Ligand-observed NMR – quantitatively

Ligand-observed NMR is one of the most popular fragment-finding methods. Among its strengths is the ability to find extraordinarily weak fragments that most other techniques would miss. However, common ligand-observed NMR methods such as STD are not quantitative: they can tell you that a fragment binds, but not how tightly. In a new open-access J. Med. Chem. paper Manjuan Liu and colleagues at the Institute of Cancer Research provide an easy solution.

 

The approach is based on an NMR phenomenon called transverse relaxation (see here), which describes how atomic nuclei return to their ground state after being excited by a radiofrequency pulse in a magnetic field. The transverse relaxation rate R₂ for a given nucleus depends on the tumbling speed of the molecule in which it is contained: small molecules tumble rapidly and have small R₂ values, while larger molecules tumble slowly and have larger R₂ values. When a small molecule binds to a protein its tumbling speed slows and its R₂ increases. The R₂ values can be measured experimentally using a Carr-Purcell-Meiboom-Gill (CPMG) pulse sequence.

 

This is all fairly standard for NMR spectroscopists, and in fact CPMG is widely used to find fragments. Liu and colleagues proposed that, by measuring the change in R₂ with changing concentrations of small molecule, they would be able to extract the dissociation constant (K_d). The theory gets a little hairy (14 equations), and the analysis depends on non-linear regression curve fitting, but this is easily done using modern analytical software. The technique is called R2KD.

 

The experiment itself is straightforward. The ligand alone is prepared at two different concentrations; these are used to determine the R₂ values of the free ligand. Another eight samples contain protein and various concentrations of ligand. The R₂ values are measured and fit to an equation to extract the dissociation constant. An initial test case with a known 50 µM ligand for the protein BCL6 was encouraging, giving a K_d of 53 to 78 µM for four different protons on the ligand. The accuracy could be further improved by using a “global fit” with all the data rather than analyzing each NMR peak in isolation.

 

Next, seven ligands against three proteins were analyzed using R2KD and compared with their literature values. Here too the results were in agreement, mostly within a factor of two. The lower limit for sensitivity is dependent on the NMR signal for the ligand; below concentrations of about 20 µM the experiments become impractically long. The upper limit is dictated by the solubility of the ligand. The researchers could reliably measure dissociation constants around 1 mM and suggested that with a sufficiently soluble ligand even weaker ligands could be measured.

 

The R2KD experiment requires that the protein concentration be less than about 20% of the lowest ligand concentration. (That said, protein concentrations up to 35 µM gave reasonable results.) Preserving protein is usually a goal, so lower concentrations (single-digit micromolar) are desirable from both a practical and theoretical standpoint.

 

Finally, the researchers demonstrated the application of R2KD to assess 10 fragment hits from a 1000-compound screen against the E3 ligase complex CRBN/DDB1, one of the most popular targets for PROTACs. The hits had dissociation constants ranging from 70 to 1200 µM, and the R2KD values were similar to those found in a fluorescence polarization (FP) assay, though for the most part the affinities from R2KD were higher. In particular, two compounds with essentially no activity in the biochemical assay came in at sub-millimolar by R2KD, which may speak to the insensitivity of the FP assay.

 

Overall this is a lovely and, as befits this blog, practical paper, and I hope R2KD becomes widely adopted. With a sweet spot for K_d values of 10-1000 µM the technique fills an important niche: biochemical assays are well-suited for tighter binders but less reliable at millimolar ligand concentrations. As crystallography becomes increasingly popular as a primary screen, I could imagine R2KD being used to rank the resulting fragment hits.

NMR poll results

The results of our latest poll are in – thanks to all who participated! Of the 119 people who responded to the first question, 87% said they use NMR for finding or validating fragments. Even if we assume that responses were biased towards NMR aficionados, big magnets are clearly popular.

The second question asked about specific NMR techniques. If everyone who said they used NMR in the first question also answered the second, this means the average user applies more than 3 different techniques; I’ll let Teddy weigh in to see whether this matches his experience.

One surprise for me was that, although many techniques are widely used, none are nearly universal; even the most popular methods seem to be used by just over half of respondents.

Among ligand-detected methods (blue in the figure), STD ranks at the top, with line-broadening, WaterLOGSY, and fluorine-based techniques all tied for second place.

