Molecular dynamics
Molecular dynamics (MD) is a
Because molecular systems typically consist of a vast number of particles, it is impossible to determine the properties of such
For systems that obey the
History
MD was originally developed in the early 1950s, following earlier successes with
As early as 1941, integration of the many-body equations of motion was carried out with
In 1957, Berni Alder and Thomas Wainwright used an IBM 704 computer to simulate perfectly elastic collisions between hard spheres.[4] In 1960, in perhaps the first realistic simulation of matter, J.B. Gibson et al. simulated radiation damage of solid copper by using a Born–Mayer type of repulsive interaction along with a cohesive surface force.[5] In 1964, Aneesur Rahman published simulations of liquid argon that used a Lennard-Jones potential; calculations of system properties, such as the coefficient of self-diffusion, compared well with experimental data.[6] Today, the Lennard-Jones potential is still one of the most frequently used intermolecular potentials.[7][8] It is used for describing simple substances (a.k.a. Lennard-Jonesium[9][10][11]) for conceptual and model studies and as a building block in many force fields of real substances.[12][13]
Areas of application and limits
First used in
The results of MD simulations can be tested through comparison to experiments that measure molecular dynamics, of which a popular method is NMR spectroscopy. MD-derived structure predictions can be tested through community-wide experiments in Critical Assessment of Protein Structure Prediction (
MD simulation has been reported for pharmacophore development and drug design.[18] For example, Pinto et al. implemented MD simulations of Bcl-xL complexes to calculate average positions of critical amino acids involved in ligand binding.[19] Carlson et al. implemented molecular dynamics simulations to identify compounds that complement a receptor while causing minimal disruption to the conformation and flexibility of the active site. Snapshots of the protein at constant time intervals during the simulation were overlaid to identify conserved binding regions (conserved in at least three out of eleven frames) for pharmacophore development. Spyrakis et al. relied on a workflow of MD simulations, fingerprints for ligands and proteins (FLAP) and linear discriminant analysis (LDA) to identify the best ligand-protein conformations to act as pharmacophore templates based on retrospective ROC analysis of the resulting pharmacophores. In an attempt to ameliorate structure-based drug discovery modeling, vis-à-vis the need for many modeled compounds, Hatmal et al. proposed a combination of MD simulation and ligand-receptor intermolecular contacts analysis to discern critical intermolecular contacts (binding interactions) from redundant ones in a single ligand–protein complex. Critical contacts can then be converted into pharmacophore models that can be used for virtual screening.[20]
An important factor is intramolecular
Design constraints
The design of a molecular dynamics simulation should account for the available computational power. Simulation size (n = number of particles), timestep, and total time duration must be selected so that the calculation can finish within a reasonable time period. However, the simulations should be long enough to be relevant to the time scales of the natural processes being studied. To make statistically valid conclusions from the simulations, the time span simulated should match the kinetics of the natural process. Otherwise, it is analogous to making conclusions about how a human walks when only looking at less than one footstep. Most scientific publications about the dynamics of proteins and DNA[23][24] use data from simulations spanning nanoseconds (10−9 s) to microseconds (10−6 s). To obtain these simulations, several CPU-days to CPU-years are needed. Parallel algorithms allow the load to be distributed among CPUs; an example is the spatial or force decomposition algorithm.[25]
During a classical MD simulation, the most CPU intensive task is the evaluation of the potential as a function of the particles' internal coordinates. Within that energy evaluation, the most expensive one is the non-bonded or non-covalent part. In big O notation, common molecular dynamics simulations scale by if all pair-wise
Another factor that impacts total CPU time needed by a simulation is the size of the integration timestep. This is the time length between evaluations of the potential. The timestep must be chosen small enough to avoid
For simulating molecules in a
In all kinds of molecular dynamics simulations, the simulation box size must be large enough to avoid
Microcanonical ensemble (NVE)
In the microcanonical ensemble, the system is isolated from changes in moles (N), volume (V), and energy (E). It corresponds to an adiabatic process with no heat exchange. A microcanonical molecular dynamics trajectory may be seen as an exchange of potential and kinetic energy, with total energy being conserved. For a system of N particles with coordinates and velocities , the following pair of first order differential equations may be written in
The potential energy function of the system is a function of the particle coordinates . It is referred to simply as the potential in physics, or the force field in chemistry. The first equation comes from Newton's laws of motion; the force acting on each particle in the system can be calculated as the negative gradient of .
