Normal Molecular Dynamics Simulations Research Articles

Enhancing sampling of water rehydration upon ligand binding using variants of grand canonical Monte Carlo.

Water plays an important role in mediating protein-ligand interactions. Water rearrangement upon a ligand binding or modification can be very slow and beyond typical timescales used in molecular dynamics (MD) simulations. Thus, inadequate sampling of slow water motions in MD simulations often impairs the accuracy of the accuracy of ligand binding free energy calculations. Previous studies suggest grand canonical Monte Carlo (GCMC) outperforms normal MD simulations for water sampling, thus GCMC has been applied to help improve the accuracy of ligand binding free energy calculations. However, in prior work we observed protein and/or ligand motions impaired how well GCMC performs at water rehydration, suggesting more work is needed to improve this method to handle water sampling. In this work, we applied GCMC in 21 protein-ligand systems to assess the performance of GCMC for rehydrating buried water sites. While our results show that GCMC can rapidly rehydrate all selected water sites for most systems, it fails in five systems. In most failed systems, we observe protein/ligand motions, which occur in the absence of water, combine to close water sites and block instantaneous GCMC water insertion moves. For these five failed systems, we both extended our GCMC simulations and tested a new technique named grand canonical nonequilibrium candidate Monte Carlo (GCNCMC). GCNCMC combines GCMC with the nonequilibrium candidate Monte Carlo (NCMC) sampling technique to improve the probability of a successful water insertion/deletion. Our results show that GCNCMC and extended GCMC can rehydrate all target water sites for three of the five problematic systems and GCNCMC is more efficient than GCMC in two out of the three systems. In one system, only GCNCMC can rehydrate all target water sites, while GCMC fails. Both GCNCMC and GCMC fail in one system. This work suggests this new GCNCMC method is promising for water rehydration especially when protein/ligand motions may block water insertion/removal.

Scalable Constant pH Molecular Dynamics in GROMACS.

Molecular dynamics (MD) computer simulations are used routinely to compute atomistic trajectories of complex systems. Systems are simulated in various ensembles, depending on the experimental conditions one aims to mimic. While constant energy, temperature, volume, and pressure are rather straightforward to model, pH, which is an equally important parameter in experiments, is more difficult to account for in simulations. Although a constant pH algorithm based on the λ-dynamics approach by Brooks and co-workers [Kong, X.; Brooks III, C. L. J. Chem. Phys.1996, 105, 2414–2423] was implemented in a fork of the GROMACS molecular dynamics program, uptake has been rather limited, presumably due to the poor scaling of that code with respect to the number of titratable sites. To overcome this limitation, we implemented an alternative scheme for interpolating the Hamiltonians of the protonation states that makes the constant pH molecular dynamics simulations almost as fast as a normal MD simulation with GROMACS. In addition, we implemented a simpler scheme, called multisite representation, for modeling side chains with multiple titratable sites, such as imidazole rings. This scheme, which is based on constraining the sum of the λ-coordinates, not only reduces the complexity associated with parametrizing the intramolecular interactions between the sites but also is easily extendable to other molecules with multiple titratable sites. With the combination of a more efficient interpolation scheme and multisite representation of titratable groups, we anticipate a rapid uptake of constant pH molecular dynamics simulations within the GROMACS user community.

Best Practices in Constant pH MD Simulations: Accuracy and Sampling.

Various approaches have been proposed to include the effect of pH in molecular dynamics (MD) simulations. Among these, the λ-dynamics approach proposed by Brooks and co-workers [Kong, X.; Brooks III, C. L. J. Chem. Phys.1996, 105, 2414−2423] can be performed with little computational overhead and hfor each typeence be used to routinely perform MD simulations at microsecond time scales, as shown in the accompanying paper [Aho, N. et al. J. Chem. Theory Comput.2022, DOI: 10.1021/acs.jctc.2c00516]. At such time scales, however, the accuracy of the molecular mechanics force field and the parametrization becomes critical. Here, we address these issues and provide the community with guidelines on how to set up and perform long time scale constant pH MD simulations. We found that barriers associated with the torsions of side chains in the CHARMM36m force field are too high for reaching convergence in constant pH MD simulations on microsecond time scales. To avoid the high computational cost of extending the sampling, we propose small modifications to the force field to selectively reduce the torsional barriers. We demonstrate that with such modifications we obtain converged distributions of both protonation and torsional degrees of freedom and hence consistent pKa estimates, while the sampling of the overall configurational space accessible to proteins is unaffected as compared to normal MD simulations. We also show that the results of constant pH MD depend on the accuracy of the correction potentials. While these potentials are typically obtained by fitting a low-order polynomial to calculated free energy profiles, we find that higher order fits are essential to provide accurate and consistent results. By resolving problems in accuracy and sampling, the work described in this and the accompanying paper paves the way to the widespread application of constant pH MD beyond pKa prediction.

