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Molecular Dynamics

Molecular dynamics is a computational simulation technique used to study the physical movements of atoms and molecules.
It provides insights into the behavior and interactions of biomolecular systems, such as proteins, nucleic acids, and lipids, at the atomic level.
This method allows researchers to investigate the structure, function, and dynamics of these complex systems, which is crucial for understanding biological processes and developing new drugs and materials.
Molecular dynamics simulations are an essential tool in the field of computational biology and chemistry, enabling the exploration of phenomena that cannot be easily observed experimentally.
By integrating experimental data and theoretical models, molecular dynamics research enhances the accuracy and reproducibility of scientific discoveries, leading to advancments in a variety of disciplines.

Most cited protocols related to «Molecular Dynamics»

Computational Ligand Docking: Advancements and Limitations

A variety of academic and commercial methods for computational ligand docking are currently available (see Ref 1 (link) for an extensive review of current methods). Most of these methods simplify the problem in two ways to make the computation tractable. First, the conformational space is reduced by imposing limitations to the system, such as a rigid receptor and fixed bond angles and lengths in the ligand. Second, a simplified scoring function, often based on empirical free energies of binding, is used to score poses quickly at each step of the conformation search.
Both of these are serious limitations, and users must employ tools such as molecular dynamics or free energy perturbation if a more realistic conformational search or energy prediction is necessary. These tools are complementary with computational docking methods, since docking methods generally search a larger conformational space, but more advanced methods can predict conformation and energy more accurately within a local area of the conformational landscape.
Advanced docking methods may be used to improve results in cases where the limitations of requiring a rapid method for energy evaluation are too restrictive. For instance, many docking methods employ a rigid model for the receptor, which often leads to improper results for proteins with appreciable induced fit upon binding. AutoDock includes a method for treating a selection of receptor sidechains explicitly, to account for limited conformational changes in the receptor. In addition, ordered water molecules often mediate interactions between ligands and receptors, and advanced methods for treating selected waters explicitly have been implemented in AutoDock. Both of these advanced methods are demonstrated in this protocol.
Many reports have compared the performance of popular docking methods such as AutoDock (recently reviewed by Sousa et al. ^{7 (link)}). Different methods can achieve different success rates depending on specific targets, but in general, they all perform similarly when tested on a series of diverse protein-ligand complexes: they all perform well for the prediction of bound complexes for drug-sized molecules, with estimates of free energies of binding with errors of roughly 2–3 kcal/mol, provided that there is not significant motion required in the receptor. Better results may be obtained by tuning the docking method for a particular system or moving to more sophisticated and computationally-intensive parameterizations of the system.

Forli S., Huey R., Pique M.E., Sanner M., Goodsell D.S, & Olson A.J. (2016). Computational protein-ligand docking and virtual drug screening with the AutoDock suite. Nature protocols, 11(5), 905-919.

Publication 2016

APEX1 protein, human Ligands Molecular Dynamics Muscle Rigidity Pharmaceutical Preparations Proteins Sousa Staphylococcal Protein A

