Molecular dynamics studies of NMR relaxation in proteins

Biomolecular NMR spectroscopy in the era of artificial intelligence

Biomolecular nuclear magnetic resonance (NMR) spectroscopy and artificial intelligence (AI) have a burgeoning synergy. Deep learning-based structural predictors have forever changed structural biology, yet these tools currently face limitations in accurately characterizing protein dynamics, allostery, and conformational heterogeneity. We begin by highlighting the unique abilities of biomolecular NMR spectroscopy to complement AI-based structural predictions toward addressing these knowledge gaps. We then highlight the direct integration of deep learning approaches into biomolecular NMR methods. AI-based tools can dramatically improve the acquisition and analysis of NMR spectra, enhancing the accuracy and reliability of NMR measurements, thus streamlining experimental processes. Additionally, deep learning enables the development of novel types of NMR experiments that were previously unattainable, expanding the scope and potential of biomolecular NMR spectroscopy. Ultimately, a combination of AI and NMR promises to further revolutionize structural biology on several levels, advance our understanding of complex biomolecular systems, and accelerate drug discovery efforts.

NMR Provides Unique Insight into the Functional Dynamics and Interactions of Intrinsically Disordered Proteins

Intrinsically disordered proteins are ubiquitous throughout all known proteomes, playing essential roles in all aspects of cellular and extracellular biochemistry. To understand their function, it is necessary to determine their structural and dynamic behavior and to describe the physical chemistry of their interaction trajectories. Nuclear magnetic resonance is perfectly adapted to this task, providing ensemble averaged structural and dynamic parameters that report on each assigned resonance in the molecule, unveiling otherwise inaccessible insight into the reaction kinetics and thermodynamics that are essential for function. In this review, we describe recent applications of NMR-based approaches to understanding the conformational energy landscape, the nature and time scales of local and long-range dynamics and how they depend on the environment, even in the cell. Finally, we illustrate the ability of NMR to uncover the mechanistic basis of functional disordered molecular assemblies that are important for human health.

Analytical Description of NMR Relaxation Highlights Correlated Dynamics in Intrinsically Disordered Proteins

The dynamic fluctuations of intrinsically disordered proteins (IDPs) define their function. Although experimental nuclear magnetic resonance (NMR) relaxation reveals the motional complexity of these highly flexible proteins, the absence of physical models describing IDP dynamics hinders their mechanistic interpretation. Combining molecular dynamics simulation and NMR, we introduce a framework in which distinct motions are attributed to local libration, backbone dihedral angle dynamics and longer-range tumbling of one or more peptide planes. This model provides unique insight into segmental organization of dynamics in IDPs and allows us to investigate the presence and extent of the correlated motions that are essential for function.

Atomic-Resolution Structural Dynamics in Crystalline Proteins from NMR and Molecular Simulation

Solid-state NMR can provide atomic-resolution information about protein motions occurring on a vast range of time scales under similar conditions to those of X-ray diffraction studies and therefore offers a highly complementary approach to characterizing the dynamic fluctuations occurring in the crystal. We compare experimentally determined dynamic parameters, spin relaxation, chemical shifts, and dipolar couplings, to values calculated from a 200 ns MD simulation of protein GB1 in its crystalline form, providing insight into the nature of structural dynamics occurring within the crystalline lattice. This simulation allows us to test the accuracy of commonly applied procedures for the interpretation of experimental solid-state relaxation data in terms of dynamic modes and time scales. We discover that the potential complexity of relaxation-active motion can lead to significant under- or overestimation of dynamic amplitudes if different components are not taken into consideration.

Relaxation dynamics of nucleosomal DNA

Recent experimental and theoretical evidence demonstrates that proteins and water in the hydration layer can follow complex stretched exponential or power law relaxation dynamics. Here, we report on a 50 ns all atom molecular dynamics (MD) simulation of the yeast nucleosome, where the interactions between DNA, histones, surrounding water and ions are explicitly included. DNA interacts with the histone core in 14 locations, approximately every 10.4 base pairs. We demonstrate that all sites of interaction exhibit anomalously slow power law relaxation, extending up to 10 ns, while fast exponential relaxation dynamics of hundreds of picoseconds applies to DNA regions outside these locations. The appearance of 1/f(alpha) noise or pink noise in DNA dynamics is ubiquitous. For histone-bound nucleotide dynamics alpha --> 1 and is a signature of complexity of the protein-DNA interactions. For control purposes two additional DNA simulations free of protein are conducted. Both utilize the same sequence of DNA, as found the in the nucleosome. In one simulation the initial conformation of the double helix is a straight B-form. In the other, the initial conformation is super helical. Neither of these simulations exhibits the variation of alpha as a function of position, the measure of power law for dynamical behavior, which we observe in the nucleosome simulation. The unique correspondence (high alpha to DNA-histone interaction sites, low alpha to free DNA sites), suggests that alpha may be an important and new quantification of protein-DNA interactions for future experiments.

Molecular dynamics of myoglobin at 298 degrees K. Results from a 300-ps computer simulation

We have carried out a very long (300 ps) molecular dynamics simulation of the protein myoglobin. This trajectory is approximately three times longer than the longest previous molecular dynamics simulation of a protein, and ten times longer than protein simulations of comparable size (1,423 atoms in our model). Here we report results from this long simulation concerning the average structure, the mean square fluctuations of atoms about the average structure, and the nuclear magnetic resonance order parameters for various groups in myoglobin. The results demonstrate that the average coordinates change very slowly during the simulation. The relative atomic mobilities are well described by the simulation. For both the mean square atomic fluctuations and the order parameters, however, there are significant quantitative differences when values calculated using shorter portions of the trajectory are compared with results obtained for the entire 300-ps simulation. The implications of this result for obtaining converged properties from protein molecular dynamics simulations for comparison with experiment are discussed.

Dipolar NMR relaxation of nonprotonated aromatic carbons in proteins. Structural and dynamical effects

The crystal structure and a 96-ps molecular dynamics simulation used to analyze structural and motional contributions to spin-lattice (T1) relaxation times of phenylalanine and tyrosine C gamma carbons of the pancreatic trypsin inhibitor. The H beta and H delta protons geminal to C gamma are calculated to account for approximately 80% of the dipolar relaxation for each residue. Experimental T1 values for the phenylalanine residues obtained at 25 MHz are observed to be 15-25% longer than estimates based on the rigid crystal structure. It is shown how an increase in T1 can be related to order parameters for the picosecond motional averaging of the important C,H dipolar interactions, and how these order parameters can be calculated from a protein molecular dynamics trajectory.