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Electrostatic interactions are fundamental to biomolecular structure, stability, and function. While these interactions are traditionally modeled using fixed-charge force fields, such approaches are not transferable among di↵erent molecular environments. Polarizable force fields, such as DRUDE, address this limitation by explicitly incorpo- rating polarization e↵ect. However, their performance does not uniformly surpass that of nonpolarizable force fields, since multiple factors such as bonded terms, dihedral correction maps, and solvent screening also modulate biomolecular dynamics. In this work, we study the Im7 protein to evaluate the structural and dynamic behaviors of non-polarizable (CHARMM36m) and polarizable (DRUDE2019) force fields relative to NMR experiments. Our simulations show that DRUDE better stabilizes ↵-helices than CHARMM36m, including shorter ones that contain helix-breaking residues. However, both force fields underestimate loop dynamics, particularly in the loop I region, mainly due to restricted dihedral angle sampling. Moreover, salt bridge analysis reveals that DRUDE and CHARMM36m preferentially stabilize di↵erent salt bridges, driven by ionic interactions, charge screening by the environment, and neighboring residue flex- ibility Additionally, the latest DRUDE2019 variant, featuring updated NBFIX and NBTHOLE parameters for ion-protein interactions, demonstrated improved accuracy in modeling Na+-protein interactions. These findings are further supported by simu- lations of CBD1, a protein with a -sheet and flexible loops, which exhibited similar trends of stable structured regions and restricted loop dynamics across both force fields. These findings highlight the need to balance bonded and non-bonded interactions along with dihedral correction maps while incorporating polarization e↵ects to improve the accuracy of force fields to model protein structure and dynamics. 

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. 

An NMR NOAH-supersequence is presented consisting of five CEST experiments for studying protein backbone and side-chain dynamics by¹⁵N-CEST, carbonyl-¹³CO-CEST, aromatic-¹³C_ar-CEST,¹³C_α-CEST, and methyl-¹³C_met-CEST. 

Intrinsically disordered proteins (IDPs) are highly dynamic systems that play an important role in cell signaling processes and their misfunction often causes human disease. Proper understanding of IDP function not only requires the realistic characterization of their three-dimensional conformational ensembles at atomic-level resolution but also of the time scales of interconversion between their conformational substates. Large sets of experimental data are often used in combination with molecular modeling to restrain or bias models to improve agreement with experiment. It is shown here for the N-terminal transactivation domain of p53 (p53TAD) and Pup, which are two IDPs that fold upon binding to their targets, how the latest advancements in molecular dynamics (MD) simulations methodology produces native conformational ensembles by combining replica exchange with series of microsecond MD simulations. They closely reproduce experimental data at the global conformational ensemble level, in terms of the distribution properties of the radius of gyration tensor, and at the local level, in terms of NMR properties including 15 N spin relaxation, without the need for reweighting. Further inspection revealed that 10–20% of the individual MD trajectories display the formation of secondary structures not observed in the experimental NMR data. The IDP ensembles were analyzed by graph theory to identify dominant inter-residue contact clusters and characteristic amino-acid contact propensities. These findings indicate that modern MD force fields with residue-specific backbone potentials can produce highly realistic IDP ensembles sampling a hierarchy of nano- and picosecond time scales providing new insights into their biological function. 

An NMR supersequence is introduced for the rapid acquisition of 15 N-CEST and methyl- 13 C-CEST experiments in the same pulse sequence for applications to proteins. The high sensitivity and accuracy allows the simultaneous quantitative characterization of backbone and side-chain dynamics on the millisecond timescale ideal for routine screening for alternative protein states. 

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.