Fully Automated Characterization of Protein-Peptide Binding by Microfluidic 2D NMR

Advancing Protein Detection and Analysis Based on Ag/Au PHCN for Enhanced SERS Sensitivity and Specificity in Biomolecular Diagnostics

In the realm of disease diagnostics, particularly for conditions such as proteinuria and hemoglobinuria, the quest for a method that combines accurate, label-free detection of protein compositions and their conformational changes remains a formidable challenge. In this study, we introduce an innovative Ag/Au plasmonic hybrid coupling nanoarray (Ag/Au PHCN) architecture marked by sub-10 nm interparticle gaps. These nanoarrays, leveraging plasmonic hybrid coupling and synergistic enhancement mechanisms, create a plethora of uniform surface-enhanced Raman spectroscopy (SERS) hotspots. The Ag/Au PHCN substrates demonstrated unparalleled sensitivity in the unmarked detection of hemoglobin (HGB), bovine serum albumin (BSA), and cytochrome C (Cyt.C) in bodily fluids, incorporating the advantages of high sensitivity, high reproducibility, durability, recyclability, and biocompatibility. Notably, the detection limits for BSA and HGB are unprecedented at 0.5 and 5 ng/mL, respectively. This achievement sets a new benchmark for label-free protein detection using two-dimensional nanostructures. Crucially, the Ag/Au PHCNs possess the novel capability to discern protein conformational changes post denaturation, underscoring their potential in probing protein functionalities. Most importantly, these nanoarrays can differentiate between normal and proteinuria-affected urine samples and monitor protein content variations over time, heralding a new era in clinical diagnostics with particular relevance to proteinuria and hemoglobinuria detection.

A native mass spectrometry approach to qualitatively elucidate interfacial epitopes of transient protein-protein interactions

Native mass spectrometric analysis of TPR2A and GrpE with unpurified peptides derived from limited proteolysis of their respective PPI partners (HSP90 C-terminus and DnaK) facilitated efficient, qualitative identification of interfacial epitopes involved in transient PPI formation. Application of this approach can assist in elucidating interfaces of currently uncharacterised transient PPIs.

Broadband stripline Lenz lens achieves 11 × NMR signal enhancement

A Lenz lens is an electrically passive conductive element that, when placed in a time-varying magnetic field, acts as a magnetic flux concentrator or a magnetic lens. In the realm of nuclear magnetic resonance (NMR), Lenz lenses have been exploited as electrically passive metallic radiofrequency interposers placed between a sample and a tuned or untuned NMR detector in order to focus the [Formula: see text]-field of the detector onto a smaller sample space. Here we explore a novel embodiment of the Lenz lens, which acts as a non-resonant stripline interposer, i.e., the [Formula: see text]-field acts along the longitudinal volume of a sample container, such as a capillary or other microfluidic channel that is coincident with the axis of the stripline. The almost vanishing self-resonance of the stripline Lenz lens, at frequencies relevant for NMR, leads to a desirable [Formula: see text]-field amplitude that is nearly perfectly uniform across the sample and hence lacking a characteristic sinusoidal modal shape. The action of Lenz' law ensures that no stray [Formula: see text]-field is found outside of the stripline's active volume. Because the stripline Lenz lens does not rely on its own geometry to achieve resonance, its frequency response is thus widely broadband for field enhancements up to a factor of 11, with only the external driving resonator properties governing the overall resonant behaviour. We explore the use of the stripline Lenz lens with a sub-nanolitre sample volume, readily detecting 4 isotopes with resonances ranging from 125.76 to 500 MHz. The concept holds potential for the NMR study of thin films, small biological samples, as well as the in situ study of battery materials.

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.

Chemical shift transfer: an effective strategy for protein NMR assignment with ARTINA

Chemical shift transfer (CST) is a well-established technique in NMR spectroscopy that utilizes the chemical shift assignment of one protein (source) to identify chemical shifts of another (target). Given similarity between source and target systems (e.g., using homologs), CST allows the chemical shifts of the target system to be assigned using a limited amount of experimental data. In this study, we propose a deep-learning based workflow, ARTINA-CST, that automates this procedure, allowing CST to be carried out within minutes or hours of computational time and strictly without any human supervision. We characterize the efficacy of our method using three distinct synthetic and experimental datasets, demonstrating its effectiveness and robustness even when substantial differences exist between the source and target proteins. With its potential applications spanning a wide range of NMR projects, including drug discovery and protein interaction studies, ARTINA-CST is anticipated to be a valuable method that facilitates research in the field.