Spherical Cutoff Approach for Non-Bonded Interactions

Calculation of the non-bonded pairwise atomic interactions, i.e.,
interactions between atoms not directly bonded to one another, is typically
the most computationally demanding aspect of energy and energy-derivative
calculations. Since the number of possible pairwise interactions in a system
of N atoms grows as N², the
explicit calculation of all Coulombic and LJ terms is usually impractical
for large systems. It is therefore necessary, in systems of greater than a
few thousand atoms, to truncate the non-bonded interactions at a
user-specified cutoff distance. The use of this approximation, which is
referred to as a spherical cutoff approach, means that only atom pairs
within the cutoff distance need to be included, greatly speeding up the
calculation. However, it may introduce artifacts. Most notably, a simple
truncation of the potential energy creates artificial forces at the cutoff
distance (because of the discontinuity in the energy), which can give rise
to artifacts in dynamics or structure.308 Such artificial forces have been shown, for example, to
significantly inhibit protein motion.309 (link) For this reason, proper truncation schemes for non-bonded
interactions are an essential part of the spherical cutoff approach; this is
especially true for the electrostatic interactions, which have a longer
range than the van der Waals interactions. The simplest treatments consist
of truncating the Coulomb interaction at the cutoff distance, while using a
numerical procedure to decrease the unwanted influence of the
truncation.308 CHARMM provides a
variety of truncation methods that act to smooth the transition in the
energy and force at the cutoff distance, thereby reducing the errors in that
region. These methods, which can be applied to both the electrostatic
(Coulombic) and LJ interactions, include energy shifting and switching,22 as well as force shifting and
switching approaches.308 ,310 The force shift/switch methods
insure that, as the interatomic separation approaches the truncation
distance, the forces go to zero in a smooth, continuous manner. These
methods are, thus, particularly useful in MD simulations, where the forces
determine the trajectories of the atoms, and they are the currently
recommended approaches for most cases when a spherical cutoff is used.
Molecular dynamics trajectories of even highly charged biomolecules like DNA
have been shown to be stable if the appropriate smoothing functions and
cutoff distances (usually at least 12 Å) are used (see
below).40 ,311 (link)

Brooks B.R., Brooks CL I.I.I., MacKerell AD J.r., Nilsson L., Petrella R.J., Roux B., Won Y., Archontis G., Bartels C., Boresch S., Caflisch A., Caves L., Cui Q., Dinner A.R., Feig M., Fischer S., Gao J., Hodoscek M., Im W., Kuczera K., Lazaridis T., Ma J., Ovchinnikov V., Paci E., Pastor R.W., Post C.B., Pu J.Z., Schaefer M., Tidor B., Venable R.M., Woodcock H.L., Wu X., Yang W., York D.M, & Karplus M. (2009). CHARMM: The Biomolecular Simulation Program. Journal of computational chemistry, 30(10), 1545-1614.

Publication 2009

Cardiac Arrest Electrostatics Molecular Dynamics Proteins

Highly Reproducible RESP and ESP Charges Protocol

The R.E.D. program is a tool for developing highly reproducible RESP and ESP charges and building the force field libraries for new molecules and molecular fragments in the Tripos mol2 file format (http://tripos.com/data/support/mol2.pdf). Although atomic charges and force field libraries can be obtained separately, these two concepts are strongly connected in R.E.D. Charges are derived for whole molecules and/or molecular fragments using intra-, inter-molecular charge constraint(s) as well as inter-molecular charge equivalencing. A force field library is then built for the whole molecule and molecular fragment designed during the fitting step. Chemical elements up to Bromine are automatically handled. A modular approach has been followed allowing the straightforward development of large numbers of charge fitting protocols. R.E.D. IV is a direct expansion of the R.E.D. III.x suite of programs. R.E.D. IV extends many features of R.E.D. III.x, and handles complex charge derivation. It enables to use a large set of orientations, conformations and molecules in RESP and ESP charge derivation, and it makes it possible to build force field library practically for any type of molecular fragments. The latest developments include (i) the reinforcement of charge reproducibility by differentiating translation and rotation in MEP computation, (ii) the building of a complex set of force field library or Force Field Topology DataBases (FFTopDB) for any type of bio-organic and bio-inorganic molecules, (iii) the generation of all-atom force field libraries or united-carbon force field libraries and (iv) the development of a statistic module allowing charge value comparisons (this represents a first attempt toward validating charge values).

Vanquelef E., Simon S., Marquant G., Garcia E., Klimerak G., Delepine J.C., Cieplak P, & Dupradeau F.Y. (2011). R.E.D. Server: a web service for deriving RESP and ESP charges and building force field libraries for new molecules and molecular fragments. Nucleic Acids Research, 39(Web Server issue), W511-W517.

