10 December 2014

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.


Dr. Teddy Z said...

I like to see work like this. I have always been a strong proponent of Epitope Mapping (EM). The question to ask is why didn't they use the epitope map to dock the fragments rather than going to the much more resource intensive 15N experiments? Or, maybe they should have done the EM-guided docking in ADDITION and then compared it to the CSP derived poses.

Julien said...

It would be interesting to see if this is something that could be anticipated from docking and/or MD simulations

Darren B. said...

It is nice to see continued caution around T1 in epitope mapping by STD-NMR. But there are ways to deal with protons having differing T1s, rather than exclude all non-aromatic protons. From STD build-up curves to CORCEMA.

Pete said...

I have some comments for the authors that I hope will be seen as constructive. Firstly, ligand efficiency adds nothing of substance to the study so please don’t feel pressured to tabulate LE values just because the subject of the study is FBDD. LE is actually thermodynamic nonsense (see http://dx.doi.org/10.1007/s10822-014-9757-8 ) and to include it gratuitously actually weakens a good study needlessly.

It is good that the authors recognize that catechols are considered by some to be PAINS although this study demonstrates that they can be very useful probes and the authors should be assertive about this. Furthermore, the catechol substructure is present in neuromodulators such as epinephrine, norepinephrine and dopamine.

I have included phenols in fragment libraries for NMR screening. While we worry about phenols becoming conjugated and getting oxidized, these compounds represent a good way to present a hydrogen bond donor (phenols are stronger donors and weaker acceptors than aliphatic alcohols) to target proteins. Phenols are generally easy to source and the hydrogen bonding characteristics of the hydroxyl can be modulated with appropriate substitution (e.g. 4-CF3) which helps when assembling SAR. Catechol with an intramolecular hydrogen bond would look very similar to benzimidazole.