For every time step, each particle's position and velocity may be integrated with a symplectic integrator method such as Verlet integration. The time evolution of and is called a trajectory. Given the initial positions (e.g., from theoretical knowledge) and velocities (e.g., randomized
One frequent source of confusion is the meaning of temperature in MD. Commonly we have experience with macroscopic temperatures, which involve a huge number of particles, but temperature is a statistical quantity. If there is a large enough number of atoms, statistical temperature can be estimated from the instantaneous temperature, which is found by equating the kinetic energy of the system to nkBT/2, where n is the number of degrees of freedom of the system.
A temperature-related phenomenon arises due to the small number of atoms that are used in MD simulations. For example, consider simulating the growth of a copper film starting with a substrate containing 500 atoms and a deposition energy of 100 eV. In the real world, the 100 eV from the deposited atom would rapidly be transported through and shared among a large number of atoms ( or more) with no big change in temperature. When there are only 500 atoms, however, the substrate is almost immediately vaporized by the deposition. Something similar happens in biophysical simulations. The temperature of the system in NVE is naturally raised when macromolecules such as proteins undergo exothermic conformational changes and binding.
Canonical ensemble (NVT)
In the canonical ensemble, amount of substance (N), volume (V) and temperature (T) are conserved. It is also sometimes called constant temperature molecular dynamics (CTMD). In NVT, the energy of endothermic and exothermic processes is exchanged with a thermostat.
A variety of thermostat algorithms are available to add and remove energy from the boundaries of an MD simulation in a more or less realistic way, approximating the canonical ensemble. Popular methods to control temperature include velocity rescaling, the Nosé–Hoover thermostat, Nosé–Hoover chains, the Berendsen thermostat, the Andersen thermostat and Langevin dynamics. The Berendsen thermostat might introduce the flying ice cube effect, which leads to unphysical translations and rotations of the simulated system.
It is not trivial to obtain a canonical ensemble distribution of conformations and velocities using these algorithms. How this depends on system size, thermostat choice, thermostat parameters, time step and integrator is the subject of many articles in the field.
Isothermal–isobaric (NPT) ensemble
In the isothermal–isobaric ensemble, amount of substance (N), pressure (P) and temperature (T) are conserved. In addition to a thermostat, a barostat is needed. It corresponds most closely to laboratory conditions with a flask open to ambient temperature and pressure.
In the simulation of
Generalized ensembles
The
Potentials in MD simulations
A molecular dynamics simulation requires the definition of a potential function, or a description of the terms by which the particles in the simulation will interact. In chemistry and biology this is usually referred to as a force field and in materials physics as an interatomic potential. Potentials may be defined at many levels of physical accuracy; those most commonly used in chemistry are based on molecular mechanics and embody a classical mechanics treatment of particle-particle interactions that can reproduce structural and conformational changes but usually cannot reproduce chemical reactions.
The reduction from a fully quantum description to a classical potential entails two main approximations. The first one is the Born–Oppenheimer approximation, which states that the dynamics of electrons are so fast that they can be considered to react instantaneously to the motion of their nuclei. As a consequence, they may be treated separately. The second one treats the nuclei, which are much heavier than electrons, as point particles that follow classical Newtonian dynamics. In classical molecular dynamics, the effect of the electrons is approximated as one potential energy surface, usually representing the ground state.
When finer levels of detail are needed, potentials based on quantum mechanics are used; some methods attempt to create hybrid classical/quantum potentials where the bulk of the system is treated classically but a small region is treated as a quantum system, usually undergoing a chemical transformation.