Publication 2011

Bromine Carbon DNA Library Mental Orientation Molecular Dynamics Reinforcement, Psychological Respiratory Rate

Molecular Dynamics Simulations of Peptides

Simulations of Ala₅ were run using the simulation package Gromacs^{28 ,29} using a protocol similar to that used in our previous work,^{15 (link)} and with the implementation of the Amber force fields by Sorin and Pande.^{30 (link)} The peptide was unblocked and protonated at both N and C termini, corresponding to the experimental conditions of pH 2.^{14 (link)} Molecular dynamics simulations of each peptide in a 30 Å cubic simulation box of explicit TIP3P water³¹ were run at a constant temperature of 300 K and a constant pressure of 1 atm, with long range electrostatic terms evaluated using particle-mesh Ewald (PME) using a 1.0 Å grid spacing and a 9 Å cutoff for short-range interactions. For each force field, four runs of 50 ns each were initiated from different starting configurations. Further details of the simulation protocols are as published.^{15 (link)}Replica exchange molecular dynamics (REMD) simulations of the blocked peptide Ac-(AAQAA)₃-NH₂ were run using Gromacs^{28 ,29} with 32 replicas spanning a temperature range of 278 K to 595 K. The peptide was solvated in a truncated octahedron simulation cell of 1022 TIP3P water molecules with an initial distance of 35 Å between the nearest faces of the cell. This cell was equilibrated for 200 ps at 300 K and a constant pressure of 1 atm. Subsequently, all REMD simulations were done at constant volume, with long range electrostatics calculated using PME with a 1.2 Å grid spacing and 9 Å cutoff. Dynamics was propagated with a Langevin integration algorithm using a friction of 1 ps⁻¹, and replica exchange attempts every 1 ps (every 500 steps with a time step of 2 fs). Typical acceptance probabilities for the replica exchange were in the range 0.1–0.5. All replica exchange runs used the same set of initial configurations, which were taken from the final configurations of a preliminary replica exchange simulation with ff99SB. The simulations were run for at least 30 ns per replica, of which the first 10 ns were discarded in the analysis (with an aggregate of ≈ 1 µs for each force field). To test for possible system size dependence, additional simulations of Ac-(AAQAA)₃-NH₂ in a 45 Å truncated octahedron box solvated by 2268 water molecules were run for 30 ns using a similar protocol, in this case with 32 replicas at 5 K intervals between 278 and 433 K.
Additional simulations were performed for the unblocked peptide HEWL19, derived from hen egg-white lysozyme with sequence KVFGRC(SMe)ELAAAMKRHGLDN. The structure and parameters for the S-methylated Cys 6 were adapted from those for methionine and are given in Supporting Information (SI) Figure 1 and Table 1 respectively. Both termini as well as all acidic side chains were protonated, corresponding to the experimental conditions of pH 2.^{14 (link)} The peptide was solvated in a truncated octahedron simulation cell with a 42 Å distance between nearest faces, and equilibrated at constant pressure for 200 ps at 300 K. Constant volume REMD was run with 32 replicas spanning the temperature range 278 K to 472 K, for 27 ns, of which the first 10 ns were discarded in the analysis. All other parameters were the same as for Ac-(AAQAA)₃-NH₂.
Native state simulations of ubiquitin were run starting from the crystal structure 1UBQ.^{32 (link)} The protein was solvated by 2586 explicit TIP3P water molecules in a cubic simulation box of 45 Å length with long range electrostatics calculated using PME with a 1.2 Å grid spacing and 9 Å cutoff. To neutralize the system charge, 7 sodium and 8 chloride ions were added. Dynamics was propagated for 30 ns at constant pressure (1 atm) and temperature (300 K) using a Nosé-Hoover thermostat³³ and Parrinello-Rahman barostat.³⁴

Best R.B, & Hummer G. (2009). Optimized molecular dynamics force fields applied to the helix-coil transition of polypeptides. The journal of physical chemistry. B, 113(26), 9004-9015.

