Affinity measurements in a single NMR tube?

Last week we highlighted a ligand-detected NMR method to measure affinities of protein-ligand interactions. That technique, R2KD, requires preparing multiple NMR samples with the ligand at different concentrations. In a new open-access paper published in J. Am. Chem. Soc., Serena Monaco and collaborators at University of East Anglia and Universidad de Sevilla describe a method that can be done in a single NMR tube.

 

The researchers have actually combined two methods, chemical shift imaging (CSI) and Saturation Transfer Difference (STD) NMR, to create imaging STD NMR. We’ve written previously about STD NMR, which relies on the transfer of magnetization from an irradiated protein to a bound ligand. In CSI, chemical shift information is recorded at multiple slices along the length of an NMR tube. Normally the solution in an NMR tube is homogenous and so the chemical shifts would be identical at the bottom and top of the NMR tube. Here, though, the researchers create concentration gradients by carefully pipetting a solution containing ligand on top of a solution containing protein and allowing the ligand to diffuse the length of the NMR tube.

 

Like all things NMR-related, the mathematics get a bit complicated. One important factor is the rate of diffusion for a given small molecule. This “diffusion coefficient” can be experimentally measured by creating a concentration gradient in the absence of protein and measuring the ligand concentration at various positions in an NMR tube after a given length of time (typically more than 12 hours). Diffusion is dependent on molecular weight, so it is also possible to calculate the diffusion coefficient, and in fact the researchers found that the calculated values matched the experimental values for three different small molecules.

 

Knowing the diffusion coefficient helps establish the maximum ligand concentration to use and the ideal diffusion time. The researchers examined three different protein-ligand pairs, all of which had weak affinities, with K_D values from 0.2 to 2 mM. Measuring STD signals at different slices along the NMR tube effectively yields STD signals at different concentrations of ligand, and fitting this to an equation allows calculation of the dissociation constant. For the three model systems the affinities agreed with literature values, which had been determined using ITC or WAC.

 

One nice feature of imaging STD NMR is that it can identify non-specific binding. This is because STD signals vary depending in part on how close a proton on the ligand is to the protein, resulting in different STD signals for different protons for specific binders. If this “epitope pattern” is lost at higher concentrations, this suggests non-specific binding, where the ligand can bind in random orientations to multiple sites on the protein. The researchers demonstrated this for one of their model systems: tryptophan binds specifically to bovine serum albumin with a dissociation constant of 0.2 mM, but above 1 mM or so the epitope disappears, suggesting non-specific binding.

 

Imaging STD NMR does have some limitations. For one thing, it requires a high initial concentration of ligand: 30 mM in the case of tryptophan, and even higher for the other two ligands. Most small molecules are nowhere near this soluble in water. The researchers suggest that ligands could be dissolved in DMSO and placed on the bottom of the NMR tube, with the protein solution gently layered on top. They show that the concentration gradients develop in a similar manner as a fully aqueous system, but acknowledge that high DMSO concentrations may not play well with most proteins.

 

Also not stated is the sensitivity of the method for higher affinity binders. Last week’s R2KD could measure affinities as tight as 10 µM, but it is unclear how much below 200 µM imaging STD NMR can go.

 

Finally, as we noted in 2019, STD effects are remarkably complex and not well-correlated with affinity. In particular, binding kinetics can play a role in the strength of the signal. It would have been nice to see more than three protein-ligand pairs tested.

 

All that said, this is an intriguing approach. Laudably, the researchers provide extensive supporting information, including mathematical derivation of the fitting equations, a spreadsheet, NMR pulse sequences, and macros. I’ll be curious to see how it works for others.

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.

Stabilizing protein-protein interactions: part 3 (fragment linking)

Stabilizing protein-protein interactions is becoming increasingly popular, and not just for PROTACs. Nearly three years ago we highlighted the use of crystallographic screening to find fragments that could stabilize interactions between the adapter protein 14-3-3δ and peptides derived from p53, a prominent cancer target. After noting how much work lay ahead, we ended the post with, “expect a part 3!” This has now been published (open access) in Angew. Chem. by Adam Renslo, Luc Brunsveld, Michelle Arkin, Christian Ottmann, and collaborators at UCSF and Eindhoven University of Technology.

