Cryo_fit: Democratization of flexible fitting for cryo-EM

Calcium-dependent lectin I from Pseudomonas aeruginosa (PA-IL) binds specifically to oligosaccharides presenting an alpha-galactose residue at their nonreducing end, such as the disaccharides alphaGal1-2betaGalOMe, alphaGal1-3betaGalOMe, and alphaGal1-4betaGalOMe. This provides a unique model for studying the effect of the glycosidic linkage of the ligands on structure and thermodynamics of the complexes by means of experimental and theoretical tools. The structural features of PA-IL in complex with the three disaccharides were established by docking and molecular dynamics simulations and compared with those observed in available crystal structures, including PA-IL.alphaGal1-2betaGalOMe complex, which was solved at 2.4 A resolution and reported herein. The role of a structural bridge water molecule in the binding site of PA-IL was also elucidated through molecular dynamics simulations and free energy calculations. This water molecule establishes three very stable hydrogen bonds with O6 of nonreducing galactose, oxygen from Pro-51 main chain, and nitrogen from Gln-53 main chain of the lectin binding site. Binding free energies for PA-IL in complex with the three disaccharides were investigated, and the results were compared with the experimental data determined by titration microcalorimetry. When the bridge water molecule was included in the free energy calculations, the simulations predicted the correct binding affinity trends with the 1-2-linked disaccharide presenting three times stronger affinity ligand than the other two. These results highlight the role of the water molecule in the binding site of PA-IL and indicate that it should be taken into account when designing glycoderivatives active against P. aeruginosa adhesion.

In recent years, short peptide self-assembled materials, prepared under the control of the thermolysin catalyst, have been investigated extensively and shown to acquire various morphologies and fun...

The term molecular mechanics (MM) refers to the use of simple potential-energy functions (e.g., harmonic oscillator or Coulombic potentials) to model molecular systems. Molecular mechanics approaches are widely applied in molecular structure refinement, molecular dynamics (MD) simulations, Monte Carlo (MC) simulations, and ligand-docking simulations. Typically, molecular mechanics models consist of spherical atoms connected by springs which represent bonds. Internal forces experienced in the model structure are described using simple mathematical functions. For example, Hooke’s law is commonly used to describe bonded interactions, and the nonbonded atoms might be treated as inelastic hard spheres or may interact according to a Lennard-Jones potential. Using these simple models, a molecular dynamics simulation numerically solves Newton’s equations of motion, thus allowing structural fluctuations to be observed with respect to time. Dynamic simulation methods are widely used to obtain information on the time evolution of conformations of proteins and other biological macromolecules1–4 and also kinetic and thermodynamic information. Simulations can provide fine detail concerning the motions of individual particles as a function of time. They can be utilized to quantify the properties of a system at a precision and on a time scale that is otherwise inaccessible, and simulation is, therefore, a valuable tool in extending our understanding of model systems. Theoretical consideration of a system additionally allows one to investigate the specific contributions to a property through “computational alchemy”,5 that is, modifying the simulation in a way that is nonphysical but nonetheless allows a model’s characteristics to be probed. One particular example is the artificial conversion of the energy function from that representing one system to that of another during a simulation. This is an important technique in free-energy calculations.6 Thus, molecular dynamics simulations, along with a range of complementary computational approaches, have become valuable tools for investigating the basis of protein structure and function. This review offers an outline of the origin of molecular dynamics simulation for protein systems and how it has developed into a robust and trusted tool. This review then covers more recent advances in theory and an illustrative selection of practical studies in which it played a central role. The range of studies in which MD has played a considerable or pivotal role is immense, and this review cannot do justice to them; MD simulations of biomedical importance were recently reviewed.4 Particular emphasis will be placed on the study of dynamic aspects of protein recognition, an area where molecular dynamics has scope to provide broad and far-ranging insights. This review concludes with a brief discussion of the future potential offered to advancement of the biological and biochemical sciences and the remaining issues that must be overcome to allow the full extent of this potential to be realized. 1.1. Historical Background MD methods were originally conceived within the theoretical physics community during the 1950s. In 1957, Alder and Wainwright7 performed the earliest MD simulation using the so-called hard-sphere model, in which the atoms interacted only through perfect collisions. Rahman8 subsequently applied a smooth, continuous potential to mimic real atomic interactions. During the 1970s, as computers became more widespread, MD simulations were developed for more complex systems, culminating in 1976 with the first simulation of a protein9,10 using an empirical energy function constructed using physics-based first-principles assumptions. MD simulations are now widely and routinely applied and especially popular in the fields of materials science11,12 and biophysics. As will be discussed later in this review, a variety of experimental conditions may be simulated with modern theories and algorithms. The initial simulations only considered single molecules in vacuo. Over time, more realistic or at least biologically relevant simulations could be performed. This trend is continuing today. The initial protein MD simulation, of the small bovine pancreatic trypsin inhibitor (BPTI), covered only 9.2 ps of simulation time. Modern simulations routinely have so-called equilibration periods much longer than that, and production simulations of tens of nanoseconds are routine, with the first microsecond MD simulation being reported in 1998.13 In addition, the original BPTI simulation included only about 500 atoms rather than the 104-106 atoms that are common today. While much of this advancement results from an immense increase in availability of computing power, major theoretical and methodological developments also contribute significantly. The number of publications regarding MD theory and application of MD to biological systems is growing at an extraordinary pace. A single review cannot do justice to the recent applications of MD. Using data from ISI Web of Science, the authors estimate that during 2005 at least 800 articles will be published that discuss molecular dynamics and proteins. The historical counts are shown in Figure 1. Open in a separate window Figure 1 Articles matching ISI Web of Science query “TS=(protein) AND TS=(molecular dynamics)”.