Publication 2009

Acids Amber Cells Chlorides Cuboid Bone Electrostatics Face Friction hen egg lysozyme Ions Methionine Molecular Dynamics Peptides Pressure Proteins Sodium Ubiquitin

Hybrid Clustering for Kinetic Modeling

The first step in MSM construction is to identify conformational states. Because MSM accuracy depends on the quality of state decomposition, enhanced clustering is a natural way to improve MSM methods. In MSMBuilder2, as in other MSM methods, it is vital to achieve kinetic clustering–that is, states sufficiently fine so as to be free from internal kinetic barriers.
Previous work^{9 (link),11 (link)} used an O(kN) approximate k-centers clustering,²² where k denotes the desired number of clusters and N denotes the number of conformations. That algorithm can be viewed as an approximate solution to the problem:

min_{σ} max_{i} d (x_{i}, σ (x_{i}))

Here, σ(x) is the “assignment” function that maps a conformation to the nearest cluster center. d(x, y) is the distance between two conformations x and y, measured via the RMSD metric.²³ The minimization occurs over all clusterings (σ) with k states, subject to some choice of initial center. Finally, the max is taken over all conformations in the dataset.
The k-centers approach minimizes the worst-case clustering error, as quantified by the objective function f_max(σ) = max_id(x_i, σ(x_i)). Considering only the worst-case clustering error is problematic for conformational dynamics, particularly in protein folding, as the worst-case error is often determined by extended (unfolded) conformations with very small populations. Furthermore, cluster centers generated by this algorithm are often non-central, that is, they often do not represent the geometric center of their associated data.
Alternatively, k-medoids clustering²⁴ approximately minimizes

f_{med} (σ) = \frac{1}{N} \sum_{i} d {(x_{i}, σ (x_{i}))}^{2}

. With sufficient sampling, constant temperature molecular dynamics draws Boltzmann-weighted conformations; thus, by averaging over all conformations, f_med(σ) is an objective function that penalizes the (approximately) ensemble-averaged deviation from cluster centers. The resulting clusters tend to be centrally located within their respective data–i.e. they are medoids.^{25 (link)} However, for folded proteins, strict Boltzmann weighting yields few unfolded states, often leaving unfolded conformations assigned to folded states. This deficiency can be explained in terms of f_max(σ). A clustering that minimizes f_med(σ) may in fact be worse when evaluated by f_max(σ); conversely, minimizing f_max(σ) could increase f_med(σ). For accurate kinetic clustering of biomolecule dynamics, one should consider both the worst case (f_max) and average case (f_med) clustering error.
Simultaneously optimizing both the average and worst-case error can be achieved by combining the k-centers and k-medoid algorithms. Let ε be some desired worst-case clustering error. Define the set

S (ε) = {σ : f_{max} (σ) \leq ε}

Thus, S(ε) is the set of all clusterings that have worst-case errors of ε (or better). We now apply a k-medoids clustering algorithm, but restricted to the set S(ε). In practice, we use a two step approach:

Apply approximate k-centers to return initial clusters g_i, terminating when f_max(σ) ≤ ε.

Apply approximate k-medoids to the result, but rejecting all moves that increase f_max(σ).

For (2), we employ a modification of the Partitioning Across Medoids algorithm.²⁴ For each cluster g_i, we randomly select a conformation x_i assigned to that state. The clustering errors (f_med, f_max) are calculated and compared to the values that would be obtained were x_i instead the cluster center of that state. If f_med is improved and f_max is improved (or unchanged), the move is accepted. In practice, f_max decreases insignificantly during this process, but f_med decreases dramatically over a handful of iterations. As described, the hybrid algorithm tends to preserve the overall distribution of clusters, essentially refining k-centers to be more “central“; this is desirable because k-centers is known²² to provide a reasonable partition of conformation space.

