Solid-state NMR studies of amyloids

Selective correlations between aliphatic 13C nuclei in protein solid-state NMR

Solid-state nuclear magnetic resonance (NMR) is a potent tool for studying the structures and dynamics of insoluble proteins. It starts with signal assignment through multi-dimensional correlation experiments, where the aliphatic 13Cα-13Cβ correlation is indispensable for identifying specific residues. However, developing efficient methods for achieving this correlation is a challenge in solid-state NMR. We present a simple band-selective zero-quantum (ZQ) recoupling method, named POST-C4161 (PC4), which enhances 13Cα-13Cβ correlations under moderate magic-angle spinning (MAS) conditions. PC4 requires minimal 13C radio-frequency (RF) field and proton decoupling, exhibits high stability against RF variations, and achieves superior efficiency. Comparative tests on various samples, including the formyl-Met-Leu-Phe (fMLF) tripeptide, microcrystalline β1 immunoglobulin binding domain of protein G (GB1), and membrane protein of mechanosensitive channel of large conductance from Methanosarcina acetivorans (MaMscL), demonstrate that PC4 selectively enhances 13Cα-13Cβ correlations by up to 50 % while suppressing unwanted correlations, as compared to the popular dipolar-assisted rotational resonance (DARR). It has addressed the long-standing need for selective 13C-13C correlation methods. We anticipate that this simple but efficient PC4 method will have immediate applications in structural biology by solid-state NMR.

Exploring the Higher Order Structure and Conformational Transitions in Insulin Microcrystalline Biopharmaceuticals by Proton-Detected Solid-State Nuclear Magnetic Resonance at Natural Abundance

The integrity of a higher order structure (HOS) is an essential requirement to ensure the efficacy, stability, and safety of protein therapeutics. Solution-state nuclear magnetic resonance (NMR) occupies a unique niche as one of the most promising methods to access atomic-level structural information on soluble biopharmaceutical formulations. Another major class of drugs is poorly soluble, such as microcrystalline suspensions, which poses significant challenges for the characterization of the active ingredient in its native state. Here, we have demonstrated a solid-state NMR method for HOS characterization of biopharmaceutical suspensions employing a selective excitation scheme under fast magic angle spinning (MAS). The applicability of the method is shown on commercial insulin suspensions at natural isotopic abundance. Selective excitation aided with proton detection and non-uniform sampling (NUS) provides improved sensitivity and resolution. The enhanced resolution enabled us to demonstrate the first experimental evidence of a phenol-escaping pathway in insulin, leading to conformational transitions to different hexameric states. This approach has the potential to serve as a valuable means for meticulously examining microcrystalline biopharmaceutical suspensions, which was previously not attainable in their native formulation states and can be seamlessly extended to other classes of biopharmaceuticals such as mAbs and other microcrystalline proteins.

Recent Advances in NMR Protein Structure Prediction with ROSETTA

Nuclear magnetic resonance (NMR) spectroscopy is a powerful method for studying the structure and dynamics of proteins in their native state. For high-resolution NMR structure determination, the collection of a rich restraint dataset is necessary. This can be difficult to achieve for proteins with high molecular weight or a complex architecture. Computational modeling techniques can complement sparse NMR datasets (<1 restraint per residue) with additional structural information to elucidate protein structures in these difficult cases. The Rosetta software for protein structure modeling and design is used by structural biologists for structure determination tasks in which limited experimental data is available. This review gives an overview of the computational protocols available in the Rosetta framework for modeling protein structures from NMR data. We explain the computational algorithms used for the integration of different NMR data types in Rosetta. We also highlight new developments, including modeling tools for data from paramagnetic NMR and hydrogen-deuterium exchange, as well as chemical shifts in CS-Rosetta. Furthermore, strategies are discussed to complement and improve structure predictions made by the current state-of-the-art AlphaFold2 program using NMR-guided Rosetta modeling.