Empirical potentials
Empirical potentials used in chemistry are frequently called force fields, while those used in materials physics are called interatomic potentials.
Most
Because of the non-local nature of non-bonded interactions, they involve at least weak interactions between all particles in the system. Its calculation is normally the bottleneck in the speed of MD simulations. To lower the computational cost, force fields employ numerical approximations such as shifted cutoff radii, reaction field algorithms, particle mesh Ewald summation, or the newer particle–particle-particle–mesh (P3M).
Chemistry force fields commonly employ preset bonding arrangements (an exception being
Pair potentials versus many-body potentials
The potential functions representing the non-bonded energy are formulated as a sum over interactions between the particles of the system. The simplest choice, employed in many popular force fields, is the "pair potential", in which the total potential energy can be calculated from the sum of energy contributions between pairs of atoms. Therefore, these force fields are also called "additive force fields". An example of such a pair potential is the non-bonded Lennard-Jones potential (also termed the 6–12 potential), used for calculating van der Waals forces.
Another example is the Born (ionic) model of the ionic lattice. The first term in the next equation is Coulomb's law for a pair of ions, the second term is the short-range repulsion explained by Pauli's exclusion principle and the final term is the dispersion interaction term. Usually, a simulation only includes the dipolar term, although sometimes the quadrupolar term is also included.[37][38] When nl = 6, this potential is also called the Coulomb–Buckingham potential.
In
Semi-empirical potentials
There are a wide variety of semi-empirical potentials, termed
Polarizable potentials
Most classical force fields implicitly include the effect of polarizability, e.g., by scaling up the partial charges obtained from quantum chemical calculations. These partial charges are stationary with respect to the mass of the atom. But molecular dynamics simulations can explicitly model polarizability with the introduction of induced dipoles through different methods, such as Drude particles or fluctuating charges. This allows for a dynamic redistribution of charge between atoms which responds to the local chemical environment.
For many years, polarizable MD simulations have been touted as the next generation. For homogenous liquids such as water, increased accuracy has been achieved through the inclusion of polarizability.[43][44][45] Some promising results have also been achieved for proteins.[46][47] However, it is still uncertain how to best approximate polarizability in a simulation.[citation needed] The point becomes more important when a particle experiences different environments during its simulation trajectory, e.g. translocation of a drug through a cell membrane.[48]
Potentials in ab initio methods
In classical molecular dynamics, one potential energy surface (usually the ground state) is represented in the force field. This is a consequence of the Born–Oppenheimer approximation. In excited states, chemical reactions or when a more accurate representation is needed, electronic behavior can be obtained from first principles using a quantum mechanical method, such as density functional theory. This is named Ab Initio Molecular Dynamics (AIMD). Due to the cost of treating the electronic degrees of freedom, the computational burden of these simulations is far higher than classical molecular dynamics. For this reason, AIMD is typically limited to smaller systems and shorter times.