Beauchamp K.A., Bowman G.R., Lane T.J., Maibaum L., Haque I.S, & Pande V.S. (2011). MSMBuilder2: Modeling Conformational Dynamics at the Picosecond to Millisecond Scale. Journal of chemical theory and computation, 7(10), 3412-3419.

Publication 2011

BAD protein, human Hybrids Kinetics Microtubule-Associated Proteins Molecular Dynamics Population Group Proteins

Most recents protocols related to «Molecular Dynamics»

Molecular Dynamics Simulation of Dithizone and Cellobiose

The
recent and publicly available toolkit AutoSolvate^{37 (link)} was run in an Ubuntu 22.04 LTS environment, encompassing
Python 3.7, Openbabel 2.4.0, AmberTools 22, MDTraj 1.9.4, and NGLView
3.0.3. The process involved Antechamber with the AM1-BCC model to
assign point charges, LEaP for Generalized Amber Force Field parameters,
and the B3LYP hybrid exchange-correlation functional for density functional
theory (DFT) calculations. Both water and chloroform at 298 K were
tested as solvents.
Three XYZ files (ESI-01) were studied for the structures of DTZ: dithizone_planar.xyz corresponds
to the highly conjugated symmetric form, with the nonaromatic hydrogen
atoms on the outer nitrogen atoms (Figure 1a); dithizone_thione.xyz differentiates the
azo group at one side of the thiocarbazone chain and two secondary
amino groups at the other side; and dithizone_anion.xyz corresponds
to the deprotonated thiol tautomer of dithizone with ammonium as counterion.
Finally, oxycellobiose.xyz is a proxy for CNFs, simply containing
a cellobiose molecule with regioselective oxidation on carbon 6 in
one of its two glucose units. Bond angles, bond distances, and molecular
surfaces of these three forms were calculated and displayed by Jmol
14.
With the same toolkits (AutoSolvate, AmberTools),
a simulation
of molecular dynamics was run on the basis of molecular mechanical
(MM) energy minimization.^{38 (link)} Then, a solvation
shell considering the most neighboring solvent molecules was modeled
and 10 XYZ files, also included in ESI-01, were extracted. Each file corresponds to every 10 frames, spaced
by a timelapse of 4 ps, of the dynamic simulation.

Aguado R.J., Mazega A., Fiol N., Tarrés Q., Mutjé P, & Delgado-Aguilar M. (2023). Durable Nanocellulose-Stabilized Emulsions of Dithizone/Chloroform in Water for Hg2+ Detection: A Novel Approach for a Classical Problem. ACS Applied Materials & Interfaces, 15(9), 12580-12589.

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Publication 2023

Amber Ammonium Anions Carbon Cellobiose Chloroform Dithizone Glucose Hybrids Molecular Dynamics Nitrogen Reading Frames Solvents Sulfhydryl Compounds Thiones

Computational Analysis of HNPCA Compound

The compound HNPCA was fully optimized in the gas phase using the DFT method. The functional used was B3LYP [41 (link), 42 (link)] with the 6–311 + + G(d,p) basis set. HNPCA was also optimized in methanol using the same theoretical method within the PCM solvation model [43 , 44 (link)] The ground-state structure was confirmed by frequency computations with the absence of imaginary frequency in both the gas phase and methanol. The optimized structure of HNPCA in methanol was used for computing chemical shifts with the Gauge-Including Atomic Orbital method [45 (link)] using isotropic shieldings of tetramethylsilane (TMS) computed using the same method. The non-covalent interaction (NCI), based on the reports of Johnson et al. [46 (link)], was explored by the noncovalent interaction-reduced density gradient (NCI-RDG) analysis using the Multiwfn program [47 (link)]. The isosurfaces were plotted using the Visual Molecular Dynamic (VMD) software [48 (link)] and The Gnuplot 4.2 program [49 ] and Ghostscript interpreter were employed to generate the 2D plots. All computations were carried out at 1 atm and 298.15 K using Gaussian 16 [50 ]. Visualization of the output files was done using GaussView 6 [51 ] and Chemcraft [52 ]. The coordinates of the optimized structure of HNPCA in the gas phase and methanol are provided in Table S1 of the supplementary information.
The interactions in the crystal structure of HNPCA were investigated by the Hirshfeld surface analysis [53 (link)] along with their 2D fingerprint plots [54 (link)] which were generated using the CrystalExplorer17.5 software [55 ]. Hirshfeld surface is represented by d_e and d_i, denoting the distance from the nearest atom outside and inside of the surface, respectively, and both are used to define the normalized contact distance (d_norm) with respect to the Van der Waals (vdW) radii as per Eq. 1.

