Tobacco mosaic virus (TMV) infection of tobacco is a well-known and extensively studied model system for which a number of genes and proteins involved in the systemic acquired resistance (SAR) have been characterized. Little is known about the metabolic changes connected with the infection and SAR. Here we describe the use of NMR spectroscopy in combination with multivariate data analysis to study the metabolic changes. Particularly 2-D NMR methods, such as 2-D J-resolved spectra and their projected spectra, are shown to be powerful tools in the metabolomic studies. The macroscopic view of the metabolomes obtained by NMR spectroscopy of crude extracts enabled the identification of a series of totally different metabolites that seem connected with resistance, such as the clearly increased 5-caffeoylquinic acid, alpha-linolenic acid analogues, and sesqui- and diterpenoids in the infected plant parts.

We propose a ligand screening method, called TINS (target immobilized NMR screening), which reduces the amount of target required for the fragment-based approach to drug discovery. Binding is detected by comparing 1D NMR spectra of compound mixtures in the presence of a target immobilized on a solid support to a control sample. The method has been validated by the detection of a variety of ligands for protein and nucleic acid targets (K(D) from 60 to 5000 muM). The ligand binding capacity of a protein was undiminished after 2000 different compounds had been applied, indicating the potential to apply the assay for screening typical fragment libraries. TINS can be used in competition mode, allowing rapid characterization of the ligand binding site. TINS may allow screening of targets that are difficult to produce or that are insoluble, such as membrane proteins.

One- and two-dimensional solid-state NMR experiments on a uniformly labeled intrinsic membrane-protein complex at ultra-high magnetic fields are presented. Two-dimensional backbone and side-chain correlations for a [U-13C, 15N] labeled version of the LH2 light-harvesting complex indicate significant resolution at low temperatures and under Magic Angle Spinning. Tentative assignments of some of the observed correlations are presented and attributed to the alpha-helical segments of the protein, mostly found in the membrane interior.

Most libraries for fragment-based drug discovery are restricted to 1,000–10,000 compounds, but over 500,000 fragments are commercially available and potentially accessible by virtual screening. Whether this larger set would increase chemotype coverage, and whether a computational screen can pragmatically prioritize them, is debated. To investigate this question, a 1281-fragment library was screened by nuclear magnetic resonance (NMR) against AmpC β-lactamase, and hits were confirmed by surface plasmon resonance (SPR). Nine hits with novel chemotypes were confirmed biochemically with KI values from 0.2 to low mM. We also computationally docked 290,000 purchasable fragments with chemotypes unrepresented in the empirical library, finding 10 that had KI values from 0.03 to low mM. Though less novel than those discovered by NMR, the docking-derived fragments filled chemotype holes from the empirical library. Crystal structures of nine of the fragments in complex with AmpC β-lactamase revealed new binding sites and explained the relatively high affinity of the docking-derived fragments. The existence of chemotype holes is likely a general feature of fragment libraries, as calculation suggests that to represent the fragment substructures of even known biogenic molecules would demand a library of minimally over 32,000 fragments. Combining computational and empirical fragment screens enables the discovery of unexpected chemotypes, here by the NMR screen, while capturing chemotypes missing from the empirical library and tailored to the target, with little extra cost in resources.