Molecular dynamics simulation of hen egg white lysozyme: a test of the GROMOS96 force field against nuclear magnetic resonance data

Biomolecular force fields for use in molecular dynamics (MD) simulations of proteins, DNA, or membranes are generally parametrized against ab initio quantum-chemical and experimental data for small molecules. The application of a force field in a simulation of a biomolecular system, such as a protein in solution, may then serve as a test of the quality and transferability of the force field. Here, we compare various properties obtained from two MD simulations of the protein hen egg white lysozyme (HEWL) in aqueous solution using the latest version, GROMOS96, of the GROMOS force field and an earlier version, GROMOS87+, with data derived from nuclear magnetic resonance (NMR) experiments: NOE atom-atom distance bounds, (3)J(HNalpha)-coupling constants, and backbone and side-chain order parameters. The convergence of these quantities over a 2-ns period is considered, and converged values are compared to experimental ones. The GROMOS96 simulation shows better agreement with the NMR data and also with the X-ray crystal structure of HEWL than the GROMOS87+ simulation, which was based on an earlier version of the GROMOS force field.     

Water activity coefficients in aqueous amino acid solutions by molecular dynamics simulation: 1. Force field development

New force fields for molecular dynamics (MD) simulation of aqueous zwitterionic amino acid simulations were developed. These were especially designed to calculate activity coefficient of water in amino acid solutions with high accuracy. For example, aqueous solutions of the following amino acids were considered: glycine, alanine, α-aminobutyric acid, α-aminovalerianic acid, valine and leucine. The force fields were obtained by quantum chemical calculations using B3LYP/6-31G and MP2/6-311(d,p) model theories in combination with the Merz–Kollmann–Singh scheme. To further increase the accuracy of the force field, a polarised continuum was considered in all quantum chemical calculations. Water activity coefficients obtained from MD using different all-purpose literature force fields, namely, OPLS, AMBER ff03 and GROMOS 53A6 as well as experimental data are compared with the results utilising the new force field. The new force field is shown to give better results compared with experimental data than existing force fields.     

Effects of different force fields on the structural character of α synuclein β-hairpin peptide (35–56) in aqueous environment

The hallmark of Parkinson’s disease (PD) is the intracellular protein aggregation forming Lewy Bodies (LB) and Lewy neuritis which comprise mostly of a protein, alpha synuclein (α-syn). Molecular dynamics (MD) simulation methods can augment experimental techniques to understand misfolding and aggregation pathways with atomistic resolution. The quality of MD simulations for proteins and peptides depends greatly on the accuracy of empirical force fields. The aim of this work is to investigate the effects of different force fields on the structural character of β hairpin fragment of α-syn (residues 35–56) peptide in aqueous solution. Six independent MD simulations are done in explicit solvent using, AMBER03, AMBER99SB, GROMOS96 43A1, GROMOS96 53A6, OPLS-AA, and CHARMM27 force fields with CMAP corrections. The performance of each force field is assessed from several structural parameters such as root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (Rg), solvent accessible surface area (SASA), formation of β-turn, the stability of folded β-hairpin structure, and the favourable conformations obtained for different force fields. In this study, CMAP correction of CHARMM27 force field is found to overestimate the helical conformation, while GROMOS96 53A6 is found to most successfully capture the conformational dynamics of α-syn β-hairpin fragment as elicited from NMR.     

Implementation of residue-level coarse-grained models in GENESIS for large-scale molecular dynamics simulations