d_{norm} = \frac{(d_{i} - r_{i}^{vdW})}{r_{i}^{vdW}} + \frac{(d_{e} - r_{e}^{vdW})}{r_{e}^{vdW}}

For the visualization of d_norm, a red-blue-white color scale was selected. The red color denotes a negative value of the d_norm whereas the blue color denotes a positive value of the d_norm. The positive and negative values of the d_norm denote whether intermolecular interactions are larger or smaller than the vdW separation respectively. Therefore, the mapping of d_norm on the Hirshfeld surface illustrates the donor and acceptor properties and helps in the analysis of the intermolecular interactions.

Ugwu D.I., Eze F.U., Ezeorah C.J., Rhyman L., Ramasami P., Tania G., Eze C.C., Uzoewulu C.P., Ogboo B.C, & Okpareke O.C. (2023). Synthesis, Structure, Hirshfeld Surface Analysis, Non-Covalent Interaction, and In Silico Studies of 4-Hydroxy-1-[(4-Nitrophenyl)Sulfonyl]Pyrrolidine-2-Carboxyllic Acid. Journal of Chemical Crystallography.

Publication 2023

Methanol Molecular Dynamics Psychological Techniques Radius tetramethylsilane Tissue Donors

Molecular Dynamics of Viral Proteins

Theoretical conformational
sampling was achieved using full-atomistic equilibrium molecular dynamics.
Data were collected from five individual trajectory replicas of 1
μs length each. The trajectories were calculated using GROMACS
5.1.5 and GROMACS 2018.3,^{33 (link),34} employing CHARMM36m
force field parameters.^{35 (link)} Modeling of the
initial systems was attained with CHARMM-GUI^{36 (link)−38 (link)} using the X-ray
structures from PDB-ID 4OOX (SAGA) or PDB-ID 2OBT (VA387), TIP3P water,^{39 (link)} and 0.15 M NaCl ionization in a cubic box. The systems
were minimized with the steepest descent method and briefly equilibrated
for at least 0.1 ns in the NVT ensemble. For subsequent NPT production
sampling at 303.15 K, a Nosé–Hoover thermostat^{40 (link)} and Parrinello–Rahman coupling^{41 (link)} were employed. The simulation time step was
0.002 ps, and conformations were saved every 20 ps. For each protein,
5 trajectories of 1 μs length each were simulated. We note that
the VA387 simulations were performed later than the SAGA simulations,
which gave us access to much faster GPU nodes. To take full advantage
of the GPUs, we moved to a newer GROMACS version, with the consequence
that a few updates were made to the simulation protocol and input
parameters (see the SI).
Data analysis
and visualization were carried out with VMD 1.9.3,^{42 (link)} GROMACS tools, and the Python packages NumPy,^{43 (link)} MDTraj,^{44 (link)} and MatPlotLib.^{45 (link)} Here, the side chain torsion angles of the Asn
residues, as well as the distances from the Cγ atoms of Asn
to the backbone nitrogen atoms of the subsequent amino acids, were
monitored. φ is defined as torsion angle between C_i-1-N_i-CA_i-C_i, ψ between N_i-CA_i-C_i-N_i+1, Χ₁ between N-CA-CB-CG, and Χ₂ between CA-CB-CG-OD1. The free energy maps were constructed
from the 2D probability densities as estimated by binning the data
to 100 × 100 bins of 2π/100 widths. The relative free energies
were computed as the negative natural logarithm of the probability
density. Clustering of the 4D torsional angle space was achieved with
the HDBSCAN^{46 (link)} method using an extended
angle representation z(α) = [cos α, sin
α]. More details are given in the SI. One of the 5 MD trajectories of the SAGA P-dimer has been used
to generate conformers for ensemble docking in an earlier study.^{47 (link)}

