Review of 2020 reviews

An old curse runs, "may you live in interesting times." And 2020 has been interesting indeed. Amid all the tumult, Practical Fragments will maintain its tradition of ending the year with a post highlighting conferences and reviews.

 

Despite the travel restrictions caused by COVID-19, some conferences did go ahead, adapted to online formats: I highlighted CHI’s Fifteenth Annual Fragment-based Drug Discovery and their Eighteenth Annual Discovery on Target. Although these were quite successful, I think most of us are looking forward to returning to in-person events sometime in the coming year.

 

Perhaps because so many people were stuck working from home, the number of reviews of potential interest to fragment fans has soared to a record number of more than twenty. I’ve tried to group these thematically.

 

General

If you’re looking for a concise yet thorough review, Harren Jhoti and colleagues at Astex provide one in Biochem. Soc. Trans. Harren is one of the pioneers of FBDD, and the review touches on library design, detection of fragment binding, and fragment to lead strategies. A review in Front. Mol. Biosci. by Qingxin Li (Guangzhou Sugarcane Industry Research Institute) goes into more detail on fragment screening, optimization, and biological targets.

 

For the past five years a few fragment fanciers (myself included) have been writing annual reviews in J. Med. Chem. covering fragment-to-lead success stories from the previous year, each with a handy table showing fragment, lead, and key parameters. The 2018 edition, led by yours truly (Frontier Medicines), was published at the beginning of the year, while the 2019 edition, led by Wolfgang Jahnke (Novartis), just came out a few weeks ago. At the risk of self-promotion, both are well worth perusing to see the growing diversity of targets and emerging trends, such as covalent fragments.

 

Biophysics

Biophysical methods are by far the most commonly used for finding fragments, and an excellent overview of thermal shift, SPR, and NMR by Joe Coyle and Reto Walser (Astex) appears in SLAS Discovery. The goal is “to help the anxious biophysicist withstand the relentless unforeseen,” and the paper provides loads of practical advice. For example, over more than 50 thermal shift screens, “we have never derived anything useful from negative Tm shifts.” The researchers note that “SPR is particularly user-friendly and particularly prone to artifact, overinterpretation, and varying degrees of frustration.” As for validating ligand-observed NMR hits crystallographically, rates range from 5% to 80%.

 

As we noted earlier this year, crystallography is becoming increasingly dominant in fragment screening, and in Molecules Laurent Maveyraud and Lionel Mourey (Université de Toulouse) provide an overview of the process, covering theory, workflow, practical aspects, pitfalls, examples, and other emerging methods. David Stuart and colleagues at Diamond Light Source discuss structural efforts on SARS-CoV-2 proteins in an open-access paper in Biochem. Biophys. Res. Commun. As of late October this included more than 500 released structures of 16 different proteins. Efforts against the main protease (which I reviewed in Nat. Commun.) have led to molecules with mid-nanomolar activity, and the researchers rightly highlight the worldwide collaboration that has led to such rapid progress.

 

NMR

NMR is of course a biophysical technique, but there are so many papers this year that it makes sense to group them into their own section. Ray Norton (Monash Institute of Pharmaceutical Sciences) and Wolfgang Jahnke (Novartis) introduce a special issue of J. Biomol. NMR focused on “NMR in pharmaceutical discovery and development” by briefly summarizing the state of the art and introducing 13 articles, one of which we covered previously and three of which are highlighted below.

 

“NMR in target driven drug discovery, why not?” ask Gregg Siegal and collaborators at ZoBio and Gotham in an (open access) J. Biomol. NMR review. In addition to characterizing small molecules, proteins, and their interactions, the researchers present cases studies in which NMR data has helped clarify a crystallographic protein-ligand structure, or even suggested that the crystal structure represented at most a minor conformation in solution.

 

In other words, NMR is “the swiss army knife of drug discovery,” as Reto Horst and colleagues at Pfizer put it in another J. Biomol. NMR review. The researchers describe successful NMR fragment screens against difficult targets such as an ion channel and a large (145 kDa) trimeric enzyme. They also make a good case for using NMR to determine the solution conformations of small molecules early in a project, a strategy that has paid off in more than 15 Pfizer projects over the past six years.

 

Benjamin Diethelm-Varela (University of Maryland) focuses on using NMR for “fragment-based drug discovery of small-molecule anti-cancer targeted therapies” in ChemMedChem. This is a thorough yet accessible overview of FBDD, ligand- and protein-based NMR methods, plus ten case studies. “A practical perspective on the roles of solution NMR spectroscopy in drug discovery” is provided by Qinxin Li and CongBao Kang (A*STAR) in Molecules. As the title suggests, this review is fairly broad, and includes an interesting section on NMR screening in cells.

 

All these papers might have you thinking that NMR is a “Gold Standard,” and that phrase does indeed appear in the title of another Molecules review by Abdul-Hamid Emwas (King Abdullah University of Science and Technology) and a multinational group of collaborators. This is a large (66 page) monograph with 455 references and is particularly detailed on various NMR techniques; if you want to see the pulse sequence of the HSQC experiment or review the Einstein-Stokes equation this is the place to turn.

 

In addition to the six reviews on NMR above, two specifically cover ¹⁹F NMR. The first, from the J. Biomol. NMR special issue by Claudio Dalvit (Lavis) and colleagues, focuses on fluorine NMR functional screening, or n-FABS. This paper provides an excellent theoretical and practical overview of the technique, and includes a handy table of 17 published case studies. And in Prog. Nuc. Mag. Res. Spect. Peter Howe (Syngenta) reviews “recent developments in the use of fluorine NMR in synthesis and characterization.” As the title suggests, much ground is covered, from spectrometer technology to quantum chemistry calculations, and there is a short section on fragment-based screening.

 

Computational

Turning to in silico techniques, Floriano Paes Silva Jr. and collaborators at LaBECFar and several other (mostly) Brazilian institutes provide an open-access overview in Front. Chem. After summarizing FBDD they describe how computational techniques can help along the way, from druggability prediction to docking, de novo design, and assessment of ADMET properties and synthetic accessibility. The review ends with several case studies.

 

In an open-access article in Drug Disc. Today, Stefano Moro and colleagues at University of Padova focus on “the rise of molecular simulations in fragment-based drug design.” This accessible overview covers hotspot identification, hit identification and characterization, and hit to lead optimization, and includes a nice section on free energy perturbation.

