Development of P450-BM3 using molecular dynamics simulations- A tribute to the late Professor Hideaki Yamada

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Dissociation Rate Calculation via Constant-force Steered Molecular Dynamics Simulation

Steered molecular dynamics (SMD) simulations have been applied to molecular dissociation events by adding a harmonic force to molecules. Further, molecules are pulled at a constant velocity. However, instead of the constant-velocity pulling, we use a constant force: the constant-force SMD (CF-SMD) simulation. The CF-SMD simulation employs a constant force to reduce the activation barrier of molecular dissociation, thereby enhancing the dissociation event. Here, we present the capability of the CF-SMD simulation to estimate the dissociation rate at equilibrium. We performed all-atom CF-SMD simulations for NaCl and protein–ligand systems, producing dissociation rates at various forces. We extrapolated these values to the dissociation rate without a constant force using the Dudko–Hummer–Szabo model. We demonstrate that the CF-SMD simulations and the model predicted the dissociation rate in equilibrium. A CF-SMD simulation is a powerful tool for estimating the dissociation rate in a direct and computationally efficient manner

Predicting RNA Duplex Dimerization Free-Energy Changes upon Mutations Using Molecular Dynamics Simulations

The dimerization free energies of RNA–RNA duplexes are fundamental values that represent the structural stability of RNA complexes. We report a comparative analysis of RNA–RNA duplex dimerization free-energy changes upon mutations, estimated from a molecular dynamics simulation and experiments. A linear regression for nine pairs of double-stranded RNA sequences, six base pairs each, yielded a mean absolute deviation of 0.55 kcal/mol and an <i>R</i>² value of 0.97, indicating quantitative agreement between simulations and experimental data. The observed accuracy indicates that the molecular dynamics simulation with the current molecular force field is capable of estimating the thermodynamic properties of RNA molecules

Structure determination of uniformly C-13, N-15 labeled protein using qualitative distance restraints from MAS solid-state C-13-NMR observed paramagnetic relaxation enhancement

Magic angle spinning (MAS) solid-state nuclear magnetic resonance (NMR) is a powerful method for structure determination of insoluble biomolecules. However, structure determination by MAS solid-state NMR remains challenging because it is difficult to obtain a sufficient amount of distance restraints owing to spectral complexity. Collection of distance restraints from paramagnetic relaxation enhancement (PRE) is a promising approach to alleviate this barrier. However, the precision of distance restraints provided by PRE is limited in solid-state NMR because of incomplete averaged interactions and intermolecular PREs. In this report, the backbone structure of the B1 domain of streptococcal protein G (GB1) has been successfully determined by combining the CS-Rosetta protocol and qualitative PRE restraints. The derived structure has a C alpha RMSD of 1.49 angstrom relative to the X-ray structure. It is noteworthy that our protocol can determine the correct structure from only three cysteine-EDTA-Mn2+ mutants because this number of PRE sites is insufficient when using a conventional structure calculation method based on restrained molecular dynamics and simulated annealing. This study shows that qualitative PRE restraints can be employed effectively for protein structure determination from a limited conformational sampling space using a protein fragment library

Improved Accuracy in RNA–Protein Rigid Body Docking by Incorporating Force Field for Molecular Dynamics Simulation into the Scoring Function

RNA–protein interactions play fundamental roles in many biological processes. To understand these interactions, it is necessary to know the three-dimensional structures of RNA–protein complexes. However, determining the tertiary structure of these complexes is often difficult, suggesting that an accurate rigid body docking for RNA–protein complexes is needed. In general, the rigid body docking process is divided into two steps: generating candidate structures from the individual RNA and protein structures and then narrowing down the candidates. In this study, we focus on the former problem to improve the prediction accuracy in RNA–protein docking. Our method is based on the integration of physicochemical information about RNA into ZDOCK, which is known as one of the most successful computer programs for protein–protein docking. Because recent studies showed the current force field for molecular dynamics simulation of protein and nucleic acids is quite accurate, we modeled the physicochemical information about RNA by force fields such as AMBER and CHARMM. A comprehensive benchmark of RNA–protein docking, using three recently developed data sets, reveals the remarkable prediction accuracy of the proposed method compared with existing programs for docking: the highest success rate is 34.7% for the predicted structure of the RNA–protein complex with the best score and 79.2% for 3,600 predicted ones. Three full atomistic force fields for RNA (AMBER94, AMBER99, and CHARMM22) produced almost the same accurate result, which showed current force fields for nucleic acids are quite accurate. In addition, we found that the electrostatic interaction and the representation of shape complementary between protein and RNA plays the important roles for accurate prediction of the native structures of RNA–protein complexes

Free-energy calculation of ribonucleic inosines and its application to nearest-neighbor parameters

Can current simulations quantitatively predict the stability of ribonucleic acids (RNAs)? In this research, we apply a free-energy perturbation simulation of RNAs containing inosine, a modified ribonucleic base, to the derivation of RNA nearest-neighbor parameters. A parameter set derived solely from 30 simulations was used to predict the free-energy difference of the RNA duplex with a mean unbiased error of 0.70 kcal/mol, which is a level of accuracy comparable to that obtained with parameters derived from 25 experiments. We further show that the error can be lowered to 0.60 kcal/mol by combining the simulation-derived free-energy differences with experimentally measured differences. This protocol can be used as a versatile method for deriving nearest-neighbor parameters of RNAs with various modified bases