Residue-level coarse-grained (CG) models have become one of the most popular tools in biomolecular simulations in the trade-off between modeling accuracy and computational efficiency. To investigate large-scale biological phenomena in molecular dynamics (MD) simulations with CG models, unified treatments of proteins and nucleic acids, as well as efficient parallel computations, are indispensable. In the GENESIS MD software, we implement several residue-level CG models, covering structure-based and context-based potentials for both well-folded biomolecules and intrinsically disordered regions. An amino acid residue in protein is represented as a single CG particle centered at the Cα atom position, while a nucleotide in RNA or DNA is modeled with three beads. Then, a single CG particle represents around ten heavy atoms in both proteins and nucleic acids. The input data in CG MD simulations are treated as GROMACS-style input files generated from a newly developed toolbox, GENESIS-CG-tool. To optimize the performance in CG MD simulations, we utilize multiple neighbor lists, each of which is attached to a different nonbonded interaction potential in the cell-linked list method. We found that random number generations for Gaussian distributions in the Langevin thermostat are one of the bottlenecks in CG MD simulations. Therefore, we parallelize the computations with message-passing-interface (MPI) to improve the performance on PC clusters or supercomputers. We simulate Herpes simplex virus (HSV) type 2 B-capsid and chromatin models containing more than 1,000 nucleosomes in GENESIS as examples of large-scale biomolecular simulations with residue-level CG models. This framework extends accessible spatial and temporal scales by multi-scale simulations to study biologically relevant phenomena, such as genome-scale chromatin folding or phase-separated membrane-less condensations.     

Structural Basis for the Site-Specific Incorporation of Lysine Derivatives into Proteins

Posttranslational modifications (PTMs) of proteins determine their structure-function relationships, interaction partners, as well as their fate in the cell and are crucial for many cellular key processes. For instance chromatin structure and hence gene expression is epigenetically regulated by acetylation or methylation of lysine residues in histones, a phenomenon known as the ‘histone code’. Recently it was shown that these lysine residues can furthermore be malonylated, succinylated, butyrylated, propionylated and crotonylated, resulting in significant alteration of gene expression patterns. However the functional implications of these PTMs, which only differ marginally in their chemical structure, is not yet understood. Therefore generation of proteins containing these modified amino acids site specifically is an important tool. In the last decade methods for the translational incorporation of non-natural amino acids using orthogonal aminoacyl-tRNA synthetase (aaRS):tRNAaaCUA pairs were developed. A number of studies show that aaRS can be evolved to use non-natural amino acids and expand the genetic code. Nevertheless the wild type pyrrolysyl-tRNA synthetase (PylRS) from Methanosarcina mazei readily accepts a number of lysine derivatives as substrates. This enzyme can further be engineered by mutagenesis to utilize a range of non-natural amino acids. Here we present structural data on the wild type enzyme in complex with adenylated ε-N-alkynyl-, ε-N-butyryl-, ε-N-crotonyl- and ε-N-propionyl-lysine providing insights into the plasticity of the PylRS active site. This shows that given certain key features in the non-natural amino acid to be incorporated, directed evolution of this enzyme is not necessary for substrate tolerance.     

ZNF385B and VEGFA Are Strongly Differentially Expressed in Serous Ovarian Carcinomas and Correlate with Survival

Background

The oncogenesis of ovarian cancer is poorly understood. The aim of this study was to identify mRNAs differentially expressed between moderately and poorly differentiated (MD/PD) serous ovarian carcinomas (SC), serous ovarian borderline tumours (SBOT) and superficial scrapings from normal ovaries (SNO), and to correlate these mRNAs with clinical parameters including survival.

Methods

Differences in mRNA expression between MD/PD SC, SBOT and SNO were analyzed by global gene expression profiling (n = 23), validated by RT-qPCR (n = 41) and correlated with clinical parameters.

Results

Thirty mRNAs differentially expressed between MD/PD SC, SBOT and SNO were selected from the global gene expression analyses, and 21 were verified (p<0.01) by RT-qPCR. Of these, 13 mRNAs were differentially expressed in MD/PD SC compared with SNO (p<0.01) and were correlated with clinical parameters. ZNF385B was downregulated (FC = −130.5, p = 1.2×10⁻⁷) and correlated with overall survival (p = 0.03). VEGFA was upregulated (FC = 6.1, p = 6.0×10⁻⁶) and correlated with progression-free survival (p = 0.037). Increased levels of TPX2 and FOXM1 mRNAs (FC = 28.5, p = 2.7×10⁻¹⁰ and FC = 46.2, p = 5.6×10⁻⁴, respectively) correlated with normalization of CA125 (p = 0.03 and p = 0.044, respectively). Furthermore, we present a molecular pathway for MD/PD SC, including VEGFA, FOXM1, TPX2, BIRC5 and TOP2A, all significantly upregulated and directly interacting with TP53.