Effect of Ionic Strength on Ibuprofenate Adsorption on a Lipid Bilayer of Dipalmitoylphosphatidylcholine from Molecular Dynamics Simulations.

In this work, the free energy change in the process of transferring ibuprofenate from the bulk solution to the center of a model of the dipalmitoylphosphatidylcholine bilayer at different NaCl concentrations was calculated. Two minima were found in the free energy profile: a local minimum, located in the vicinity of the membrane, and the global free energy minimum, found near the headgroup region. The downward shift of free energy minima with increasing NaCl concentration is consistent with the results of previous works. Conversely, the upward shift of the free energy maximum with increasing ionic strength is due to the competition of sodium ions and lipids molecules to coordinate with ibuprofenate and neutralize its charge. In addition, normal molecular dynamics simulations were performed to study the effects of the ibuprofenate on the lipid bilayer and in the presence of a high ibuprofenate concentration. The effect of ionic strength on the properties of the lipid bilayer and on lipid-drug interactions was analyzed. The area per lipid shrinking with increasing ionic strength, volume of lipids, and thickness of the bilayer is consistent with the experimental results. At a very high ibuprofenate concentration, the lipid bilayer dehydrates, and it consequently transforms into the gel phase, thus blocking the permeation.

Accurate Structure Prediction for Protein Loops Based on Molecular Dynamics Simulations with RSFF2C.

Protein loops, connecting the α-helices and β-strands, are involved in many important biological processes. However, due to their conformational flexibility, it is still challenging to accurately determine three-dimensional (3D) structures of long loops experimentally and computationally. Herein, we present a systematic study of the protein loop structure prediction via a total of ∼850 μs molecular dynamics (MD) simulations. For a set of 15 long (10-16 residues) and solvent-exposed loops, we first evaluated the performance of four state-of-the-art loop modeling algorithms, DaReUS-Loop, Sphinx, Rosetta-NGK, and MODELLER, on each loop, and none of them could accurately predict the structures for most loops. Then, temperature replica exchange molecular dynamics (REMD) simulations were conducted with three recent force fields, RSFF2C with TIP3P water model, CHARMM36m with CHARMM-modified TIP3P, and AMBER ff19SB with OPC. We found that our recently developed residue-specific force field RSFF2C performed the best and successfully predicted 12 out of 15 loops with a root-mean-square deviation (RMSD) < 1.5 Å. As an alternative with lower computational cost, normal MD simulations at high temperatures (380, 500, and 620 K) were investigated. Temperature-dependent performance was observed for each force field, and, for RSFF2C+TIP3P, we found that three independent 100-ns MD simulations at 500 K gave comparable results with REMD simulations. These results suggest that MD simulations, especially with enhanced sampling techniques such as replica exchange, with the RSFF2C force field could be useful for accurate loop structure prediction.

Validation of Molecular Dynamics Simulations for Prediction of Three-Dimensional Structures of Small Proteins

Although various higher-order protein structure prediction methods have been developed, almost all of them were developed based on the three-dimensional (3D) structure information of known proteins. Here we predicted the short protein structures by molecular dynamics (MD) simulations in which only Newton’s equations of motion were used and 3D structural information of known proteins was not required. To evaluate the ability of MD simulationto predict protein structures, we calculated seven short test protein (10–46 residues) in the denatured state and compared their predicted and experimental structures. The predicted structure for Trp-cage (20 residues) was close to the experimental structure by 200-ns MD simulation. For proteins shorter or longer than Trp-cage, root-mean square deviation values were larger than those for Trp-cage. However, secondary structures could be reproduced by MD simulations for proteins with 10–34 residues. Simulations by replica exchange MD were performed, but the results were similar to those from normal MD simulations. These results suggest that normal MD simulations can roughly predict short protein structures and 200-ns simulations are frequently sufficient for estimating the secondary structures of protein (approximately 20 residues). Structural prediction method using only fundamental physical laws are useful for investigating non-natural proteins, such as primitive proteins and artificial proteins for peptide-based drug delivery systems.

Enhanced conformational sampling method for proteins based on the TaBoo SeArch algorithm: application to the folding of a mini-protein, chignolin.

The conformational samplings are indispensible for obtaining reliable canonical ensembles, which provide statistical averages of physical quantities such as free energies. However, the samplings of vast conformational space of biomacromolecules by conventional molecular dynamics (MD) simulations might be insufficient, due to their inadequate accessible time-scales for investigating biological functions. Therefore, the development of methodologies for enhancing the conformational sampling of biomacromolecules still remains as a challenging issue in computational biology. To tackle this problem, we newly propose an efficient conformational search method, which is referred as TaBoo SeArch (TBSA) algorithm. In TBSA, an inverse energy histogram is used to select seeds for the conformational resampling so that states with high frequencies are inhibited, while states with low frequencies are efficiently sampled to explore the unvisited conformational space. As a demonstration, TBSA was applied to the folding of a mini-protein, chignolin, and automatically sampled the native structure (Cα root mean square deviation < 1.0 Å) with nanosecond order computational costs started from a completely extended structure, although a long-time 1-µs normal MD simulation failed to sample the native structure. Furthermore, a multiscale free energy landscape method based on the conformational sampling of TBSA were quantitatively evaluated through free energy calculations with both implicit and explicit solvent models, which enable us to find several metastable states on the folding landscape.