Hybrid QM/MM
QM (quantum-mechanical) methods are very powerful. However, they are computationally expensive, while the MM (classical or molecular mechanics) methods are fast but suffer from several limits (require extensive parameterization; energy estimates obtained are not very accurate; cannot be used to simulate reactions where covalent bonds are broken/formed; and are limited in their abilities for providing accurate details regarding the chemical environment). A new class of method has emerged that combines the good points of QM (accuracy) and MM (speed) calculations. These methods are termed mixed or hybrid quantum-mechanical and molecular mechanics methods (hybrid QM/MM).[49]
The most important advantage of hybrid QM/MM method is the speed. The cost of doing classical molecular dynamics (MM) in the most straightforward case scales O(n2), where n is the number of atoms in the system. This is mainly due to electrostatic interactions term (every particle interacts with every other particle). However, use of cutoff radius, periodic pair-list updates and more recently the variations of the particle-mesh Ewald's (PME) method has reduced this to between O(n) to O(n2). In other words, if a system with twice as many atoms is simulated then it would take between two and four times as much computing power. On the other hand, the simplest ab initio calculations typically scale O(n3) or worse (restricted
In more sophisticated implementations, QM/MM methods exist to treat both light nuclei susceptible to quantum effects (such as hydrogens) and electronic states. This allows generating hydrogen wave-functions (similar to electronic wave-functions). This methodology has been useful in investigating phenomena such as hydrogen tunneling. One example where QM/MM methods have provided new discoveries is the calculation of hydride transfer in the enzyme liver
Coarse-graining and reduced representations
At the other end of the detail scale are coarse-grained and lattice models. Instead of explicitly representing every atom of the system, one uses "pseudo-atoms" to represent groups of atoms. MD simulations on very large systems may require such large computer resources that they cannot easily be studied by traditional all-atom methods. Similarly, simulations of processes on long timescales (beyond about 1 microsecond) are prohibitively expensive, because they require so many time steps. In these cases, one can sometimes tackle the problem by using reduced representations, which are also called coarse-grained models.[51]
Examples for coarse graining (CG) methods are discontinuous molecular dynamics (CG-DMD)[52][53] and Go-models.[54] Coarse-graining is done sometimes taking larger pseudo-atoms. Such united atom approximations have been used in MD simulations of biological membranes. Implementation of such approach on systems where electrical properties are of interest can be challenging owing to the difficulty of using a proper charge distribution on the pseudo-atoms.[55] The aliphatic tails of lipids are represented by a few pseudo-atoms by gathering 2 to 4 methylene groups into each pseudo-atom.
The parameterization of these very coarse-grained models must be done empirically, by matching the behavior of the model to appropriate experimental data or all-atom simulations. Ideally, these parameters should account for both enthalpic and entropic contributions to free energy in an implicit way.[56] When coarse-graining is done at higher levels, the accuracy of the dynamic description may be less reliable. But very coarse-grained models have been used successfully to examine a wide range of questions in structural biology, liquid crystal organization, and polymer glasses.
Examples of applications of coarse-graining:
- protein folding and protein structure prediction studies are often carried out using one, or a few, pseudo-atoms per amino acid;[51]
- liquid crystal phase transitions have been examined in confined geometries and/or during flow using the Gay-Berne potential, which describes anisotropic species;
- Polymer glasses during deformation have been studied using simple harmonic or FENE springs to connect spheres described by the Lennard-Jones potential;
- DNA supercoilinghas been investigated using 1–3 pseudo-atoms per basepair, and at even lower resolution;
- Packaging of double-helical DNA into bacteriophage has been investigated with models where one pseudo-atom represents one turn (about 10 basepairs) of the double helix;
- RNA structure in the ribosome and other large systems has been modeled with one pseudo-atom per nucleotide.
The simplest form of coarse-graining is the united atom (sometimes called extended atom) and was used in most early MD simulations of proteins, lipids, and nucleic acids. For example, instead of treating all four atoms of a CH3 methyl group explicitly (or all three atoms of CH2 methylene group), one represents the whole group with one pseudo-atom. It must, of course, be properly parameterized so that its van der Waals interactions with other groups have the proper distance-dependence. Similar considerations apply to the bonds, angles, and torsions in which the pseudo-atom participates. In this kind of united atom representation, one typically eliminates all explicit hydrogen atoms except those that have the capability to participate in hydrogen bonds (polar hydrogens). An example of this is the CHARMM 19 force-field.
The polar hydrogens are usually retained in the model, because proper treatment of hydrogen bonds requires a reasonably accurate description of the directionality and the electrostatic interactions between the donor and acceptor groups. A hydroxyl group, for example, can be both a hydrogen bond donor, and a hydrogen bond acceptor, and it would be impossible to treat this with one OH pseudo-atom. About half the atoms in a protein or nucleic acid are non-polar hydrogens, so the use of united atoms can provide a substantial savings in computer time.