 

Other topics

Molecular properties are critical for developing good drugs, and in J. Med. Chem. Christopher Tinworth (GlaxoSmithKline) and Robert Young (Blue Burgundy) “appraise the rule of 5 with measured physicochemical data.” This is packed full of good stuff including a supplementary table with calculated and measured data for hundreds of compounds. The summary is that molecular weight is much less important than (measured) lipophilicity and hydrogen bond donors. “Good practice is all about compromise, aiming to maximize efficacy and efficiency while navigating many potential pitfalls in molecular optimization.” People sometimes obsess over rules vs guidelines, and the researchers close by stating that “rules are for the obedience of fools and guidance of the wise.”

 

As a poll from several years ago suggested, fragment linking tends to be less common than fragment growing, though it can work spectacularly. In J. Med. Chem. Isabelle Krimm and colleagues mostly at Université de Lyon review 45 successful fragment linking case studies (though it would have been appropriate for them to acknowledge Practical Fragments for the clearly borrowed table of clinical compounds). While by no means exhaustive, this is a useful resource. Interestingly, only 20% of the examples display superadditivity.

 

Target-guided synthesis (TGS) can be thought of as a special case of fragment linking. In J. Med. Chem., Rebecca Deprez-Poulain and colleagues at Université de Lille review kinetic TGS, in which two components react irreversibly with one another in the context of a protein to form a higher-affinity binder. Kinetic TGS may have some practical advantages over reversible TGS (or dynamic combinatorial chemistry), but as the researchers note most examples start with compounds larger than fragments, and thus only 38% of examples lead to products with a molecular weight less than 500 Da. This could partly explain why only 6 of the 50 reported examples have gone into animal studies.

 

Finally, György Keserű and collaborators at the Hungarian Research Centre for Natural Sciences review covalent fragment-based drug discovery in Drug Discovery Today (open access). Library design and validation is well-covered, as are various methods for screening covalent fragments, and there is a handy table of some four-dozen published examples. Given the increasing popularity of covalent FBLD, this contribution should be of wide interest.

 

When I wrote my concluding post for 2019, COVID-19 was an obscure and nameless disease, and SARS-CoV-2 had not even been identified. I ended with, "may 2020 bring wisdom, and progress." We've gained both, though the cost has been incalculable. So I'll just close this post by thanking you for reading and commenting.

Solving protein-ligand NMR structures without isotopic labeling

Last week we highlighted a protein-detected NMR method that does not require expensive and sometimes difficult isotopic labeling of proteins. However, while that approach is able to provide affinity information, it does not provide structural information. A new (open-access) paper in J. Am. Chem. Soc. by Roland Riek, Julien Orts, and collaborators at the Institute for Molecular Physical Science and the University of Vienna tackles this challenge.

 

The approach builds on NMR Molecular Replacement (NMR²), which we last wrote about here. In NMR², brute force calculations obviate the need for assigning individual NMR peaks to specific protein residues, thereby sidestepping considerable up-front effort. Most of the new paper focuses on applying NMR² to ligand discovery for the oncogenic G12V mutant of KRAS, which I’ll briefly summarize.

 

The researchers start by screening the 890-membered DSI-poised fragment library (in pools of six, with each fragment at 0.6 mM) against KRAS using ligand-detected STD NMR. This produced 133 hits, which were then retested at 1 mM each using [¹⁵N,¹H]-HSQC two-dimensional protein-observed NMR, invalidating about 30% of them. Dose-response titrations were performed on the top 13 hits; all of them were found to be weak binders, with at best low millimolar affinity. NMR² was then used to determine protein-ligand structures for some of these hits. That information guided the design of additional ligands, which had slightly higher affinities.

 

This thorough description of the NMR² workflow should be useful if you’re trying to do this at home. But what really caught my eye was a bit at the very end of the paper describing a new relaxation-filtered NOESY pulse sequence. Specifically, “an inversion recovery pulse block serves as a T₁ filter, followed by a perfect echo sequence and a CPMG without J-modulation, as a T₂ filter.” In essence, the experiment takes advantage of the fact that proteins relax more rapidly than small molecules, so NMR peaks coming from the protein are filtered out. But NMR peaks from protons in the ligand that are in close proximity to protons on methyl groups of the protein are observed, and the intensity of these peaks correlates with the distance between ligand and protein protons. Feeding these distance constraints into NMR² generates a three-dimensional structural model. The researchers compare models generated using NMR² on unlabeled KRAS to those generated using NMR² on labeled KRAS and show that they are roughly similar.

 

This is a neat approach, and it will be interesting to see whether it catches on. According to our poll last year ligand-detected NMR has fallen to fourth place among fragment-finding methods, and protein-detected NMR is in seventh place. Perhaps approaches like this and that described last week will usher in a new era of NMR for FBLD.

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.

Solving structures with selective labeling and NMR2

Protein-detected NMR first enabled fragment-based lead discovery way back in 1996, but improvements in crystallography have now allowed synchrotrons to surpass big magnets as preeminent tools to determine how fragments bind to proteins. One of the major challenges in NMR is assigning the chemical shift values of atoms in all the individual amino acid residues. A technique called NMR Molecular Replacement (NMR²) sidesteps the need for this tedious, time-consuming process. A refinement to this technique, making it more broadly applicable, has just been published (open-access) in Sci. Reports by Julien Orts (University of Vienna), Martin Scanlon (Monash University) and collaborators.

 

As we discussed previously, NMR² relies on intensive calculations using experimental intermolecular NOEs between a protein and a ligand to generate a model. Although the method does not require assignment of backbone or side chain chemical shifts, it does require high-quality spectra. For example, if the spectra of several amino acid residues overlap it is impossible to distinguish them (this applies to conventional NMR methods too). The researchers realized that one way to simplify the spectra is through selective labeling, in which the methyl groups of the amino acid residues alanine, isoleucine, leucine, valine, and threonine are isotopically labeled with ¹³C. Going one step further, the entire protein can be deuterated (rendering most of the protein invisible to NMR), while these methyl groups retain ordinary hydrogen atoms.

 

For the present study, the researchers focused on the protein EcDsbA, an antibacterial target we’ve written about previously. They selectively labeled methyl groups so that, in isoleucine, leucine, and valine, only one of the two methyl groups was labeled. That reduced the total number of protons to just 6% of the unlabeled protein.