 

In addition to crystallographic fragment screening, the researchers had previously performed a disulfide Tethering screen on the 14-3-3δ protein, which we described here. The fragments from the two screens bound next to one another, so the researchers decided to link them. They started by solving the crystal structure of compound 1 disulfide-bonded to 14-3-3δ in the presence of fragments from the crystallographic screen as well as a peptide derived from estrogen receptor alpha (ERα, another anti-cancer target). These co-structures guided the synthesis of new linked molecules, and these were soaked into crystals of 14-3-3δ and the ERα peptide. Compound 6 gave strong electron density and overlayed nicely on the initial fragments.

 

 

To determine whether the linked molecule could stabilize the 14-3-3δ/ERα complex, the researchers developed a fluorescence anisotropy assay with a dye-labeled peptide from ERα. Some of the linked molecules produced an increase in anisotropy, suggesting stabilization of the 14-3-3δ/ERα complex, but when the researchers ran the important control of repeating the experiment in the absence of 14-3-3δ they found that several molecules still increased anisotropy, which could be due to aggregation. (Adam published a nice early paper on aggregation and is thus particularly attuned to the dangers.)

 

Fortunately, some of the molecules passed this control, and with a robust crystallography system the researchers were able to use structure-based design to improve them, ultimately arriving at compound 24, which increased the affinity of the 14-3-3δ/ERα complex by 25-fold. It was also quite specific towards ERα, and did not increase the affinity of nine peptides from from other proteins for 14-3-3δ. The researchers attribute this selectivity to the fact that most other peptides would sterically clash with compound 24. (Not reported was the peptide from p53, which would be interesting.)

 

This is a nice paper on several levels. In addition to selectively stabilizing a therapeutically relevant protein-protein interaction, this is a rare example of starting with a covalent fragment and developing a non-covalent binder. (For another, see here.) Also, this is a good example of fragment linking, which is often challenging.

 

There is still a long way to go. The most potent molecules all contain amidine moieties, whose high polarity is a liability for cell permeability, let alone oral bioavailability. Moreover, the affinity of compound 24 is still quite weak, with a low ligand efficiency.

 

That said, with a wealth of structural and biological understanding I am optimistic further progress can be made, perhaps by rebuilding the covalent linkage to the protein, as was the case of sotorasib or this more recent paper from the UCSF team. I look forward to part 4!

Democratizing computational FBLD with BMaps

Computational approaches to FBLD continue to gain in power. For the most part, they require significant knowledge and installation of expensive, customized software. To remedy this, John Kulp, III and colleagues at Conifer Point Pharmaceuticals have introduced a new web-based application, BMaps, which they describe in a recent J. Chem. Inf. Mod. paper.

 

As the researchers note (and appropriately reference), there are more than a dozen virtual fragment-based design tools and another dozen web-based tools. BMaps (for Boltzmann Maps) aims to provide a full range of functions, from visualizing proteins, finding hot spots, docking fragments, and growing them. It also provides information on the energetics of bound water molecules, which as we’ve written can be crucial players in optimizing protein-ligand interactions.

 

Two key techniques used by BMaps are Grand Canonical Monte Carlo (GCMC) simulations and Simulated Annealing of Chemical Potential (SACP). The first entails comprehensive sampling of different fragment conformations on a protein of interest and assessing binding free energy. The second tool “forcefully inserts fragments into all the binding sites of the protein” and then removes them slowly to evaluate which are most difficult to remove, and thus most tightly bound. Together, GCMC-SACP can be used to evaluate fragment binding to any protein uploaded to the site from the protein data bank, AlphaFold, or any other source.

 

One nice feature of BMaps is a repository of several hundred proteins each with more than 100 fragment and water simulations. BMaps also contains a database of more than 4000 fragments, including MiniFrags. Users can import their own fragments or computationally deconstruct larger ligands. The paper itself is quite short, but the supporting information provides more guidance on how to use the software.

 

The researchers “aim to democratize the availability of accurate fragment and water maps,” a laudable goal. Most computational features are available with a free account, though with restrictions on the number of operations per month.

 

BMaps looks quite powerful and easy to use, but I do wish the researchers had included some full case studies, for example those used by the free FastGrow tool we highlighted last year. Try it out and let the community know what you think!