RNA nearest-neighbor parameters for inosine-cytosine pairs derived from a combined experiment-simulation approach

Recent advances in the RNA sequencing technology revealed that not only tRNAs but many mRNAs are chemically modified, e.g. deaminated, methylated or hydroxymethylated, in living cells. Furthermore, several evidences indicate that the RNA secondary structure change induced by the modification is deeply connected to the gene function. However, the structure prediction of the modified RNAs is difficult because the fundamental parameters to predict the structure are undetermined. Current RNA structure prediction model is built upon the nearest-neighbor model, which represents the score (energy) of the secondary structure as the sum of energies ("nearest-neighbor parameters") per two neighboring base pairs. These nearest-neighbor parameters are derived from the free-energy differences upon RNA duplex formation, which are typically determined from the UV adsorption measurements. Determining nearest neighbor parameters containing one modified RNA requires ca. 20 UV adsorption measurements. The cost for the synthesis of modified RNA sequences makes this approach difficult to scale with various RNA modifications.We developed a method that estimates the nearest neighbor parameters by combining the molecular dynamics (MD) simulation with the UV adsorption experiments. The free-energy differences were estimated in MD through free-energy perturbation method. The results were combined with the UV adsorption measurements to compensate the biases between the simulation and the experiment. With the new method, the required number of experiments to determine the parameters can be greatly reduced. We applied the method to estimate the nearest neighbor parameter containing inosine-cytosine pairs. The derived free-energy parameters were consistent to the parameters of canonical RNAs. Comparison to the recently reported inosine-containing nearest neighbor parameters from experiments (Wright et al., 2018) will also be discussed.Annual Meeting of the Biophysical Societ

Molecular Dynamics Simulation of the Arginine-Assisted Solubilization of Caffeic Acid: Intervention in the Interaction

We have previously demonstrated that arginine increases the solubility of aromatic compounds that have poor water solubility, an effect referred to as the “arginine-assisted solubilization system (AASS)”. In the current study, we utilized a molecular dynamics simulation to examine the solubilization effects of arginine on caffeic acid, which has a tendency to aggregate in aqueous solution. Caffeic acid has a hydrophobic moiety containing a π-conjugated system that includes an aromatic ring and a hydrophilic moiety with hydroxyl groups and a carboxyl group. While its solubility increases at higher pH values due to the acquisition of a negative charge, the solubility was greatly enhanced by the addition of 1 M arginine hydrochloride at any pH. The results of the simulation indicated that the caffeic acid aggregates were dissociated by the arginine hydrochloride, which is consistent with the experimental data. The binding free energy calculation for two caffeic acid molecules in an aqueous 1 M arginine hydrochloride solution indicated that arginine stabilized the dissociated state due to the interaction between its guanidinium group and the π-conjugated system of the caffeic acid. The binding free energy of two caffeic acid molecules in the arginine hydrochloride solution exhibited a local minimum at approximately 8 Å, at which the arginine intervened between the caffeic acid molecules, causing a stabilization of the dissociated state of caffeic acid. Such stabilization by arginine likely led to the caffeic acid solubilization, as observed in both the experiment and the MD simulation. The results reported in this paper suggest that AASS can be attributed to the stabilization resulting from the intervention of arginine in the interaction between the aromatic compounds

Molecular Dynamics Simulation of the Arginine-Assisted Solubilization of Caffeic Acid: Intervention in the Interaction

We have previously demonstrated that arginine increases the solubility of aromatic compounds that have poor water solubility, an effect referred to as the “arginine-assisted solubilization system (AASS)”. In the current study, we utilized a molecular dynamics simulation to examine the solubilization effects of arginine on caffeic acid, which has a tendency to aggregate in aqueous solution. Caffeic acid has a hydrophobic moiety containing a π-conjugated system that includes an aromatic ring and a hydrophilic moiety with hydroxyl groups and a carboxyl group. While its solubility increases at higher pH values due to the acquisition of a negative charge, the solubility was greatly enhanced by the addition of 1 M arginine hydrochloride at any pH. The results of the simulation indicated that the caffeic acid aggregates were dissociated by the arginine hydrochloride, which is consistent with the experimental data. The binding free energy calculation for two caffeic acid molecules in an aqueous 1 M arginine hydrochloride solution indicated that arginine stabilized the dissociated state due to the interaction between its guanidinium group and the π-conjugated system of the caffeic acid. The binding free energy of two caffeic acid molecules in the arginine hydrochloride solution exhibited a local minimum at approximately 8 Å, at which the arginine intervened between the caffeic acid molecules, causing a stabilization of the dissociated state of caffeic acid. Such stabilization by arginine likely led to the caffeic acid solubilization, as observed in both the experiment and the MD simulation. The results reported in this paper suggest that AASS can be attributed to the stabilization resulting from the intervention of arginine in the interaction between the aromatic compounds