Conclusions

We have identified 21 mRNAs differentially expressed (p<0.01) between MD/PD SC, SBOT and SNO. Thirteen were differentially expressed in MD/PD SC, including ZNF385B and VEGFA correlating with survival, and FOXM1 and TPX2 with normalization of CA125. We also present a molecular pathway for MD/PD SC.     

The Fitness Landscape of HIV-1 Gag: Advanced Modeling Approaches and Validation of Model Predictions by In Vitro Testing

Viral immune evasion by sequence variation is a major hindrance to HIV-1 vaccine design. To address this challenge, our group has developed a computational model, rooted in physics, that aims to predict the fitness landscape of HIV-1 proteins in order to design vaccine immunogens that lead to impaired viral fitness, thus blocking viable escape routes. Here, we advance the computational models to address previous limitations, and directly test model predictions against in vitro fitness measurements of HIV-1 strains containing multiple Gag mutations. We incorporated regularization into the model fitting procedure to address finite sampling. Further, we developed a model that accounts for the specific identity of mutant amino acids (Potts model), generalizing our previous approach (Ising model) that is unable to distinguish between different mutant amino acids. Gag mutation combinations (17 pairs, 1 triple and 25 single mutations within these) predicted to be either harmful to HIV-1 viability or fitness-neutral were introduced into HIV-1 NL4-3 by site-directed mutagenesis and replication capacities of these mutants were assayed in vitro. The predicted and measured fitness of the corresponding mutants for the original Ising model (r = −0.74, p = 3.6×10⁻⁶) are strongly correlated, and this was further strengthened in the regularized Ising model (r = −0.83, p = 3.7×10⁻¹²). Performance of the Potts model (r = −0.73, p = 9.7×10⁻⁹) was similar to that of the Ising model, indicating that the binary approximation is sufficient for capturing fitness effects of common mutants at sites of low amino acid diversity. However, we show that the Potts model is expected to improve predictive power for more variable proteins. Overall, our results support the ability of the computational models to robustly predict the relative fitness of mutant viral strains, and indicate the potential value of this approach for understanding viral immune evasion, and harnessing this knowledge for immunogen design.     

Molecular dynamics simulation of proteins under high pressure: Structure,function and thermodynamics

BackgroundMolecular dynamics (MD) simulation is well-recognized as a powerful tool to investigate protein structure, function, and thermodynamics. MD simulation is also used to investigate high pressure effects on proteins. For conducting better MD simulation under high pressure, the main issues to be addressed are: (i) protein force fields and water models were originally developed to reproduce experimental properties obtained at ambient pressure; and (ii) the timescale to observe the pressure effect is often much longer than that of conventional MD simulations.Scope of reviewFirst, we describe recent developments in MD simulation methodologies for studying the high-pressure structure and dynamics of protein molecules. These developments include force fields for proteins and water molecules, and enhanced simulation techniques. Then, we summarize recent studies of MD simulations of proteins in water under high pressure.Major conclusionsRecent MD simulations of proteins in solution under pressure have reproduced various phenomena identified by experiments using high pressure, such as hydration, water penetration, conformational change, helix stabilization, and molecular stiffening.General significanceMD simulations demonstrate differences in the properties of proteins and water molecules between ambient and high-pressure conditions. Comparing the results obtained by MD calculations with those obtained experimentally could reveal the mechanism by which biological molecular machines work well in collaboration with water molecules.     