Incorporating solvent effects
In many simulations of a solute-solvent system the main focus is on the behavior of the solute with little interest of the solvent behavior particularly in those solvent molecules residing in regions far from the solute molecule.[57] Solvents may influence the dynamic behavior of solutes via random collisions and by imposing a frictional drag on the motion of the solute through the solvent. The use of non-rectangular periodic boundary conditions, stochastic boundaries and solvent shells can all help reduce the number of solvent molecules required and enable a larger proportion of the computing time to be spent instead on simulating the solute. It is also possible to incorporate the effects of a solvent without needing any explicit solvent molecules present. One example of this approach is to use a potential mean force (PMF) which describes how the free energy changes as a particular coordinate is varied. The free energy change described by PMF contains the averaged effects of the solvent.
Without incorporating the effects of solvent simulations of macromolecules (such as proteins) may yield unrealistic behavior and even small molecules may adopt more compact conformations due to favourable van der Waals forces and electrostatic interactions which would be dampened in the presence of a solvent.[58]
Long-range forces
A long range interaction is an interaction in which the spatial interaction falls off no faster than where is the dimensionality of the system. Examples include charge-charge interactions between ions and dipole-dipole interactions between molecules. Modelling these forces presents quite a challenge as they are significant over a distance which may be larger than half the box length with simulations of many thousands of particles. Though one solution would be to significantly increase the size of the box length, this brute force approach is less than ideal as the simulation would become computationally very expensive. Spherically truncating the potential is also out of the question as unrealistic behaviour may be observed when the distance is close to the cut off distance.[59]
Steered molecular dynamics (SMD)
Steered molecular dynamics (SMD) simulations, or force probe simulations, apply forces to a protein in order to manipulate its structure by pulling it along desired degrees of freedom. These experiments can be used to reveal structural changes in a protein at the atomic level. SMD is often used to simulate events such as mechanical unfolding or stretching.[60]
There are two typical protocols of SMD: one in which pulling velocity is held constant, and one in which applied force is constant. Typically, part of the studied system (e.g., an atom in a protein) is restrained by a harmonic potential. Forces are then applied to specific atoms at either a constant velocity or a constant force. Umbrella sampling is used to move the system along the desired reaction coordinate by varying, for example, the forces, distances, and angles manipulated in the simulation. Through umbrella sampling, all of the system's configurations—both high-energy and low-energy—are adequately sampled. Then, each configuration's change in free energy can be calculated as the potential of mean force.[61] A popular method of computing PMF is through the weighted histogram analysis method (WHAM), which analyzes a series of umbrella sampling simulations.[62][63]
A lot of important applications of SMD are in the field of drug discovery and biomolecular sciences. For e.g. SMD was used to investigate the stability of Alzheimer's protofibrils,[64] to study the protein ligand interaction in cyclin-dependent kinase 5[65] and even to show the effect of electric field on thrombin (protein) and aptamer (nucleotide) complex[66] among many other interesting studies.
Examples of applications
Molecular dynamics is used in many fields of science.