Creutznacher R., Schulze-Niemand E., König P., Stanojlovic V., Mallagaray A., Peters T., Stein M, & Schubert M. (2023). Conformational Control of Fast Asparagine Deamidation in a Norovirus Capsid Protein. Biochemistry, 62(5), 1032-1043.

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Publication 2023

Amino Acids Biological Models Cuboid Bone Microtubule-Associated Proteins Molecular Dynamics Nitrogen Proteins Python Sodium Chloride Vertebral Column

Conformational Analysis of Molecular Structure

A 3D structure was
obtained by conformational distribution calculation with the molecular
mechanic of Merck molecular force field.^{63 (link)}

Miao Q., Dekkers R., Gupta K.B., Overhand M., Dasgupta R, & Ubbink M. (2023). Rigidified and Hydrophilic DOTA-like Lanthanoid Ligands: Design, Synthesis, and Dynamic Properties. Inorganic Chemistry, 62(9), 3776-3787.

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Publication 2023

Molecular Dynamics

Metadynamics Simulations of Divalent Ion Binding

We used all-atom metadynamics to
simulate the process of uptake of divalent ions within the active
site of the GCS enzyme, i.e., Mg²⁺ and Mn²⁺.
The same binding site was found by both simulations; thus, we report
here only the Mn²⁺ case. The ion was placed outside the
binding pocket, and its motion was driven by a metadynamics bias potential,^{17 (link)} combined with a cylinder-shaped restraint potential
as a special case of the more general funnel shape, as shown in Figure S2. The PLUMED 2.8 package,^{18 (link),19 (link)} which includes the funnel metadynamics (FM) code,^{20 (link)} has been patched to the GROMACS 2021.4^{21 (link)} engine to carry out the metadynamics runs. The cylinder
shape of the potential has been customized on the target structure
according to the following parameters: Zcc = 0.2 nm as the distance
between the switching point from the cone to the cylinder section,
Alpha = 0.01 rad as the angle defining the amplitude of the cone section,
and R_cyl = 1.1 nm as the radius of the
cylinder section.^{20 (link)} The cylinder axis lies
approximately along the axis of the substrate entry channels, and
the position of the ion is restrained inside the cylinder. Two biased
collective variables (CVs) were chosen according to the FM protocol,
i.e., the position along the axis of the cylinder and its distance
from the rotation axis. Gaussian functions, describing the bias potential
added in time at the position defined by CVs, had a height of 1 kJ/mol
and a sigma value of 0.05 and were saved every 1 ps. At the end of
the simulation, we determined the position of the ion binding site;
thereafter, the distance between the ion and the binding site and
the ion hydration degree were recalculated, and their free energy-like
diagrams were obtained. Although classical molecular dynamics and
metadynamics based on point-charge atomistic force fields are inadequate
to provide the accurate binding geometry of the coordination metal
Mn²⁺, we are fully confident that its approximate location
within the enzyme (the same as Mg²⁺), mainly determined
by its charge, is correctly defined.

Canini G., Lo Cascio E., Della Longa S., Cecconi F, & Arcovito A. (2023). Human Glucosylceramide Synthase at Work as Provided by “In Silico” Molecular Docking, Molecular Dynamics, and Metadynamics. ACS Omega, 8(9), 8755-8765.

Publication 2023

Binding Sites Enzymes Epistropheus Molecular Dynamics Radius Retinal Cone Self Confidence

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What are the key benefits of using Molecular Dynamics simulations?