 

The researchers then solved the structure of EcDsbA with a previously identified ligand. At 23 heavy atoms the ligand is on the large side, though with an affinity of just 0.9 mM it presents a difficult test case. A total of twelve intermolecular NOEs were used in NMR² to build a model of the complex. One challenge with NMR² is that there may not be a single solution. For example, if two methionine methyl groups are both near a ligand, it may be impossible to determine a unique binding mode. This turned out to be the case, and the top two structures had different positions for a carboxylic acid group and a phenyl in the ligand.

 

To benchmark NMR², the protein-ligand complex was also determined using conventional two-dimensional techniques (HADDOCK and CYANA, which made use of assigned chemical shifts) as well as X-ray crystallography. These all agreed with the NMR² model in placing a phenylpropyl moiety from the ligand in a hydrophobic groove, but they differed in the placement of the carboxylic acid and the other phenyl moiety: the top scoring NMR² model agreed with the crystal structure and the CYANA NMR structure but differed from the HADDOCK structure, which was similar to the second-best NMR² model. Before assuming that the crystallographic structure is correct, though, it is worth noting that the ligand makes crystal contacts with a neighboring protein, and the electron density around the ambiguous phenyl is weak.

 

This is a nice demonstration of the utility of NMR². It seems to provide similar information as classic NMR methods, but the time taken is “orders of magnitude” less. And selective labeling should make NMR² applicable to even larger proteins. I look forward to seeing more people use this strategy.

Inter-ligand STD NMR: Better than ILOE?

Although our poll in 2019 suggested that crystallography has surpassed NMR in FBLD, not all proteins can be crystallized. Ligand-detected methods such as saturation transfer difference (STD) NMR can be particularly useful for quickly identifying individual fragment binders and getting some sense of how they bind. A new variation published (open access) in Pharmaceuticals by Jesus Angulo and collaborators at University of East Anglia and Universidad de Sevilla provides information on the relative binding modes of two ligands.

 

Long-time readers may remember the inter-ligand NOE method (ILOE) we wrote about in 2010, in which proximity of two ligands is assessed by measuring NOE signals between them. However, despite being described more than 20 years ago, the technique seems to be rarely used, with fewer than a dozen papers in Pubmed, perhaps because ILOE requires large amounts of both protein and NMR time.

 

The new method is called inter-ligand STD NMR (IL-STD NMR), and it was discovered serendipitously while studying the binding of the drug naproxen to bovine serum albumin (BSA). As Teddy discussed several years ago, STD NMR normally involves irradiating specific protons in a protein (for example, the hydrogen atoms on buried methyl groups) and then measuring the “transfer” of this magnetization to bound ligands. When the researchers instead irradiated protons on one end of naproxen, they found that while the STD effect fell along the length of the molecule as expected based on distance, the signal suddenly increased at the other end of the molecule. Naproxen is known to bind to three sites on BSA, and this increased signal was attributed to the proximity of two adjacently bound naproxen molecules.

 

Inspired by this observation, the researchers developed IL-STD NMR. The experiment requires two samples, with two NMR experiments on each. One sample contains the protein and ligand of interest, while the other sample also contains a “reporter ligand” with a known binding mode. For each sample, one NMR experiment is run with selective irradiation of protons on the protein, while the other is run using irradiation of the reporter ligand. Comparison of the spectra reveals which regions of the ligand of interest are near the reporter ligand. The researchers demonstrated that the method works using a model system they had previously studied, the cholera toxin subunit B (CTB), which binds two ligands at nearby sites.

 

Importantly, the time and amount of protein is considerably less than required for ILOE: in this case 2 hours of NMR time and 0.3 mg of protein compared with 88 hours (!) and 1.8 mg. Moreover, the experiment could be run on a 500 MHz NMR, which is a relatively common instrument.

 

IL-STD NMR does have limitations. The researchers note that it is important to avoid irradiating the ligand of interest while irradiating the reporter ligand. Also, the approach obviously only applies to proteins with two nearby pockets (or one larger pocket). Still, it does look interesting, and I could imagine it being used as part of a screening cascade to find candidate fragments for merging or linking. What do the NMR aficionados think?

How much information can NMR provide?

A frequent assumption in fragment-based lead discovery is that similar fragments have similar binding modes, which are conserved as the fragments are elaborated. However, this isn’t always the case, a fact that can complicate optimization. Ideally multiple crystal structures help guide the chemistry, but in the real world crystal structures can be difficult to obtain.

One of the seminal papers in FBLD used NMR rather than crystallography to guide design, a strategy still used today. But how effective is NMR at assessing the binding modes of related fragments? This is the question that Isabelle Krimm and colleagues at the Université de Lyon sought to answer in a paper published a few months ago in PLOS ONE.

The researchers were interested in the inflammatory enzyme peroxiredoxin 5 (PRDX5), and they examined its interactions with five catechols: the parent unsubstituted molecule and four derivatives with substituents ranging from methyl to phenyl. Although catechols are PAINS, the researchers took pains to carefully examine the NMR spectra to look for signs of misbehavior.

Two NMR techniques were used, saturation transfer difference (STD) NMR and chemical shift perturbation (CSP). STD is nice because it is a ligand-detected method: you don’t need to go to all the work of assigning the chemical shifts of the protein. One piece of information from an STD experiment is whether a hydrogen atom is exposed to solvent or buried close to the protein, and in this case three of the catechols showed one particular hydrogen atom was exposed to solvent. The unsubstituted catechol provided only a single NMR peak and thus no information, and the fifth catechol was also not very informative, though it did seem to bind. Repeating this “epitope mapping” of all the catechols with human serum albumin instead of PRDX5 gave different results, suggesting a different binding mode.

Of course, there is only so much information you can get from ligand-detected NMR, so the researchers turned to protein-detected NMR and examined the CSPs of proton-nitrogen cross peaks using ¹⁵N-HSQC experiments. They also calculated CSPs for various potential binding modes and compared these with the experimentally observed CSPs to generate models. These suggested a common binding mode for the same three catechols that STD revealed as having a single solvent-exposed hydrogen atom each. Combining all this information led to specific binding models for these three fragments.

But how good are the models? Happily, the researchers were able to obtain crystal structures of four of the catechols bound to PRDX5, and these agree quite well with the NMR-derived structures. Unfortunately, the fifth catechol couldn’t be characterized bound to the protein crystallographically; NMR also suggested that this bound differently than the others.

