Accelerated Screening of Protein-Ligand Interactions via Parallel T2-Weighted 19F-MRI.

In drug discovery, ligands are sought that modulate the (mal-)function of medicinally relevant target proteins. In order to develop new drugs, typically a multitude of potential ligands are initially screened for binding and subsequently characterized for their affinity. Nuclear magnetic resonance (NMR) is a well-established and highly sensitive technology for characterizing such interactions. However, it has limited throughput, because only one sample can be measured at a time. In contrast, magnetic resonance imaging (MRI) is inherently parallel and MR parameters can conveniently be encoded in its images, potentially offering increased sample throughput. We explore this application using a custom-built 9-fold sample holder and a 19F-MRI coil. With this setup, we show that ligand binding can be detected by T2-weighted 19F-MRI using 4-(trifluoromethyl)benzamidine (TFBA) and trypsin as the reporter ligand and target protein, respectively. Furthermore, we demonstrate that the affinity of nonfluorinated ligands can be determined in a competition format by monitoring the dose-dependent displacement of TFBA. By comparing 19F-T2-weighted MR images of TFBA in the presence of different benzamidine (BA) concentrations-all recorded in parallel-the affinity of BA could be derived. Therefore, this approach promises parallel characterization of protein-ligand interactions and increased throughput of biochemical assays, with potential for increased sensitivity when combined with hyperpolarization techniques.

Artificial intelligence-driven shimming for parallel high field nuclear magnetic resonance.

Rapid drug development requires a high throughput screening technology. NMR could benefit from parallel detection but is hampered by technical obstacles. Detection sites must be magnetically shimmed to ppb uniformity, which for parallel detection is precluded by commercial shimming technology. Here we show that, by centering a separate shim system over each detector and employing deep learning to cope with overlapping non-orthogonal shimming fields, parallel detectors can be rapidly calibrated. Our implementation also reports the smallest NMR stripline detectors to date, based on an origami technique, facilitating further upscaling in the number of detection sites within the magnet bore.