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The many emerging applications of nanoparticles in diverse fields in chemistry and biology require the characterization of interactions between nanoparticles and surrounding biomolecules, such as proteins. Nuclear magnetic resonance (NMR) spin relaxation of proteins, highly sensitive to interactions with nanoparticles, contains rich information about protein mobility and binding kinetics. The interactions of globular proteins with silica nanoparticles differ markedly from those with liposome nanoparticles, although both are driven by electrostatic forces. For unmodified silica nanoparticles, their interactions with an internally rigid protein like ubiquitin uniformly increases the backbone amide 15N transverse R2 relaxation for most residues. In contrast, for ubiquitin-POPG liposome interactions, their characteristic transverse R2 profiles indicate that ubiquitin undergoes diffusive rotational motions on the liposome surface. Here, we show that coating silica nanoparticles with sulfobetaine siloxane (SBS) zwitterionic molecules profoundly alters their interactions with proteins in a manner that closely resembles the interaction mode observed with liposomes. 15N-R2 relaxation reveals that ubiquitin and the Ras-binding domain (RBD) of B-Raf both exhibit axial reorientational motions about an axis perpendicular to the nanoparticle surface in the bound state, where the interactions involve the predominantly positively charged surface regions. These findings point toward a global dynamics mechanism of proteins when interacting with organic or inorganic nanoparticles with densely charged soft surfaces. This information may help tailor the coatings of nanoparticles to adopt specific modes of interaction with proteins that can be used to control their function in vivo and in vitro. 

Abstract Despite the prominent role of the K-Ras protein in many different types of human cancer, major gaps in atomic-level information severely limit our understanding of its functions in health and disease. Here, we report the quantitative backbone structural dynamics of K-Ras by solution nuclear magnetic resonance spectroscopy of the active state of wild-type K-Ras bound to guanosine triphosphate (GTP) nucleotide and two of its oncogenic P-loop mutants, G12D and G12C, using a new nanoparticle-assisted spin relaxation method, relaxation dispersion and chemical exchange saturation transfer experiments covering the entire range of timescales from picoseconds to milliseconds. Our combined experiments allow detection and analysis of the functionally critical Switch I and Switch II regions, which have previously remained largely unobservable by X-ray crystallography and nuclear magnetic resonance spectroscopy. Our data reveal cooperative transitions of K-Ras·GTP to a highly dynamic excited state that closely resembles the partially disordered K-Ras·GDP state. These results advance our understanding of differential GTPase activities and signaling properties of the wild type versus mutants and may thus guide new strategies for the development of therapeutics. 

Abstract Rapid progress in machine learning offers new opportunities for the automated analysis of multidimensional NMR spectra ranging from protein NMR to metabolomics applications. Most recently, it has been demonstrated how deep neural networks (DNN) designed for spectral peak picking are capable of deconvoluting highly crowded NMR spectra rivaling the facilities of human experts. Superior DNN-based peak picking is one of a series of critical steps during NMR spectral processing, analysis, and interpretation where machine learning is expected to have a major impact. In this perspective, we lay out some of the unique strengths as well as challenges of machine learning approaches in this new era of automated NMR spectral analysis. Such a discussion seems timely and should help define common goals for the NMR community, the sharing of software tools, standardization of protocols, and calibrate expectations. It will also help prepare for an NMR future where machine learning and artificial intelligence tools will be common place. 

Protein function depends critically on intrinsic internal dynamics, which is manifested in distinct ways, such as loop motions that regulate protein recognition and catalysis. Under physiological conditions, dynamic processes occur on a wide range of time scales from subpicoseconds to seconds. Commonly used NMR spin relaxation in solution provides valuable information on very fast and slow motions but is insensitive to the intermediate nanosecond to microsecond range that exceeds the protein tumbling correlation time. Presently, very little is known about the nature and functional role of these motions. It is demonstrated here how transverse spin relaxation becomes exquisitely sensitive to these motions at atomic resolution when studying proteins in the presence of nanoparticles. Application of this novel cross-disciplinary approach reveals large-scale dynamics of loops involved in functionally critical protein-protein interactions and protein-calcium ion recognition that were previously unobservable.