Protein-detected methods (red in the figure) also appear quite healthy, with nearly as many respondents using ¹⁵N-HSQC/HMQC as STD.

Finally, 11 of you said you use "other" techniques. We didn't include TINS, even though it seems quite useful, because it is only available through the services of ZoBio. But what else is out there?

NMR poll!

Among fragment-finding techniques, nuclear magnetic resonance (NMR) ranks near the top. Protein-detected methods, like HSQC/HMQC-based SAR by NMR, helped usher in fragment-based drug discovery as a practical endeavor. More recently, ligand-detected methods such as line broadening (or CPMG), STD, and WaterLOGSY appear to have gained the edge. There are also more boutique methods, such as ILOE and spin labeling. And of course, some people proudly embrace their fluorine fetishism.

So what’s your favorite flavor? Now's your chance to weigh in on our latest poll (on the right). The first question asks whether you use NMR, and the second asks which methods you use. PLEASE ANSWER BOTH QUESTIONS - the free version of Polldaddy doesn't track individuals, so we need the answer to the first question to know the total number of respondents.

And, as always, your comments are welcome.

Biofragments: extracting signal from noise, and the limits of three-dimensionality

What does this protein do? Now that any genome can be sequenced, this question gets raised quite often. In many cases it is possible to give a rough answer based on protein sequence: this protein is a serine protease, that one is a protein tyrosine kinase, but figuring out the specific substrates can be more of a challenge. In a recent paper in ChemBioChem, Chris Abell and collaborators at the University of Cambridge and the University of Manchester attempt to answer this question with fragments.

The bacterium Mycobacterium tuberculosis (Mtb), which causes tuberculosis, has 20 cytochrome P450 proteins (CYPs), heme-containing enzymes that usually oxidize small molecules. Although some are essential for the pathogen, it is not clear what many of them do. The researchers used an approach called “biofragments” to try to pin down the substrate of CYP126.

The biofragments approach starts by selecting a collection of fragments based on known substrates. Of course, the specific substrates are not known, so in this case the researchers started with a set of several dozen natural (ie, non-synthetic) substrates of various other CYPs, both bacterial and eukaryotic. They then computationally screened the ZINC database of commercial molecules for fragments most similar to these substrates and purchased 63 of them. Perhaps not surprisingly given their similarity to natural products, these turned out to be more “three-dimensional” than conventional fragment libraries, as assessed both by the fraction of sp³ hybridized carbons and by principal moment-of-inertia.

Next, the researchers screened their fragments against CYP126 using three different NMR techniques (CPMG, STD, and WaterLOGSY). Since they were primarily interested in hits that bind at the active site, they also used a displacement assay in which the synthetic heme-binding drug ketoconazole was competed against fragments. This exercise yielded 9 hits – a relatively high 14% hit rate.

Strikingly, all of the hits are aromatic, and 7 of them could reasonably be described as planar. In other words, even though the biofragment library was relatively 3-dimensional, the confirmed hits were some of the flattest in the library! The researchers interpreted this to mean that “CYP126 might preferentially recognize aromatic moieties within its catalytic site,” but there could be something more general going on – perhaps aromatics are simply less complex, and thus more promiscuous.

Examining the fragment hits more closely, the researchers found that one of them – a dichlorophenol – produced a spectrophotometric shift similar to that produced by substrates when bound to the enzyme. This led them to look for similar structures among proposed Mtb metabolites. Weirdly, pentachlorophenol came up as a possible hit, and a spectrophotometric shift assay reveals that this molecule does have relatively high affinity for CYP126. Whether this is a biologically relevant substrate for the enzyme remains to be seen.

This is an intriguing approach, but I do have reservations. First, in constructing fragment libraries based on natural products, it is essential to avoid anything too “funky”. The Abell lab is one of the top fragment groups out there, well aware of potential artifacts, and has a long history of studying CYPs, but researchers with less experience could easily populate a library with dubious compounds.

More fundamentally though, I wonder about the basic premise of biofragments. The whole point of fragments is that they have low molecular complexity and are thus likely to bind to many targets, so is it realistic to try to extract selectivity data from them? Indeed, as we’ve seen (here and here), fragment selectivity is not necessarily predictive of larger molecules.

That said, the approach is worth trying. Even if it doesn’t ultimately lead to new insights into proteins’ natural substrates, it could lead to new inhibitors.