Source rocks contain significant volumes of hydrocarbon fluids trapped in kerogen, however their effective recovery is challenged due to amplified fluid-wall interactions and the nanopore confinement impact on fluid composition. Enhanced oil production can be achieved by modifying the existing molecular forces in kerogen pore-network by using custom-designed targeted chemistry technologies. Our objective is to show how the transport of hydrocarbons in kerogen and its recovery can be altered with the delivery of microemulsion nanodroplets into the pore network. This is done by using computational chemistry and molecular dynamics (MD) simulations. Molecular dynamics simulation is used to generate a 3D model replica of a Type II kerogen representative of source rocks located in Delaware and Midland basins in the United States. Oil phase saturated kerogen is modeled as consisting of nine different types of molecules: dimethyl naphthalene, toluene, tetradecane, decane, octane, butane, propane, ethane and methane. The delivered microemulsion is an aqueous dispersion of solvent-swollen surfactant micelles. The solvent and nonionic surfactant present in the microemulsion are modeled as d-limonene and dodecanol heptaethyl ether (C12E7), respectively. Molecular dynamics simulation experiments include two steps: (i) the injection of microemulsion treatment fluid into the oil-saturated kerogen pore-network, and (ii) transient flow-back of the oil-chemical mixture in the pore network. The utilized 3D kerogen models were developed based on a representative oil sample composition (H, C, O, S, N) from the region. Simulation results show that microemulsions can affect the reservoir via two different mechanisms. During the injection, microemulsion nanodroplets that enter the nano-capillaries of pore network disperse in the liquid present in the pore space under the influence of pore walls. The solvent dissolves in the oil phase and alters the physical and transport properties of the phase, while the surfactant molecules modify the wettability of the solid kerogen surfaces. The recovery effectiveness of heavier oil fractions is improved compared to the recovery effectiveness achieved with surfactant micelles without the solubilized solvent. New 3D kerogen models are presented using atomistic modeling and molecular simulations. These models possess important chemical and physical characteristics of the organic matter of the source rock. Molecular dynamic experiments indicate that solubilized solvent and surfactant are delivered as part of a microemulsion droplet and are expected to aid the mobilization of oil present within kerogen.

Selectivity and transport in aquaporins from molecular simulation studies.

Cryo-electron microscopy (EM) and X-ray studies proposed different mechanisms for annexin-induced membrane aggregation. In this work, molecular dynamics (MD) simulation technique was utilized to gain an insight into the calcium-induced conformational changes on annexin I and their implication in membrane aggregation mechanism. MD simulations were performed on the Ca2+ -free annexin I with the N-terminal domain buried inside the core (System 1), the Ca2+ -bound annexin I without N-terminal domain (System 2) and the Ca2+ -bound annexin I with the N-terminal domain exposed (System 3). Our results indicated that calcium binding increases the flexibility of annexin I core domain residues including the calcium coordinating residues. As a result, annexin I was activated to interact with the negatively charged membrane. The exposed N-terminal domain was very flexible and gradually lost the secondary structure during MD simulation, suggesting that the N-terminal may adopt a favorable conformation to bind a second membrane and also explaining the failure of attempts to crystallize the full-length annexin I in the presence of calcium ions. The measured dimensions of the averaged simulation structure of the Ca2+ -bound annexin I with the N-terminal exposed (System 3) support the proposed membrane aggregation mechanism based on X-ray studies.