Molecular Dynamics (MD) simulations provide invaluable insights into the behavior and interactions of biomolecular systems at the atomic level. This method allows researchers to investigate the structure, function, and dynamics of complex systems like proteins, nucleic acids, and lipids, which is crucial for understanding biological processes and developing new drugs and materials. By integrating experimental data and theoretical models, MD research enhances the accuracy and reproducibility of scientific discoveries, leading to advancements in a variety of disciplines.

What are some common applications of Molecular Dynamics?

Molecular Dynamics simulations have a wide range of applications, including the study of protein folding and interactions, drug design and optimization, membrane protein dynamics, and the development of new materials. Researchers use MD to explore phenomena that cannot be easily observed experimentally, such as the conformational changes of biomolecules or the transport of ions and small molecules across membranes. This computational approach provides a powerful complement to experimental techniques, enabling a more comprehensive understanding of complex systems.

How can PubCompare.ai assist with Molecular Dynamics research?

PubCompare.ai can enhance the accuracy and reproducibility of your Molecular Dynamics research in several ways. The platform's AI-driven analysis helps you screen protocol literature more efficiently, leveraging artificial intelligence to pinpoint critical insights. PubCompare.ai can assist researchers in identifying the most effective protocols related to Molecular Dynamics for their specific research goals, highlighting key differences in protocol effectiveness and enabling them to choose the best option for reproducibility and accuracy. By optimizing your Molecular Dynamics workflows, PubCompare.ai can elevate your research and lead to more robust scientific discoveries.

What are some common challenges in Molecular Dynamics simulations?

One of the key challenges in Molecular Dynamics simulations is the need for accurate force fields, which describe the interactions between atoms and molecules. Selecting the appropriate force field for a particular system can be tricky, as different force fields may be better suited for different types of molecules or applications. Additionally, the computational power required for MD simulations can be quite significant, especially when modeling large, complex systems. Researchers must carefully balance the level of detail and the computational resources available to ensure the accuracy and feasibility of their simulations. Proper validation and comparison of simulation results with experimental data are also crucial to ensure the reliability of the findings.

More about "Molecular Dynamics"

Molecular Dynamics (MD) is a powerful computational simulation technique that allows researchers to study the physical movements and interactions of atoms and molecules at the atomic level.
This method is widely used in the fields of computational biology, chemistry, and materials science, as it provides invaluable insights into the structure, function, and dynamics of complex biomolecular systems, such as proteins, nucleic acids, and lipids.
MD simulations are an essential tool for understanding biological processes and developing new drugs and materials.
By integrating experimental data and theoretical models, MD research enhances the accuracy and reproducibility of scientific discoveries, leading to advancements in a variety of disciplines.
The Metadescription mentioned the use of ImageQuant software, PhosphorImager, ImageQuant 5.2, ImageQuant, MMFF94, Storm PhosphorImager, Storm 860 PhosphorImager, [γ-32P]ATP, Storage phosphor screen, and TRIzol reagent.
These tools and techniques are often used in conjunction with MD simulations, as they provide valuable experimental data that can be used to validate and refine the computational models.
For example, ImageQuant software and PhosphorImager systems are commonly used to analyze and quantify biomolecular interactions, such as protein-protein or protein-DNA interactions, which can then be incorporated into MD simulations to improve their predictive power.
Similarly, the use of [γ-32P]ATP and Storage phosphor screens can provide insights into the dynamics of biomolecular processes, which can be leveraged to enhance the accuracy of MD simulations.
In addition to these experimental techniques, MD simulations also rely on advanced computational methods, such as the MMFF94 (Merck Molecular Force Field) for accurate force field calculations, to ensure the reliability and reproducibility of the results.
By combining cutting-edge experimental and computational approaches, researchers can gain a deeper understanding of the complex behavior and interactions of biomolecular systems, ultimately leading to groundbreaking discoveries and advancements in fields like drug development, materials science, and the study of fundamental biological processes.