So in the end, NMR was able to successfully predict that three ligands had similar binding modes, while another likely doesn’t. The process does seem to require a fair bit of effort. Nonetheless, in cases where crystallography is difficult or impossible, it may be the best way to get essential structural information, and this paper provides a good road map.

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.

NMR structures without protein assignments

Our latest poll asks how much structural information you need to advance a fragment (please vote on the right hand side of the page). On this subject, a recent paper by Marielle Wälti, Roland Riek, and Julien Orts in Angew Chem. demonstrates a new NMR method.

Researchers typically begin an NMR structure campaign by examining the chemical shift perturbations (CSPs) of proton-nitrogen or proton-carbon crosspeaks from an isotopically labeled protein in the presence and absence of a ligand. If you know which crosspeaks correspond to which specific atoms in an amino acid residue, you can deduce the ligand binding site by looking for the residues with the largest CSPs. Next comes the measurement of nuclear Overhauser effects (NOEs) between atoms in the ligand and atoms in the protein; these are exquisitely dependent on distance, so if you have enough measurements you can use these to accurately dock your ligand into the binding site of your protein.

This is how SAR by NMR was done more than twenty years ago, and it still works well today, but it is neither fast nor easy. In particular, the initial step of assigning the hundreds of protons, nitrogens, and carbons in a typical protein can be daunting.

To streamline the process, the researchers developed NMR molecular replacement (NMR²), first published last year (here) and presented by Julien at FBLD 2016. Rather than requiring knowledge of which peaks correspond to which specific protein atoms, NMR² relies on the increasing power of computers to run large numbers of complex calculations. Various docking poses will generate different NOEs, so exhaustively and iteratively examining these possibilities and comparing them with the experimental data should generate an optimal model. (NMR² does require that the structure of the protein is known, so you know the residues surrounding a ligand-binding pocket, even if you don’t know their chemical shifts. Also, the protons of the ligand are assigned, and in fact the intramolecular NOEs of the ligand itself are an important input.)

In the new paper the researchers apply NMR² to a complex between the onocology target MDMX and a previously disclosed high nanomolar binder and find good agreement (1.35 Å RMSD) with the crystal structure.

The researchers then turn to “ligand #845,” which binds with millimolar affinity to the oncology target HDM2. A total of 33 intramolecular NOEs (from ligand #845) and 21 intermolecular NOEs (between ligand #845 and HDM2) were fed into NMR² and used to crank through 54,000 structure calculations in a few hours to produce a binding model. No crystal structure was available, but conventional NMR methods support the model.

This seems like a rapid and powerful approach, but readers of this blog are probably wondering how well it will apply to fragments. Clearly NMR² is sufficiently sensitive to weak binders. However, with 24 non-hydrogen atoms and a molecular weight of 354 Da, ligand #845 is too large to be called a fragment. Smaller molecules will have fewer hydrogen atoms and thus fewer intramolecular and intermolecular NOEs, decreasing the information content of the model. It will be fun to plumb the lower ligand size limits for this technique – leave a comment if you’ve done so!

NMR for SAR: All about the ligand

In last week’s post we described a free online tool for predicting bad behavior of compounds in various assays. But as we noted, you often get what you pay for, and computational methods can’t (yet) take the place of experimentation. In a new (open-access) J. Med. Chem. paper, Steven LaPlante and collaborators at NMX and INRS describe a roadmap for discovering, validating, and advancing weak fragments. They call it NMR by SAR

 

Unlike SAR by NMR, the grand-daddy of fragment-finding techniques which involves protein-detected NMR, NMR for SAR focuses heavily on the ligand. The researchers illustrate the process by finding ligands for the protein HRAS, for which drug discovery has lagged in comparison to its sibling KRAS.

 

The researchers started by screening the G12V mutant form of HRAS in its inactive (GDP-bound) state. They screened their internal library of 461 fluorinated fragments in pools of 11-15 compounds (each at ~0.24 mM) using ¹⁹F NMR. An initial screen at 15 µM protein produced a very low hit rate, so the protein concentration was increased to 50 µM. After deconvolution, two hits confirmed, one of which was NMX-10001.

 

The affinity of the compound was found to be so low that ¹H NMR experiments could not detect binding. Thus, the researchers kept to fluorine NMR to screen for commercial analogs. They used ¹⁹F-detected versions of differential line width (DLW) and CPMG experiments to rank affinities, and the latter technique was also used to test for compound aggregation using methodology we highlighted in 2019. Indeed, the researchers have developed multiple tools for detecting aggregators, such as those we wrote about in 2022.

 

Ligand concentrations were measured by NMR, which sometimes differed from the assumed concentrations. As the researchers note, these differences, which are normally not measured experimentally, can lead to errors in ranking the affinities of compounds. The researchers also examined the 1D spectra of the proteins to assess whether compounds caused dramatic changes via pathological mechanisms, such as precipitation.

 

The researchers turned to protein-detected 2D NMR for orthogonal validation and to determine the binding sites of their ligands. These experiments revealed that the compounds bind in a shallow pocket that has previously been targeted by several groups (see here for example). Optimization of their initial hit ultimately led to NMX-10095, which binds to the protein with low double digit micromolar affinity. This compound also blocked SOS-mediated nucleotide exchange and was cytotoxic, albeit at high concentrations.

I do wish the researchers had measured the affinity of their molecules towards other RAS isoforms as this binding pocket is conserved, and inhibiting all RAS activity in cells is generally toxic. Moreover, the best compound is reminiscent of a series reported by Steve Fesik back in 2012.

 

But this specific example is less important than the clear description of an NMR-heavy assay cascade that weeds out artifacts in the quest for true binders. The strategy is reminiscent of the “validation cross” we mentioned back in 2016. Perhaps someday computational methods will advance to the point where “wet” experiments become an afterthought. But in the meantime, this paper provides a nice set of tools to find and rigorously validate even weak binders.

A bright idea for rapid affinity measurements

Finding fragments that bind to a target is important but so is measuring their affinities. NMR methods can find even weak fragments, but accurately assessing affinities takes time. In a recent (open-access) J. Am. Chem. Soc. paper, Felix Torres, Roland Riek, and collaborators at the Institute for Molecular and Physical Science and NexMR provide a new, fast method.

 

The approach is based on photochemically induced dynamic nuclear polarization (photo-CIDNP), which we wrote about here; Felix also spoke about it at the FBDD-DU meeting in June. As the name implies, the technique involves illuminating NMR samples to electronically excite ligands, thus increasing the signal to noise ratio of the NMR signal by as much as 100-fold. Previous work focused on using the method to identify binders, even with cheap, benchtop NMR instruments.