Introduction: Milk kefir, a fermenting milk made with kefir grains, has shown potential in promoting apoptosis, regulating the cell cycle, and reducing tumor growth in breast cancer cells. This study aimed to investigate the stability and potential of milk kefir metabolites as inhibitors of breast cancer growth by interacting with the estrogen receptor alpha (ER-α), a key protein involved in breast cancer cell proliferation. We used computational methods, specifically molecular docking simulations with AutoDock and molecular dynamics (MD) simulations with Gromacs, to analyze how these metabolites bind to ER-α. Methods: A combination of molecular docking and MD simulations was used to explore how metabolites derived from milk kefir interact with ER-α, a crucial target in breast cancer therapy. The methodology included multiple stages: preparation of target proteins, preparation and screening of the metabolites, geometry optimization, molecular docking, and MD simulations. Results: The molecular docking simulations of 43 metabolites revealed three promising candidates: 2-Methyl (S35), benzeneethanol (S42), and 2,6-dimethyl-4-heptanone (S54), with binding affinities (ΔG) of -5.08, -5.06, and -4.90 kcal/mol, respectively. MD simulations further showed that the selected metabolites stabilized the ER-α-metabolite complex, with the 2,6-dimethyl-4-heptanone (S54) metabolite demonstrating the most negative total MM-GBSA energy value (ΔG = -22.98 kcal/mol), indicating a strong and stable binding interaction. Conclusion: 2,6-Dimethyl-4-heptanone, a metabolite from milk kefir, showed promising potential as a candidate for further development as a breast cancer treatment, offering a novel alternative to conventional therapies.

The diffusion of five flavor organic molecules, including D-limonene, myrcene, ethyl hexanoate, 2-nonanone, and linalool in low density polyethylene (LDPE) film were investigated by combined experimental and molecular dynamics (MD) simulation studies. The diffusion coefficients derived from the prediction model, experimental determination, and MD simulation were compared, and the related microscopic diffusion mechanism was investigated. The effects of the presence of the flavor organic molecules on the diffusion of O2 and H2O in polyethylene (PE) were also studied by MD simulation. Results show that: The diffusion of five flavor molecules in LDPE is well followed to Fick’s second law by the immersion experiment; MD simulation indicates the dual mode diffusion mechanism of the flavor molecules is in LDPE; the diffusion coefficients from MD simulation are close to those from the experimental determination, but are slightly larger than those values; the presence of the flavor organic molecules hinders the diffusion of O2 and H2O, which can be well explained from the fraction of free volume (FFV) and interaction energy calculation results derived from MD simulation.

Prediction of surface excess adsorption and retention factors in reversed-phase liquid chromatography from molecular dynamics simulations

HighlightsAccording to the design of the experiment (DoE), multivariate analysis models were used to optimize the critical process parameters combined with multi-objective optimization.Based on the optimized operating conditions, the MILT-HD method not only enhances the extraction efficiency from Amomi fructus but also reduces energy demands and CO2 emissions.Based on the density functional theoretical (DFT) and molecular dynamics (MD) simulations, the mechanisms for ionic liquids (ILs) to improve the extraction efficiency of essential oil was comprehensively revealed.In this paper, Amomi fructus (Latin) was used to explore the mechanism of ionic liquids (ILs) in improving the extraction efficiency of essential oils. Microwave assisted ionic liquid treatment followed by a hydro-distillation (MILT-HD) process for isolating Amomi fructus essential oil was optimized by multi-objective optimization. Under optimum operating conditions, the IL-assisted extraction method not only enhances extraction efficiency but also reduces energy demands and CO2 emissions. Since the hydrogen bond structure network of cellulose in the cell wall is an important reason for hindering diffusion of essential oils, the mechanism of ILs was explored by density functional theoretical (DFT) and molecular dynamics (MD) simulations. According to DFT calculations, ILs can facilitate the cleavage of cellulose chains and have strong non-covalent interactions with cellulose. Based on the MD simulations, the degree of destruction of the cellulose hydrogen bond structure was explored. According to the DFT and MD simulations, the ILs can significantly destroy cellulose structure, thereby promoting essential oil release from the plant. These results were confirmed by scanning electron microscopy (SEM) and Fourier transform infrared spectroscopy (FTIR). This work is conducive to better understand the MILT-HD process for isolating essential oil and comprehensively understand the mechanism of ILs in the extraction process.

