 5 and a half days.

The TINS process led to 93 hits, a respectable hit rate of 8.7%. Each of these was then tested in a functional assay at 250 micromolar concentration, and more than half of the hits inhibited DsbB activity by at least 30%. Eight of these were subsequently characterized using full IC50 curves and kinetic analysis. The potencies were impressive, ranging from 7 micromolar to 193 micromolar, with ligand efficiencies as high as 0.45 kcal/mol/atom. DsbB has the advantage that it has been characterized structurally, and the researchers used chemical shift information from 2-dimensional NMR experiments to show that the fragments could be divided into two groups, with one set competing with a quinone cofactor and the other binding at a different site.

This paper demonstrates that it is possible to find fragments that bind to membrane proteins. Of course, the next question is, what can you do with the fragments? In this case there were structural data about the target, but this will not generally be true for membrane proteins, and in the absence of structure, advancing fragments to leads can be challenging. On the other hand, medicinal chemists have been developing drugs against membrane targets for decades without knowing their precise structures, so perhaps the challenge is as much psychological as scientific.