- First MD simulation of a simplified biological folding process was published in 1975. Its simulation published in Nature paved the way for the vast area of modern computational protein-folding.[68]
- First MD simulation of a biological process was published in 1976. Its simulation published in Nature paved the way for understanding protein motion as essential in function and not just accessory.[69]
- MD is the standard method to treat ion irradiation have on solids and solid surfaces.[70]
The following biophysical examples illustrate notable efforts to produce simulations of a systems of very large size (a complete virus) or very long simulation times (up to 1.112 milliseconds):
- MD simulation of the full satellite tobacco mosaic virus (STMV) (2006, Size: 1 million atoms, Simulation time: 50 ns, program: NAMD) This virus is a small, icosahedral plant virus that worsens the symptoms of infection by Tobacco Mosaic Virus (TMV). Molecular dynamics simulations were used to probe the mechanisms of viral assembly. The entire STMV particle consists of 60 identical copies of one protein that make up the viral capsid (coating), and a 1063 nucleotide single stranded RNA genome. One key finding is that the capsid is very unstable when there is no RNA inside. The simulation would take one 2006 desktop computer around 35 years to complete. It was thus done in many processors in parallel with continuous communication between them.[71]
- Folding simulations of the Vijay Pande at Stanford University. The kinetic properties of the Villin Headpiece protein were probed by using many independent, short trajectories run by CPU's without continuous real-time communication. One method employed was the Pfold value analysis, which measures the probability of folding before unfolding of a specific starting conformation. Pfold gives information about transition state structures and an ordering of conformations along the folding pathway. Each trajectory in a Pfold calculation can be relatively short, but many independent trajectories are needed.[72]
- Long continuous-trajectory simulations have been performed on Anton, a massively parallel supercomputer designed and built around custom application-specific integrated circuits (ASICs) and interconnects by D. E. Shaw Research. The longest published result of a simulation performed using Anton is a 1.112-millisecond simulation of NTL9 at 355 K; a second, independent 1.073-millisecond simulation of this configuration was also performed (and many other simulations of over 250 μs continuous chemical time).[73] In How Fast-Folding Proteins Fold, researchers Kresten Lindorff-Larsen, Stefano Piana, Ron O. Dror, and David E. Shaw discuss "the results of atomic-level molecular dynamics simulations, over periods ranging between 100 μs and 1 ms, that reveal a set of common principles underlying the folding of 12 structurally diverse proteins." Examination of these diverse long trajectories, enabled by specialized, custom hardware, allow them to conclude that "In most cases, folding follows a single dominant route in which elements of the native structure appear in an order highly correlated with their propensity to form in the unfolded state."[73] In a separate study, Anton was used to conduct a 1.013-millisecond simulation of the native-state dynamics of bovine pancreatic trypsin inhibitor (BPTI) at 300 K.[74]
Another important application of MD method benefits from its ability of 3-dimensional characterization and analysis of microstructural evolution at atomic scale.
- MD simulations are used in characterization of grain size evolution, for example, when describing wear and friction of nanocrystalline Al and Al(Zr) materials.[75] Dislocations evolution and grain size evolution are analyzed during the friction process in this simulation. Since MD method provided the full information of the microstructure, the grain size evolution was calculated in 3D using the Polyhedral Template Matching,[76] Grain Segmentation,[77] and Graph clustering[78] methods. In such simulation, MD method provided an accurate measurement of grain size. Making use of these information, the actual grain structures were extracted, measured, and presented. Compared to the traditional method of using SEM with a single 2-dimensional slice of the material, MD provides a 3-dimensional and accurate way to characterize the microstructural evolution at atomic scale.
Molecular dynamics algorithms
Integrators
- Symplectic integrator
- Verlet–Stoermer integration
- Runge–Kutta integration
- Beeman's algorithm
- Constraint algorithms(for constrained systems)
Short-range interaction algorithms
- Cell lists
- Verlet list
- Bonded interactions
Long-range interaction algorithms
- Ewald summation
- Particle mesh Ewald summation (PME)
- Particle–particle-particle–mesh (P3M)
- Shifted force method
Parallelization strategies
- Domain decomposition method (Distribution of system data for parallel computing)
Ab-initio molecular dynamics
Specialized hardware for MD simulations
- Anton – A specialized, massively parallel supercomputer designed to execute MD simulations
- MDGRAPE– A special purpose system built for molecular dynamics simulations, especially protein structure prediction
Graphics card as a hardware for MD simulations
Molecular modeling on GPU is the technique of using a graphics processing unit (GPU) for molecular simulations.[79]
In 2007,See also
- Molecular modeling
- Computational chemistry
- Force field (chemistry)
- Comparison of force field implementations
- Monte Carlo method
- Molecular design software
- Molecular mechanics
- Multiscale Green's function
- Car–Parrinello method
- Comparison of software for molecular mechanics modeling
- Quantum chemistry
- Discrete element method
- Comparison of nucleic acid simulation software
- Molecule editor
- Mixed quantum-classical dynamics
References
- ISBN 978-0-387-94838-6.
