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.