Protocol To Make Protein NMR Structures Amenable to Stable Long Time Scale Molecular Dynamics Simulations

Protective effects of a small molecule inhibitor ligand against hyperphosphorylated tau-induced mitochondrial and synaptic toxicities in Alzheimer disease

The purpose of our study is to understand the protective effects of small molecule ligands for phosphorylated tau (p-tau) in Alzheimer's disease (ad) progression. Many reports show evidence that p-tau is reported to be an important contributor to the formation of paired helical filaments (PHFs) and neurofibrillary tangles (NFTs) in ad neurons. In ad, glycogen synthase kinase-3 beta (GSK3β), cyclin-dependent kinase- 5 (CDK5) and dual specificity tyrosine-phosphorylation-regulated kinase 1A (DYRK1A), are the three important kinases responsible for tau hyperphosphorylation. Currently, there are no drugs and/or small molecules that reduce the toxicity of p-tau in ad. In the present study, we rationally selected and validated small molecule ligands that binds to the phosphorylated tau at SER23 (Ser 285). We also assessed the molecular dynamics and validated molecular docking sites for the three best ligands. Based on the best docking scores -8.09, -7.9 and - 7.8 kcal/mol, we found that ligand 1 binds to key hyperphosphorylation residues of p-tau that inhibit abnormal PHF-tau, DYRK1A, and GKS3β that reduce p-tau levels in ad. Using biochemical, molecular, immunoblotting, immunofluorescence, and transmission electron microscopy analyses, we studied the ligand 1 inhibition as well as mitochondrial and synaptic protective effects in immortalized primary hippocampal neuronal (HT22) cells. We found interactions between NAT10-262501 (ligand 1) and p-tau at key phosphorylation sites and these ligand-based inhibitions decreased PHF-tau, DYRK1A and GSK3β levels. We also found increased mitochondrial biogenesis, mitochondrial fusion and synaptic activities and reduced mitochondrial fission in ligand 1-treated mutant tau HT22 cells. Based on these results, we cautiously conclude that p-tau NAT10-262501 (ligand 1) reduces hyperphosphorylation of tau based GKS3β and CDK5 kinase regulation in ad, and aids in the maintenance of neuronal structure, mitochondrial dynamics, and biogenesis with a possible therapeutic drug target for ad.

Patterns in Protein Flexibility: A Comparison of NMR "Ensembles", MD Trajectories, and Crystallographic B-Factors

Proteins are molecular machines requiring flexibility to function. Crystallographic B-factors and Molecular Dynamics (MD) simulations both provide insights into protein flexibility on an atomic scale. Nuclear Magnetic Resonance (NMR) lacks a universally accepted analog of the B-factor. However, a lack of convergence in atomic coordinates in an NMR-based structure calculation also suggests atomic mobility. This paper describes a pattern in the coordinate uncertainties of backbone heavy atoms in NMR-derived structural “ensembles” first noted in the development of FindCore2 (previously called Expanded FindCore: DA Snyder, J Grullon, YJ Huang, R Tejero, GT Montelione, Proteins: Structure, Function, and Bioinformatics 82 (S2), 219–230) and demonstrates that this pattern exists in coordinate variances across MD trajectories but not in crystallographic B-factors. This either suggests that MD trajectories and NMR “ensembles” capture motional behavior of peptide bond units not captured by B-factors or indicates a deficiency common to force fields used in both NMR and MD calculations.

Hybrid methods for combined experimental and computational determination of protein structure

Knowledge of protein structure is paramount to the understanding of biological function, developing new therapeutics, and making detailed mechanistic hypotheses. Therefore, methods to accurately elucidate three-dimensional structures of proteins are in high demand. While there are a few experimental techniques that can routinely provide high-resolution structures, such as x-ray crystallography, nuclear magnetic resonance (NMR), and cryo-EM, which have been developed to determine the structures of proteins, these techniques each have shortcomings and thus cannot be used in all cases. However, additionally, a large number of experimental techniques that provide some structural information, but not enough to assign atomic positions with high certainty have been developed. These methods offer sparse experimental data, which can also be noisy and inaccurate in some instances. In cases where it is not possible to determine the structure of a protein experimentally, computational structure prediction methods can be used as an alternative. Although computational methods can be performed without any experimental data in a large number of studies, inclusion of sparse experimental data into these prediction methods has yielded significant improvement. In this Perspective, we cover many of the successes of integrative modeling, computational modeling with experimental data, specifically for protein folding, protein-protein docking, and molecular dynamics simulations. We describe methods that incorporate sparse data from cryo-EM, NMR, mass spectrometry, electron paramagnetic resonance, small-angle x-ray scattering, Förster resonance energy transfer, and genetic sequence covariation. Finally, we highlight some of the major challenges in the field as well as possible future directions.

How to understand atomistic molecular dynamics simulations of RNA and protein-RNA complexes?

We provide a critical assessment of explicit-solvent atomistic molecular dynamics (MD) simulations of RNA and protein/RNA complexes, written primarily for non-specialists with an emphasis to explain the limitations of MD. MD simulations can be likened to hypothetical single-molecule experiments starting from single atomistic conformations and investigating genuine thermal sampling of the biomolecules. The main advantage of MD is the unlimited temporal and spatial resolution of positions of all atoms in the simulated systems. Fundamental limitations are the short physical time-scale of simulations, which can be partially alleviated by enhanced-sampling techniques, and the highly approximate atomistic force fields describing the simulated molecules. The applicability and present limitations of MD are demonstrated on studies of tetranucleotides, tetraloops, ribozymes, riboswitches and protein/RNA complexes. Wisely applied simulations respecting the approximations of the model can successfully complement structural and biochemical experiments. WIREs RNA 2017, 8:e1405. doi: 10.1002/wrna.1405 For further resources related to this article, please visit the WIREs website.

Synergy between NMR measurements and MD simulations of protein/RNA complexes: application to the RRMs, the most common RNA recognition motifs

RNA recognition motif (RRM) proteins represent an abundant class of proteins playing key roles in RNA biology. We present a joint atomistic molecular dynamics (MD) and experimental study of two RRM-containing proteins bound with their single-stranded target RNAs, namely the Fox-1 and SRSF1 complexes. The simulations are used in conjunction with NMR spectroscopy to interpret and expand the available structural data. We accumulate more than 50 μs of simulations and show that the MD method is robust enough to reliably describe the structural dynamics of the RRM-RNA complexes. The simulations predict unanticipated specific participation of Arg142 at the protein-RNA interface of the SRFS1 complex, which is subsequently confirmed by NMR and ITC measurements. Several segments of the protein-RNA interface may involve competition between dynamical local substates rather than firmly formed interactions, which is indirectly consistent with the primary NMR data. We demonstrate that the simulations can be used to interpret the NMR atomistic models and can provide qualified predictions. Finally, we propose a protocol for 'MD-adapted structure ensemble' as a way to integrate the simulation predictions and expand upon the deposited NMR structures. Unbiased μs-scale atomistic MD could become a technique routinely complementing the NMR measurements of protein-RNA complexes.