- S2CID 178710030.
- ^ Fermi E., Pasta J., Ulam S., Los Alamos report LA-1940 (1955).
- .
- .
- .
- S2CID 204545481.
- S2CID 204512243.
- .
- ISSN 0021-9606.
- S2CID 266440296.
- S2CID 95716947.
- S2CID 119199372.
- S2CID 3162636.
- S2CID 10613106.
- PMID 22754404.
- PMID 24463371.
- PMID 25751016.
- S2CID 11339000.
- S2CID 11561853.
- PMID 8889177.
- ^ a b Israelachvili J (1992). Intermolecular and surface forces. San Diego: Academic Press.
- S2CID 15149133.
- .
- ^ Plimpton S. "Molecular Dynamics - Parallel Algorithms". sandia.gov.
- .
- doi:10.1063/1.460259.
- S2CID 488073.
- .
- S2CID 263455009.
- .
- .
- from the original on September 22, 2017.
- .
- S2CID 250760409.
- .
- PMID 16375450.
- .
- ^ S2CID 14585375.
- PMID 9948964.
- .
- PMID 10006745.
- .
- PMID 25941394.
- PMID 25715668.
- PMID 21688848.
- S2CID 16741310.
- S2CID 248696332.
- The Pennsylvania State University), Donald Truhlar and Jiali Gao (University of Minnesota) and Kenneth Merz (University of Florida).
- PMID 11697969.
- ^ PMID 27333362.
- S2CID 21774752.
- S2CID 17254380.
- PMID 12496075.
- .
- PMID 26723589.
- .
- OCLC 45008511.
- .
- ISBN 978-1-61737-525-5.
- ISBN 978-981-256-742-0.
- S2CID 8571486.
- .
- PMID 20055378.
- PMID 24437446.
- PMID 27874042.
- PMID 25078022.
- S2CID 4211714.
- S2CID 4161081.
- ^ Smith, R., ed. (1997). Atomic & ion collisions in solids and at surfaces: theory, simulation and applications. Cambridge, UK: Cambridge University Press.[page needed]
- ^ Freddolino P, Arkhipov A, Larson SB, McPherson A, Schulten K. "Molecular dynamics simulation of the Satellite Tobacco Mosaic Virus (STMV)". Theoretical and Computational Biophysics Group. University of Illinois at Urbana Champaign.
- PMID 16674165.
- ^ S2CID 27988268.
- S2CID 3495023.
- S2CID 224954349.
- S2CID 53980652.
- S2CID 118371554.
- arXiv:1806.01664 [cs.SI].
- S2CID 15313533.
General references
- Allen MP, Tildesley DJ (1989). Computer simulation of liquids. Oxford University Press. ISBN 0-19-855645-4.
- McCammon JA, Harvey SC (1987). Dynamics of Proteins and Nucleic Acids. Cambridge University Press. ISBN 0-521-30750-3.
- Rapaport DC (1996). The Art of Molecular Dynamics Simulation. ISBN 0-521-44561-2.
- ISBN 978-3-540-68094-9.
- ISBN 978-0-12-267351-1.
- Haile JM (2001). Molecular Dynamics Simulation: Elementary Methods. Wiley. ISBN 0-471-18439-X.
- Sadus RJ (2002). Molecular Simulation of Fluids: Theory, Algorithms and Object-Orientation. Elsevier. ISBN 0-444-51082-6.
- Becker OM, Mackerell Jr AD, Roux B, Watanabe M (2001). Computational Biochemistry and Biophysics. Marcel Dekker. ISBN 0-8247-0455-X.
- Leach A (2001). Molecular Modelling: Principles and Applications (2nd ed.). Prentice Hall. ISBN 978-0-582-38210-7.
- ISBN 0-387-95404-X.
- ISBN 0-444-88192-1.
- Evans DJ, Morriss G (2008). Statistical Mechanics of Nonequilibrium Liquids (Second ed.). Cambridge University Press. ISBN 978-0-521-85791-8.