 

The new paper describes how to quantitatively measure dissociation constants using photo-CIDNP. The theory gets a bit hairy, but the basic idea is that the more photochemically excited ligand that binds to the protein, the more the signal decreases. A series of samples are prepared with increasing concentrations of ligand and either no protein or a fixed concentration of protein. After measuring the NMR signals, the data are plugged into equations to derive the K_D values in a method called CIDNP-K_D.

 

As the researchers have previously noted, not every ligand can be photosensitized. However, dissociation constants can still be measured for these using competition experiments with previously characterized reporter ligands that can polarized, akin to using to NMR competition studies with ¹⁹F reporter ligands (see here).

 

So how well does the technique work? The researchers first turned to the PDZ2 domain of a phosphatase called hPTP1E, which is involved in cell proliferation. They measured the affinities of a series of peptides having 4 to 8 amino acid residues and compared these values to those obtained using two dimensional [¹H,¹⁵N]-HSQC chemical shift perturbation, the gold standard NMR technique. Affinities ranged from low micromolar to low millimolar, and there was reasonable agreement (generally within about two-fold) between both techniques. Most of the peptides contained tryptophan, which is suitable for photo-CIDNP, but CIDNP-K_D also worked in competition mode when non-tryptophan containing peptides were competed against peptides containing tryptophan. And the technique was fast, with each datapoint taking only 30 seconds for photo-CIDNP compared to as long as 80 minutes for HSQC NMR.

 

Next the researchers turned to fragments. They had previously conducted a screen against the oncology target PIN1 and identified a number of fragment hits, two of which had been characterized in detail. The affinities of these were measured by CIDNP-K_D, and the low millimolar values agreed with those from HSQC NMR.

 

Another neat application described in the paper is “CIDNP-based epitope mapping,” which is based on the fact that an excited proton on a ligand that is in close proximity to the protein will relax more rapidly than one that is distant from the protein. This phenomenon is similar to STD epitope mapping, and the two methods yielded similar information for the two PIN1 ligands: one region of each molecule was buried in the protein, consistent with crystal structures.

 

One drawback of the technique is that, because measurements require fast protein-ligand exchange, CIDNP-K_D is limited to relatively weak binders (K_D > 10 µM), but this is usually not a problem in the early stages of a fragment program. A full affinity measurement takes about 15 minutes, which compares very favorably to two hours using [¹H,¹⁵N]-HSQC and without the need for isotopically labeled protein. It would be interesting to run head-to-head comparisons with two ligand-based NMR techniques we wrote about last year, imaging STD NMR and R2KD, to see how they compare in terms of speed, accuracy, and generality. Please let us know if you’ve done so.

SAR by STD: NOT

As noted last week, Practical Fragments has been on something of a crystallography binge. But according to polling, NMR is the most common fragment-finding method. And, according to a different poll, saturation transfer difference (STD) is the most popular NMR technique. Familiarity breeds complacency, and widespread assumptions go untested. A new paper in Front. Chem. by Jonas Aretz and Christoph Rademacher (Max Planck Institute and Freie Universität Berlin) suggests that this is a mistake.

In STD NMR, a protein is saturated by specific electromagnetic pulses, and the resulting magnetization transfers to bound ligands. Assuming that the bound ligands are in rapid equilibrium with ligands free in solution, this “saturation transfer” results in a reduction of NMR signal for the small molecule in the presence of protein compared to no protein. High affinity ligands will remain bound to the protein and thus be missed by STD NMR, but this is usually not relevant in FBLD, where most fragments bind with dissociation constants weaker than 10 µM.

A common assumption with STD NMR is that the strength of an STD signal increases with the affinity of the ligand (again, in affinity ranges between about 10 µM and 10 mM). Indeed, when STD NMR is used as part of a screening cascade, molecules showing the strongest effect are generally prioritized as hits. But is this assumption correct?

To find out, the researchers retrospectively analyzed a fragment screen against langerin, a carbohydrate-binding protein we discussed last year. When they plotted the STD amplification factor against the affinity (measured by SPR) for several dozen fragments, the resulting scatter plot showed no correlation.

Recognizing that experimental errors could obscure a true correlation, the researchers ran virtual STD experiments using COmplete Relaxation and Conformational Exchange MAtrix (CORCEMA) theory. They used well-characterized fragments with published crystal structures and affinities for some dozen diverse proteins. As they conclude, “varying saturation time, receptor size, binding kinetics, and interaction site… there were no conditions in which the STD NMR amplification factor correlated unambiguously with affinity.”

But it gets worse. When the researchers explored the effects of binding kinetics, they found that ligands with slower on-rates or off-rates also had lower STD signals. Several groups have advocated prioritizing compounds with slower-off rates, yet these are the very compounds STD is most likely to miss.

All in all this paper could go some way toward explaining the sometimes poor correlation between different fragment-finding methods.

That said, I’m no NMR spectroscopist, so I’m certainly not as qualified to comment on the importance of this paper as someone like Teddy, who co-wrote this how-to guide for STD NMR. I’d be interested to hear what NMR folks think, and whether we should rethink use of STD. In any case, this work is a useful reminder that skepticism is a scientific virtue.

A sharp NMR trick for rapidly measuring affinities

As noted in our poll last year, ligand-detected NMR ranks among the most popular fragment-finding approaches. The various methods are able to detect even weak binders, so determining affinities is important to effectively prioritize hits. This, however, can be time-consuming. In a recent J. Am. Chem. Soc. paper, Ridvan Nepravishta, Dušan Uhrín, and collaborators at CRUK Scotland Institute, University of Edinburgh, and Universidad de Sevilla present a clever way to speed up the process.

 

Normally, NMR spectra of small molecules show multiple spectral lines, with each line corresponding to a different atom or atoms (typically protons). Indeed, depending on the details, the signal from a single proton might be split into multiple peaks. All these signals are great for understanding the details of individual atoms, but the more lines there are, the lower the signal to noise ratio. For maximum sensitivity it would be nice to combine all the lines from all the atoms in a given molecule into a single, intense singlet. This is exactly what the researchers have done.