Molecular Dynamics Simulations on the Coenzyme Thiamin Diphosphate in Apoenzyme Environment

Thiamin diphosphate (ThDP) is an essential cofactor for a number of enzymes, and especially involved in the nonoxidative decarboxylation of -keto acids by pyruvate decarboxylase (PDC). Recently the crystal structure of PDC bound ThDP has been determined. Based on these X-ray data MD simulations of the isolated coenzyme as well as of ThDP in its enzymatic environment were performed, using the GROMOS87 software package. For the ThDP-apoenzyme modelling all significant amino acid residues with a cut-off radius less than 8.5 Å from the cofactor were taken into account.Because the activity of the coenzyme mainly depends on the formation of a specific structure, the conformational behavior of ThDP and enzyme bound ThDP were investigated within the MD simulations in more detail. Therefore, trajectories of significant structural parameters such as the ring torsion angles _T and _P as well as essential hydrogen bonds were analyzed by our graphics tool. Moreover, Ramachandran-like plots with respect to the torsion angles _T and _P were used for the illustration of preferred orientations of the two aromatic rings in ThDP.Finally, MD simulations on ThDP analogs with less or none catalytic activity and apoenzyme mutants were included, in order to get hints of conformational effects and significant interactions in relation to cofactor-apoenzyme binding and the catalytic mechanism.Supplementary material to this paper is available in electronic form at http://dx.doi.org/10.1007/s0089460020312     

A general user interface for prediction servers of proteins' post-translational modification sites

Post-translational modifications (PTMs) of proteins play essential roles in governing the functions and dynamics of proteins and are implicated in many cellular processes. Several types of PTMs have been investigated through computational approaches, including phosphorylation, sumoylation, palmitoylation, and lysine and arginine methylation, among others. Because the large diversity in the user interfaces (UIs) of different prediction servers for PTMs could possibly hinder experimental biologists in using these servers, we propose to develop a protocol for a unified UI for PTM prediction servers, based on our own work and that of other groups on PTM site prediction. By following this protocol, tool developers can provide a uniform UI regardless of the PTM types and the underlying computational algorithms. With such uniformity in the UI, experimental biologists would be able to use any PTM prediction server compliant with this protocol once they had learned to use one of them. It takes a typical PTM prediction server compliant with this unified UI several minutes to calculate the prediction results for a protein 1,000 amino acids in length.     

Characterization of Protein Flexibility Using Small-Angle X-Ray Scattering and Amplified Collective Motion Simulations

Large-scale flexibility within a multidomain protein often plays an important role in its biological function. Despite its inherent low resolution, small-angle x-ray scattering (SAXS) is well suited to investigate protein flexibility and determine, with the help of computational modeling, what kinds of protein conformations would coexist in solution. In this article, we develop a tool that combines SAXS data with a previously developed sampling technique called amplified collective motions (ACM) to elucidate structures of highly dynamic multidomain proteins in solution. We demonstrate the use of this tool in two proteins, bacteriophage T4 lysozyme and tandem WW domains of the formin-binding protein 21. The ACM simulations can sample the conformational space of proteins much more extensively than standard molecular dynamics (MD) simulations. Therefore, conformations generated by ACM are significantly better at reproducing the SAXS data than are those from MD simulations.     

Characterization of Protein Flexibility Using Small-Angle X-Ray Scattering and Amplified Collective Motion Simulations

Large-scale flexibility within a multidomain protein often plays an important role in its biological function. Despite its inherent low resolution, small-angle x-ray scattering (SAXS) is well suited to investigate protein flexibility and determine, with the help of computational modeling, what kinds of protein conformations would coexist in solution. In this article, we develop a tool that combines SAXS data with a previously developed sampling technique called amplified collective motions (ACM) to elucidate structures of highly dynamic multidomain proteins in solution. We demonstrate the use of this tool in two proteins, bacteriophage T4 lysozyme and tandem WW domains of the formin-binding protein 21. The ACM simulations can sample the conformational space of proteins much more extensively than standard molecular dynamics (MD) simulations. Therefore, conformations generated by ACM are significantly better at reproducing the SAXS data than are those from MD simulations.     