Molecular Dynamics (MD) simulations are uniquely suitable for providing molecular-level insights into the Electric Double Layer (EDL) that forms when a charged surface is in contact with an aqueous solution. However, simulations are only as accurate in predicting EDL properties as permitted by the atomic interaction models. Experimental ζ-potential values and surface charges could provide a potentially suitable reference to validate and tune the interaction models, if not for the fact that they themselves are a product of imperfect models used to interpret the raw measurement data. Here, we present an approach to tune an interaction model by comparing Electro-Osmotic Flow (EOF) MD simulations against experimental Streaming Current (SC) measurements while minimizing potential modeling errors arising from both approaches. The point that is least susceptible to interpretation and modeling errors is argued to be at the concentration for which zero flow velocity is observed in EOF simulations and a net zero electric current is measured in SC experiments. At this concentration, the ζ-potential is also zero. We were able to match the experimental concentration at which ζ = 0 in MD simulations for a CaCl2 solution at pH 7.5 in contact with fused silica by tuning the ion-surface Lennard-Jones cross interactions. These interactions were found to greatly affect the ion distribution within the EDL and particularly the formation of inner-sphere surface-complexes, which, in turn, affects the electrokinetic flow. With the ion distribution determined explicitly, a series of properties can be calculated unambiguously, such as the capacitance needed for surface complexation models.

Prediction of nonlocal elasticity parameters using high-throughput molecular dynamics simulations and machine learning

Molecular dynamic (MD) simulations using the BMS nucleic acid force field produce environment and sequence dependent DNA conformations that closely mimic experimentally derived structures. The parameters were initially developed to reproduce the potential energy surface, as defined by quantum mechanics, for a set of small molecules that can be used as the building blocks for nucleic acid macromolecules (dimethyl phosphate, cyclopentane, tetrahydrofuran, etc.). Then the dihedral parameters were fine tuned using a series of condensed phase MD simulations of DNA and RNA (in zero added salt, 4M NaCl, and 75% ethanol solutions). In the tuning process the free energy surface for each dihedral was derived from the MD ensemble and fitted to the conformational distributions and populations observed in 87 A- and B-DNA x-ray and 17 B-DNA NMR structures. Over 41 nanoseconds of MD simulations are presented which demonstrate that the force field is capable of producing stable trajectories, in the correct environments, of A-DNA, double stranded Aform RNA, B-DNA, Z-DNA, and a netropsin-DNA complex that closely reproduce the experimentally determined and/or canonical DNA conformations. Frequently the MD averaged structure is closer to the experimentally determined structure than to the canonical DNA conformation. MD simulations of A- to B- and B- to A-DNA transitions are also shown. A-DNA simulations in a low salt environment cleanly convert into the B-DNA conformation and converge into the RMS space sampled by a low salt simulation of the same sequence starting from B-DNA. In MD simulations using the BMS force field the B-form of d(GGGCCC)2 in a 75% ethanol solution converts into the A-form. Using the same methodology, parameters, and conditions the A-form of d(AAATTT)2 correctly converts into the B- DNA conformation. These studies demonstrate that the force field is capable of reproducing both environment and sequence dependent DNA structures. The 41 nanoseconds (nsec) of MD simulations presented in this paper paint a global picture which suggests that the DNA structures observed in low salt solutions are largely due to the favorable internal energy brought about by the nearly uniform screening of the DNA electrostatics. While the conformations sampled in high salt or mixed solvent environments occur from selective and asymmetric screening of the phosphate groups and DNA grooves, respectively, brought about by sequence induced ion and solvent packing.

Quantitative Analysis of Water Dynamics in and near Proteins

The composition dependent local environment of three organic dyes in binary mixtures of a room temperature ionic liquid (1-methyl-3-pentylimidazolium bromide, [pmim][Br]) and water is studied by fluorescence correlation spectroscopy (FCS) and molecular dynamics (MD) simulations. We used three dyes-neutral coumarin 480 (C480), anionic coumarin 343 (C343), and highly hydrophobic 4-(dicyanomethylene)-2-methyl-6-(p-dimethyl-aminostyryl)-4H-pyran (DCM)-to probe different environments in the binary mixtures. The heterogeneity of the [pmim][Br]-water mixture leads to multiple values (i.e., distribution) of diffusion coefficients (Dt). In these binary mixtures, the effective viscosity (ηeff, obtained from FCS) and the local concentration of the [pmim][Br] around the three dyes (revealed by MD simulations) are found to be quite different than that in bulk. The viscosity experienced by the C480 and C343 dyes is almost twice as large as that experienced by DCM dye. Through rigorous MD simulation, we show that in the vicinity of the less hydrophobic coumarin dyes (C480 and C343) compared to DCM dye, the local concentration of the [pmim][Br] is ∼3-7 times larger than that in bulk. In the case of the most hydrophobic dye, DCM, the local concentration of [pmim][Br] is almost similar to bulk-like. Further analysis reveals the formation of hydrogen bond between the imidazolium ring of [pmim][Br] and the carbonyl oxygen atom of the coumarin dyes (C-H[pmim][Br]⋯O=CDye). Finally, computer simulation indicates a slow component of solvation dynamics in the [pmim][Br]-water mixture in the time scale of ∼100-200 ps, which is similar to the experimental observation.