 

The approach is called Sensitive, Homogeneous And Resolved PEaks in Real time, or SHARPER. For the NMR aficionados out there, “when placed before the acquisition of the NMR signal, a train of spin-echoes in the form of the Carr-Purcell-Meiboom-Gill (CPMG) pulse sequence suppresses evolution due to chemical shifts and J couplings…. All these attributes of the CPMG pulse sequence are maintained when the spin-echo train is employed during the acquisition of the NMR signal. However, this time, the outcome is not a regular spectrum, but under certain conditions, a single spectral line formed as a sum of Lorentzian lines of contributing spins.”

 

The researchers initially applied SHARPER to two commonly used ligand-detected methods: ¹H STD, which we wrote about here, and ¹H CPMG, which we wrote about here. The first test system was human serum albumin (HSA) binding to naproxen. Keeping protein concentration constant at 9 µM and varying ligand concentration gave similar K_D values (210-280 µM) for standard STD, STD SHARPER, and CPMG SHARPER (conventional CPMG failed due to insensitivity at lower ligand concentrations). These values are an order of magnitude higher than those reported using SPR and ITC (25 and 10 µM, respectively) because of the high protein and ligand concentrations needed for conventional NMR approaches; when the SHARPER experiments were rerun at 1 µM HSA, the K_D values were 39 µM. Several other HSA ligands also gave good agreement with the literature.

 

Next, the researchers applied STD SHARPER to the anti-cancer target fascin, which we wrote about in 2019. An examination of 11 ligands from that study gave good agreement with the published dissociation constants. Importantly, SHARPER was faster than conventional approaches, with 15 K_D determinations per day instead of four.

 

Not content with this four-fold improvement in throughput, the researchers developed a new experiment based on line broadening called ¹H LB SHARPER. This allows the determination of 48 dissociation constants per day, and the results for HSA and fascin agreed with the other methods.

 

One of the most time-consuming aspects of most NMR-based affinity measurements is preparing and analyzing samples at multiple ligand concentrations, so the researchers turned to machine learning to choose which ligand concentrations would be most informative and choose just two of them rather than the six or more commonly used. This worked too, thereby potentially increasing throughput to 144 dissociation constants per day.

 

The researchers suggest that SHARPER could also be applied to some of the other recent NMR techniques we’ve discussed, such as PEARLScreeen and photo-CIDNP. Although I always emphasize that I’m no NMR spectroscopist, this strikes me as a neat, practical approach. What do you think?

New tools for NMR: 2017 edition

NMR was the first practical fragment-finding method, and continues to be popular. Just over the past year we’ve discussed several new techniques, (here, here, and here), and this post highlights three more.

In Angew. Chem. Int. Ed., Jesus Angulo and colleagues at the University of East Anglia describe differential epitope mapping by STD NMR (DEEP-STD NMR). STD NMR, the most popular of ligand-detected methods according to our poll, can provide some information as to which portions of a ligand are close to a protein, but doesn’t show where on a protein the ligand binds. In DEEP-STD NMR, two separate NMR experiments are conducted and the results compared to provide this information.

The researchers provide two implementation of the technique. In the first, the protein is “irradiated” at two different frequencies; for example, the aliphatic and aromatic regions. Protein residues that are directly irradiated will show a stronger STD to ligand protons than those that are indirectly irradiated, thus revealing whether one region of the ligand is closer to an aromatic or an aliphatic amino acid side chain. If the structure of the protein is known, this can then reveal the orientation of the ligand within the binding site. A similar experiment can be done using H₂O vs D₂O to determine whether a portion of a ligand is in close proximity to polar residues in the protein.

Water is the subject of the second paper, in J. Med. Chem., by Robert Konrat and colleagues at the University of Vienna and Boehringer Ingelheim. As we’ve previously noted, water often plays a critical role in protein-ligand interactions. The new method, called LOGSY titration, involves doing a series of WaterLOGSY experiments at different protein concentrations and plotting the signals for each proton in the ligand as a function of protein concentration; ligand protons close to the protein show steeper slopes. The researchers examine pairs of bromodomain ligands and demonstrate that LOGSY titration can confirm changes in binding mode previously seen by crystallography. The technique could also reveal what portions of the ligands make interactions with disordered water molecules, which are more difficult to detect in crystal structures.

Both of these techniques provide useful but incomplete information about ligand binding modes. A paper in J. Am. Chem. Soc. by Andreas Lingel and his Novartis colleagues describes how to generate more detailed models. The researchers used a deuterated protein in which all methyl groups (in methionine, isoleucine, leucine, valine, alanine, and threonine) were ¹³C-labeled. Multiple intermolecular NOEs between the protein and several previously characterized ligands were collected and the resulting distances fed into modeling software to produce good agreement with the known structures. More significantly, the researchers were able to use the method prospectively with two weak (0.9 and 2.8 mM) fragments. The binding models were sufficiently accurate to guide chemical optimization, resulting in molecules with 30-50 µM affinities. Subsequent crystal structures revealed that these bound as predicted. Impressively, this was done on a protein that forms 115 kD hexamers – larger than those typically tackled by NMR.

Teddy would normally close his NMR posts by stating – usually quite forcefully – whether he felt the technique was practical or not. I’m no NMR spectroscopist, so I’ll throw this question out to readers – do you plan to try any of these approaches?

Pacifichem 2021

Pacifichem, the last significant conference of 2021, has just ended. Traditionally held every five years, these meetings usually bring thousands of visitors from Pacific Rim countries to Honolulu. They are planned years in advance: symposia proposals were due in early 2018. Pacifichem 2015 saw the first symposium dedicated to FBLD, and that was so popular that a few of us planned one for 2020. The conference organizers decided to postpone the 2020 meeting in the hope that we could all meet in person. But SARS-CoV-2 had other plans, and instead of meeting in Hawaii we met on Zoom.

 

Time zones were challenging. Four-hour sessions started in the morning or evening Hawaiian time, which translated to 02:00 in Shanghai or 23:00 in Boston, respectively. In contrast to other virtual meetings almost all the presentations were live and not recorded, which meant that you only had one chance to see a talk.

 

Despite these challenges and universal Zoom-fatigue, the event came off quite well. With more than two dozen presentations I won’t attempt to cover everything but will instead just touch on a few themes.