Increasing the chemical space of proteins in living cells via genetic code expansion

In recent years it has become possible to genetically encode an expanded set of designer amino acids with tailored chemical and physical properties (dubbed unnatural amino acids, UAAs) into proteins in living cells by expanding the genetic code. Together with developments in chemistries that are amenable to and selective within physiological settings, these strategies have started to have a big impact on biological studies, as they enable exciting in cellulo applications. Here we highlight recent advances to covalently stabilize transient protein–protein interactions and capture enzyme substrate-complexes in living cells using proximity-triggered and residue-selective photo-induced crosslinking approaches. Furthermore, we describe recent efforts in controlling enzyme activity with photocaged UAAs and in extending their application to a variety of enzymatic scaffolds. In addition, we discuss the site-specific incorporation of UAAs mimicking post-translational modifications (PTMs) and approaches to generate natively-linked ubiquitin–protein conjugates to probe the role of PTMs in modulating complex cellular networks.     

Protein molecular dynamics with electrostatic force entirely determined by a single Poisson-Boltzmann calculation

The electrostatic force including the intramolecular Coulombic interactions and the electrostatic contribution of solvation effect were entirely calculated by using the finite difference Poisson-Boltzmann method (FDPB), which was incorporated into the GROMOS96 force field to complete a new finite difference stochastic dynamics procedure (FDSD). Simulations were performed on an insulin dimer. Different relative dielectric constants were successively assigned to the protein interior; a value of 17 was selected as optimal for our system. The simulation data were analyzed and compared with those obtained from 500-ps molecular dynamics (MD) simulation with explicit water and a 500-ps conventional stochastic dynamics (SD) simulation without the mean solvent force. The results indicate that the FDSD method with GROMOS96 force field is suitable to study the dynamics and structure of proteins in solution if used with the optimal protein dielectric constant.     

Molecular modelling of the mass density of single proteins

Using molecular dynamics (MD) simulations, the density of single proteins and its temperature dependence was modelled starting from the experimentally determined protein structure and a generic, transferable force field, without the need of prior parameterization. Although all proteins consist of the same 20 amino acids, their density in aqueous solution varies up to 10% and the thermal expansion coefficient up to twofold. To model the protein density, systematic MD simulations were carried out for 10 proteins with a broad range of densities (1.32–1.43?g/cm³) and molecular weights (7–97?kDa). The simulated densities deviated by less than 1.4% from their experimental values that were available for four proteins. Further analyses of protein density showed that it can be essentially described as a consequence of amino acid composition. For five proteins, the density was simulated at different temperatures. The simulated thermal expansion coefficients ranged between 4.3 and 7.1?×?10^?4?K^?1 and were similar to the experimentally determined values of ribonuclease-A and lysozyme (deviations of 2.4 and 14.6%, respectively). Further analyses indicated that the thermal expansion coefficient is linked to the temperature dependence of atomic fluctuations: proteins with a high thermal expansion coefficient show a low increase in flexibility at increasing temperature. A low increase in atomic fluctuations with temperature has been previously described as a possible mechanism of thermostability. Thus, a high thermal expansion coefficient might contribute to protein thermostability.     

Glucose oxidase from Penicillium amagasakiense: Characterization of the transition state of its denaturation from molecular dynamics simulations

Glucose oxidase (GOx) is a flavoenzyme having applications in food and medical industries. However, GOx, as many other enzymes when extracted from the cells, has relatively short operational lifetimes. Several recent studies (both experimental and theoretical), carried out on small proteins (or small fractions of large proteins), show that a detailed knowledge of how the breakdown process starts and proceeds on molecular level could be of significant help to artificially improve the stability of fragile proteins. We have performed extended molecular dynamics (MD) simulations to study the denaturation of GOx (a protein dimer containing nearly 1200 amino acids) to identify weak points in its structure and in this way gather information to later make it more stable, for example, by mutations. A denaturation of a protein can be simulated by increasing the temperature far above physiological temperature. We have performed a series of MD simulations at different temperatures (300, 400, 500, and 600 K). The exit from the protein's native state has been successfully identified with the clustering method and supported by other methods used to analyze the simulation data. A common set of amino acids is regularly found to initiate the denaturation, suggesting a moiety where the enzyme could be strengthened by a suitable amino acid based modification. Proteins 2014; 82:2353–2363. © 2014 Wiley Periodicals, Inc.     