Accelerated Molecular Dynamics Study of Z/E Isomerization of Azobenzene: Kramers’ Theory Validation

The reaction rate in a condensed phase is retarded by solvent friction and deviates from the transition state theory (TST) in a highly viscous solvent. The Z/E isomerization kinetics of 4-dimethylamino-4′-nitroazobenzene in solution have been experimentally analyzed in a wide range of pressure in our previous studies. Herein the non-TST behaviors of the rate constant were theoretically modelled by means of Kramers’ theory and its extensions and by molecular dynamics (MD) simulations. The Z/E isomerization takes place in milliseconds or longer and so is not reachable by normal MD simulations. The accelerated MD method was employed to simulate the rare events using a biased isomerization potential barrier.

Exploring the Mechanism of Zanamivir Resistance in a Neuraminidase Mutant: A Molecular Dynamics Study

It is critical to understand the molecular basis of the drug resistance of influenza viruses to efficiently treat this infectious disease. Recently, H1N1 strains of influenza A carrying a mutation of Q136K in neuraminidase were found. The new strain showed a strong Zanamivir neutralization effect. In this study, normal molecular dynamics simulations and metadynamics simulations were employed to explore the mechanism of Zanamivir resistance. The wild-type neuraminidase contained a 310 helix before the 150 loop, and there was interaction between the 150 and 430 loops. However, the helix and the interaction between the two loops were disturbed in the mutant protein due to interaction between K136 and nearby residues. Hydrogen-bond network analysis showed weakened interaction between the Zanamivir drug and E276/D151 on account of the electrostatic interaction between K136 and D151. Metadynamics simulations showed that the free energy landscape was different in the mutant than in the wild-type neuraminidase. Conformation with the global minimum of free energy for the mutant protein was different from the wild-type conformation. While the drug fit completely into the active site of the wild-type neuraminidase, it did not match the active site of the mutant variant. This study indicates that the altered hydrogen-bond network and the deformation of the 150 loop are the key factors in development of Zanamivir resistance. Furthermore, the Q136K mutation has a variable effect on conformation of different N1 variants, with conformation of the 1918 N1 variant being more profoundly affected than that of the other N1 variants studied in this paper. This observation warrants further experimental investigation.

Comparison of molecular dynamics simulation methods for amyloid β 1–42 monomers containing d-aspartic acid residues for predicting retention times in chromatography

Molecular dynamics simulations of amyloid β 1–42 containing d-aspartic acid residues were performed using several continuous solvent models to investigate the usefulness of simulation methods for d-amino acid-containing proteins and peptides. Normal molecular dynamics simulations and replica exchange molecular dynamics simulations, which are one of the generalized-ensemble algorithms, were performed. Because the β-structure contents of amyloid β 1–42 peptides obtained by replica exchange molecular dynamics simulations with Onufriev–Bashford–Case generalized Born implicit solvent were qualitatively consistent with experimental data, replica exchange molecular dynamics rather than other methods appeared to be more reasonable for calculations of amyloid β 1–42 containing d-aspartic acid residues. Computational results revealed that peptides with stereoinversion of Asp23 tend to form β-sheet structures by themselves, in contrast to the wild-type peptides that form β-sheet structures only after aggregation. These results are expected to be useful for computational investigations of proteins and peptides such as prediction of retention time of peptides and proteins containing d-aspartic acid residues.

Accelerated molecular dynamics: a promising and efficient simulation method for biomolecules.

Many interesting dynamic properties of biological molecules cannot be simulated directly using molecular dynamics because of nanosecond time scale limitations. These systems are trapped in potential energy minima with high free energy barriers for large numbers of computational steps. The dynamic evolution of many molecular systems occurs through a series of rare events as the system moves from one potential energy basin to another. Therefore, we have proposed a robust bias potential function that can be used in an efficient accelerated molecular dynamics approach to simulate the transition of high energy barriers without any advance knowledge of the location of either the potential energy wells or saddle points. In this method, the potential energy landscape is altered by adding a bias potential to the true potential such that the escape rates from potential wells are enhanced, which accelerates and extends the time scale in molecular dynamics simulations. Our definition of the bias potential echoes the underlying shape of the potential energy landscape on the modified surface, thus allowing for the potential energy minima to be well defined, and hence properly sampled during the simulation. We have shown that our approach, which can be extended to biomolecules, samples the conformational space more efficiently than normal molecular dynamics simulations, and converges to the correct canonical distribution.