 

Methods

Quite a few talks focused on methods, with NMR being especially well-represented. The symposium started with Will Pomerantz (University of Minnesota) discussing Protein-Observed Fluorine (PrOF) NMR, in which fluorinated amino acids are introduced into proteins. We’ve written about this previously, including Will’s longstanding interest in assessing shapely fragments. After reading about Mads Clausen’s fluorinated Fsp³-rich library, Will established a collaboration and has found 8 hits from 79 fragments screened against BET bromodomain proteins. He has also been able to optimize potent leads selective for either the BD1 or BD2 domains of BRD4.

 

Scott Prosser (University of Toronto) is also using NMR to study fluorine-labeled proteins, in this case GPCRs. And Michael Overduin (University of Alberta), one of the symposium organizers, is also studying membrane-bound proteins using NMR techniques.

 

On the extreme side of spectrometers, Chojiro Kojima described the 950 MHz NMR at Osaka University. This enables a ¹H-¹³C HSQC experiment on protein as dilute as 0.2 micromolar, which could be valuable for insoluble or hard-to-purify proteins. The facility is open to international collaborators. Chojiro also described isotopically labeling proteins with transglutaminase and ¹H{¹⁹F} STD NMR, which works even when the fluorine peak itself is broadened to invisibility.

 

But you don’t need a big magnet to do good science. Brian Stockman’s group at Adelphi University is composed entirely of undergraduates who use substrate-detected NMR to follow enzymatic reactions to find inhibitors of neglected parasitic infections.

 

All techniques can give false positives, and NMR can be very effective at weeding these out. As we described two years ago Steven LaPlante (NMX) has been developing methods to rapidly identify aggregators and has been assembling something of a taxonomy; more later.

 

But the conference was not all NMR all the time. Rebecca Whitehouse (Monash University) described a 91-compound “MicroFrag” library consisting of fragments containing 5-8 atoms, “somewhere in the land between solvents and fragments.” NMR and crystallographic screens of the difficult antimicrobial target DsbA gave very high hit rates, and both techniques successfully identified the large but shallow substrate-binding groove. In contrast, screening actual solvents or using the well-established FTMap computational approach did not clearly identify this groove.

 

The push in crystallography is towards increased speed, and Debanu Das described the high-throughput platform at Acclero BioStructures, which is capable of screening 1000 fragments per week, similar to XChem. But if even that is too slow for you, Marius Schmidt (University of Wisconsin-Madison) described mix-and-inject experiments using the European X-ray free-electron laser (XFEL). Much of the focus with this technique has been on high-speed enzymology, but since 100 datasets can be collected in 10 hours it can be used for high-throughput crystallographic screening too. An upgrade next year will increase this to 1000 datasets in 3 hours, though the resulting petabytes of data will no doubt create headaches for IT departments.

 

It’s not enough to find hits, you need to figure out what to do with them, and symposium organizer Martin Scanlon (Monash University) discussed a computational approach (GRADe, similar to Fragment Network) as well as the experimental (REFiL) approach we’ve discussed previously. Across 9 projects the techniques were successful at improving affinity, in some cases from unmeasurable levels.

 

Covalent Fragments

Several talks focused on covalent FBLD. Alexander Statsyuk (University of Houston) proposed five rules for covalent fragments: 1) ease of synthesis; 2) non-promiscuous electrophiles; 3) intrinsic reactivity should be the same within the library; 4) a given library should use the same electrophile; and 5) the electrophile should be on the end of the molecule with a minimal linker connecting it to the variable fragment. Some of these make sense, but it would have been fun to debate others over Mai Tais.

 

Dan Nomura (UC Berkeley) described using covalent fragments in chemoproteomic experiments, where he has identified more than 100,000 potentially ligandable hotspots in more than 16,000 human proteins. Among other applications, this allows him to make bifunctional molecules to bring two proteins together. A clear application is PROTACs, where the electrophilic fragment targets an E3 ligase, but he also described targeting the deubiquitinase OTUB1 to stabilize proteins.

 

Earlier this year we celebrated the approval of the KRAS^G12C inhibitor sotorasib. This target had long resisted drug discovery efforts; Ratmir Derda (University of Alberta) evocatively mentioned “waves and waves of medicinal chemists washing off its shore for 30 years.” Success was finally enabled using disulfide Tethering, and David Turner (Frederick National Laboratory) is now using this approach to interrogate nearly every surface-exposed residue by systematically mutating them to cysteines and screening against more than 1000 disulfide-containing fragments. He is well over half-way through the 85 mutants, and the resulting dataset should be valuable not just for drug discovery but for understanding molecular interactions.

 

Success Stories

With more than 50 drugs in the clinic derived from fragments, there were several success stories. Masakazu Atobe (Asahi Kasei) presented the discovery of the potent PKCζ inhibitors we wrote about here. And Chaohong Sun (AbbVie) described inhibitors of TNFα (see here), emphasizing the importance of robust biophysics and early committed chemistry.

 

Finally, Emiliano Tamanini (Astex) presented a nice fragment-to-lead effort to find a selective inhibitor of HDAC2. Despite some successes, histone deacetylase inhibitors tend to be non-specific and come with side effects. Emiliano described a fragment screen that identified a new metal chelator and used fragment merging to develop a molecule capable of crossing the blood-brain-barrier.

 

These last two stories in particular are examples of pursuing difficult targets, another theme throughout the conference. When asked about the challenges of targeting cancer-resistance-causing glucuronosyltransferases, Katherine Borden (University of Montreal) responded, “if you don’t try, where will you be?”

 

Bright words for these darkling days.

New tools for NMR

As most of you know, Teddy has retired from active blogging, which is unfortunate not just for the loss of his wit but also for the loss of his expertise – particularly regarding NMR. But you blog with the army you have, not the army you want, so I'll take a stab at two recent papers on the subject.

The first, published in J. Med. Chem. by Chen Peng and colleagues at software maker Mestrelab in collaboration with Andreas Lingel and colleagues at Novartis, describes an automated processing program for just about any type of ligand-observed NMR data. After going into some detail on how “Mnova Screen” works, the program was benchmarked on three experimental data sets (on undisclosed proteins) which had previously been processed manually. The first was ¹⁹F data from a collection of 19 mixtures of up to 30 fluorinated compounds each – 551 altogether. Here the program performed quite well, identifying 56 of the 64 hits identified manually and misidentifying only 4 compounds as hits. Most of the false positives and false negatives were close to the predetermined cutoff threshold, which can be set as stringent or lax as desired.