166 Millisecond-long molecular dynamics simulations of proteins on a special-purpose machine

Molecular dynamics (MD) simulation has long been recognized as a potentially powerful tool for understanding the structural, dynamic, and functional characteristics of proteins at an atomic level of detail. Many biologically important phenomena, however, occur over timescales that have previously fallen far outside the reach of MD technology. We have constructed a specialized, massively parallel machine, called Anton, that is capable of performing all-atom simulations of proteins in an explicitly represented solvent environment at a speed roughly two orders of magnitude beyond that of the previous state of the art. Using novel algorithms developed within our lab, the machine has now simulated the behavior of a number of proteins for periods as long as two milliseconds – approximately 200 times the length of the longest such simulation previously published. Such simulations have allowed us to observe and analyze key characteristics of the dynamics of proteins (including central elements of the process of protein folding) that were previously inaccessible to both computational and experimental study.     

Molecular modelling of the mass density of single proteins

Using molecular dynamics (MD) simulations, the density of single proteins and its temperature dependence was modelled starting from the experimentally determined protein structure and a generic, transferable force field, without the need of prior parameterization. Although all proteins consist of the same 20 amino acids, their density in aqueous solution varies up to 10% and the thermal expansion coefficient up to twofold. To model the protein density, systematic MD simulations were carried out for 10 proteins with a broad range of densities (1.32-1.43 g/cm(3)) and molecular weights (7-97 kDa). The simulated densities deviated by less than 1.4% from their experimental values that were available for four proteins. Further analyses of protein density showed that it can be essentially described as a consequence of amino acid composition. For five proteins, the density was simulated at different temperatures. The simulated thermal expansion coefficients ranged between 4.3 and 7.1 × 10(-4) K(-1) and were similar to the experimentally determined values of ribonuclease-A and lysozyme (deviations of 2.4 and 14.6%, respectively). Further analyses indicated that the thermal expansion coefficient is linked to the temperature dependence of atomic fluctuations: proteins with a high thermal expansion coefficient show a low increase in flexibility at increasing temperature. A low increase in atomic fluctuations with temperature has been previously described as a possible mechanism of thermostability. Thus, a high thermal expansion coefficient might contribute to protein thermostability.     

Nanoindentation of 35 Virus Capsids in a Molecular Model: Relating Mechanical Properties to Structure

A coarse-grained model is used to study the mechanical response of 35 virus capsids of symmetries T = 1, T = 2, T = 3, pseudo T = 3, T = 4, and T = 7. The model is based on the native structure of the proteins that constitute the capsids and is described in terms of the C atoms associated with each amino acid. The number of these atoms ranges between 8 460 (for SPMV – satellite panicum mosaic virus) and 135 780 (for NBV – nudaureli virus). Nanoindentation by a broad AFM tip is modeled as compression between two planes: either both flat or one flat and one curved. Plots of the compressive force versus plate separation show a variety of behaviors, but in each case there is an elastic region which extends to a characteristic force . Crossing results in a drop in the force and irreversible damage. Across the 35 capsids studied, both and the elastic stiffness are observed to vary by a factor of 20. The changes in mechanical properties do not correlate simply with virus size or symmetry. There is a strong connection to the mean coordination number , defined as the mean number of interactions to neighboring amino acids. The Young''s modulus for thin shell capsids rises roughly quadratically with , where 6 is the minimum coordination for elastic stability in three dimensions.