T1ρ and STD NMR experiments on 55 individual protein-compound complexes were also examined, and the results were similarly positive. Of course, single compound experiments are easy to analyze, and the real test was with a set of 1240 compounds in 174 pools. Here the program was not quite as good, missing 16 of the 50 manually identified hits and coming up with 74 hits that had not been identified manually. Although most of these were false positives, closer inspection revealed that 10 of them are probably real. Moreover, some of the “false negatives” should perhaps not have been classified as hits in the first place. Clearly the program isn’t perfect, but it does seem to be a quick way to triage large amounts of data.

Of course, ligand-detected NMR provides at best only limited information on binding modes, which is where the second paper comes in, published in J. Biomol. NMR. by Mehdi Mobli (University of Queensland), Martin Scanlon (Monash University) and collaborators at Bruker and La Trobe University. The researchers were interested in finding inhibitors of the bacterial protein DsbA, and a previous screen had identified a weak fragment that initially proved recalcitrant to crystallography.

One of the best methods to determine the binding mode of a ligand is to look at intermolecular NOEs, NMR signals which only show up when two atoms are in close proximity to one another. In theory you can look at NOEs from ligands to the backbone amide protons in proteins, but this is technically challenging for aromatic ligands, of which there are many. Proteins have plenty of methyl groups – so many in fact that it can be difficult to correctly assign each methyl group to a specific residue, leading some researchers to only focus on isoleucine, leucine, and valine (ILV). However, by carefully studying more than 5000 high-quality protein ligand complexes, the researchers found that looking at all the methyl groups in a protein (ie, including those found in alanine, threonine, and methionine) greatly increases the number of protein-ligand complexes suitable for analysis.

The researchers were able to assign most of the methyl groups in DsbA using several approaches, and this allowed them to identify 11 NOEs between their ligand and ILV methyl groups. Modeling was unable to provide a unique binding mode, but by including 8 more NOEs to threonine and methionine methyl groups a single binding mode for the ligand was determined. Crystallography came through in the end too and confirmed the NMR-derived model.

Teddy would normally end his NMR posts by stating – often forcefully – whether he thought the tools under discussion were practical or not. NMR is one of the most popular methods out there, so new tools are clearly welcome. Since I'm no expert on the subject, I'll ask readers to weigh in – what do you think?

Druggable is as Druggable Does; Or a Million Ways to use NMR

As we all know, the closure of sites is a bad thing for those of us in Pharma.  One very small silver lining is that this frees up a lot of very nice work to be published.  The former BI site in Laval has been closed for a year and we are still seeing great papers coming out.  In this one in JMed Chem,  LaPlante and co-workers tell us about their fragment efforts against HCV helicase. 

HCV has recently had drugs approved for its treatment, but as with any virus, different modes of treatment are important.  The ATP-dependent helicase activity is found in the C-terminal 2/3 of the NS3 protein. Helicase activity is straight forward to measure and there has been some success in terms of non-viral specific inhibitors.  The inhibitors found to date have been found to act through undesireable mechanisms, but with a wealth of structural information there is no reason why helicase is inherently undruggable.  With this information in hand, they decided to target site 3+4 (green sticks are DNA from the structure), near the most conserved residue W501.  The ATP-binding site is 1+2 for reference. 

 Their first approach was to screen the 1,000,000+ corporate compound collection.  As you would expect for a paper blogged about here, they failed to find anything interesting (all the inhibitors worked by undesireable modes).  So, on to the FBDD campaign, to save the day once more.  The used a "shotgun" approach with their fragment screen:

One source of compounds came from an earlier HTS where they rejected fragment-like molecules for lack of potency, additional HCS screening of in house fragment collection, commercial fragments were screened in an SPR assay, virtual screening, and NMR.  They had a stringent workflow aimed at producing quality compounds for X-ray.  [The in-house fragment collection was 1000 compounds.]  This, along with NMR, validated ligands that bound to site 3+4.  They note one particularly noteworthy problem: high false positive rates due to the high ligand concentrations needed for the assays.  This lead to aggregation, solubility, and promiscuity.  This lead them to implement specific assays designed to eliminate these compounds (two NMR papers published in 2013, ref 18). 

They then clustered the best hits into 9 chemotypes:

 They used an "Analog by Catalog" approach and soaked or co-crytallized the best compounds into crystals.  S6, S7, and S9 were not found to bind to helicase in the crystallization trials and were deprioritized.  S5 was found at Site 3+4, but also others.  S1-4, and S8 were found to bind solely to site 3+4 (12 examples shown overlain). The key feature of this is the compounds are centralized in a wide groove over W501.  The topology of the binding site (wide groove and small lipophilic pocket) meant that optimizing for potency could be challenging.

From this, they decided S2-S4 were the most promising.  In the end, the focused on the S2 indole series as the most promising.  The S2 stereotype 1

was found from an STD-NMR screen of 3 fragment per sample (300 uM fragment and 3.5 uM helicase).  They then, much to my heart's delight, they reached into the NMR cabinet for line broadening and competition experiments confirming it binds in site 3+4.  X-ray confirmed the binding mode, but potency was not improved with chemistry.  So back into the NMR cabinet they went: a methyl resonance assay, 

 15N TROSY showing peaks shifting upon addition of a derivative of 1, and 19F NMR!  OMG, how awesome is this?  

In terms of the chemistry, removing the Br does not change the potency, but did change the orientation of the compound in the binding site.  Further elaboration led to this compound 19 (3 uM and 0.23 LE):

It contains a nitro group, think what you may.  In order to confirm the binding affinity of the compound without immobilizing protein, they used the methyl resonances to do the titrations.  The two separate peaks they followed gave values of 32 and 28 uM (+/- 8).  Given the broadness of these peaks, I think this is a pretty decent assay, although it is an order of magnitude different than the biochemical Kd.  However, subsequent structural studies revealed that there is significant structural dynamic differences between pH 6.5 and 7.5.  ITC gave the same number (33 uM and enthalpy driven); however, the ITC had to be run at high compound concentration and a different pH.  They then went off the deep end and decided to use CD (I can't link to a previous post of using CD because we have never had a post where someone used it).  With a horrible assay (don't even get me started on near-UV CD as a readout of tertiary structure), they got reasonably close to the Kds determined by ITC and methyl-NMR.  

This is a very nice example of not being afraid of a target and using all available tools to advance hits against it.  It also shows the WIDE range of NMR experiments that can be used and that are easy and practical.  In terms of full disclosure, Steven LaPlante is a FOT (Friend of Teddy) and I have been working with him.