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Force field (chemistry)

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For other uses, see Force field.
A force field is used to minimize the bond stretching energy of this ethane molecule.

In the context of molecular modelling, a force field (a special case of energy functions or interatomic potentials; not to be confused with force field in classical physics) refers to the functional form and parameter sets used to calculate the potential energy of a system of atoms or coarse-grained particles in molecular mechanics and molecular dynamics simulations. The parameters of the energy functions may be derived from experiments in physics or chemistry, calculations in quantum mechanics, or both.

All-atom force fields provide parameters for every type of atom in a system, including hydrogen, while united-atom interatomic potentials treat the hydrogen and carbon atoms in each methyl group (terminal methyl) and each methylene bridge as one interaction center. Coarse-grained potentials, which are often used in long-time simulations of macromolecules such as proteins, nucleic acids, and multi-component complexes, provide even cruder representations for higher computing efficiency.

Functional form

Molecular mechanics potential energy function with continuum solvent.

The basic functional form of potential energy in molecular mechanics includes bonded terms for interactions of atoms that are linked by covalent bonds, and nonbonded (also termed noncovalent) terms that describe the long-range electrostatic and van der Waals forces. The specific decomposition of the terms depends on the force field, but a general form for the total energy in an additive force field can be written as where the components of the covalent and noncovalent contributions are given by the following summations:

The bond and angle terms are usually modeled by quadratic energy functions that do not allow bond breaking. A more realistic description of a covalent bond at higher stretching is provided by the more expensive Morse potential. The functional form for dihedral energy is highly variable. Additional, "improper torsional" terms may be added to enforce the planarity of aromatic rings and other conjugated systems, and "cross-terms" that describe coupling of different internal variables, such as angles and bond lengths. Some force fields also include explicit terms for hydrogen bonds.

The nonbonded terms are most computationally intensive. A popular choice is to limit interactions to pairwise energies. The van der Waals term is usually computed with a Lennard-Jones potential and the electrostatic term with Coulomb's law, although both can be buffered or scaled by a constant factor to account for electronic polarizability.

Bond stretching

As it is rare for bonds to deviate significantly from their reference values the Morse potential is seldom employed for molecular mechanics due to it not being efficient to compute. The most simplistic approaches utilize a Hooke's law formula:

Where is the force constant, is the bond length and is the value for the bond length when all other terms in the force field are set to 0. The term is often referred to as the equilibrium bond length which may cause confusion. The equilibrium bond length would be the value adopted in a minimum energy structure with all other terms contributing.

In most cases the effects on accuracy of modelling a bond as a harmonic oscillator are small however a real bond stretching potential is not harmonic and modelling it as such may lead to inaccuracies in predictions of bond lengths to the thousandth of an angstrom. The strong nature of the interactions between atoms yields large values for the force constant (the stronger the bond the higher the value of the force constant).

Though the Hooke's law formula provides a reasonable level of accuracy at bond lengths near the equilibrium distance it is less accurate as one moves away. In order to model the Morse curve better one can employ cubic and higher powers.[1]

Parametrization

In addition to the functional form of the potentials, force fields define a set of parameters for different types of atoms, chemical bonds, dihedral angles and so on. The parameter sets are usually empirical. A force field would include distinct parameters for an oxygen atom in a carbonyl functional group and in a hydroxyl group. The typical parameter set includes values for atomic mass, van der Waals radius, and partial charge for individual atoms, and equilibrium values of bond lengths, bond angles, and dihedral angles for pairs, triplets, and quadruplets of bonded atoms, and values corresponding to the effective spring constant for each potential. Most current force fields parameters use a fixed-charge model by which each atom is assigned one value for the atomic charge that is not affected by the local electrostatic environment; proposed developments in next-generation force fields incorporate models for polarizability, in which a particle's charge is influenced by electrostatic interactions with its neighbors. For example, polarizability can be approximated by the introduction of induced dipoles; it can also be represented by Drude particles, massless, charge-carrying virtual sites attached by a springlike harmonic oscillator potential to each polarizable atom. The introduction of polarizability into force fields in common use has been inhibited by the high computational expense associated with calculating the local electrostatic field.

Although many molecular simulations involve biological macromolecules such as proteins, DNA, and RNA, the parameters for given atom types are generally derived from observations on small organic molecules that are more tractable for experimental studies and quantum calculations. Different force field parameters can be derived from dissimilar types of experimental data, such as enthalpy of vaporization (OPLS), enthalpy of sublimation, dipole moments, or various spectroscopic parameters.

Parameter sets and functional forms are defined by interatomic potentials developers to be self-consistent. Because the functional forms of the potential terms vary extensively between even closely related interatomic potentials (or successive versions of the same interatomic potential), the parameters from one interatomic potential function should clearly never be used together with another interatomic potential function.

There are many approaches to parameterisation of a forcefield. The main classical forcefields are typically paramaterized through a complex process often using approximate quantum mechanics as a basis and modifying potentials to most accurately match experimental observables. [2] [3] [4] More recently a variety of automated tools have been provided to parameterize new force fields and to assist users to develop their own parameter sets for chemistries which are not parameterized to date. [5] [6] Efforts are underway within the community to provide open source codes and methods such as openMM and openMD.

Deficiencies

All interatomic potentials are based on many approximations and derived from different types of experimental data. Thus, they are termed empirical. Most force fields rely on point charges in reproducing the electrostatic potential around molecules. This leads to substantial failures for anisotropic charge distributions.[7] Some existing energy functions do not account for electronic polarization of the environment, an effect that can significantly reduce electrostatic interactions of partial atomic charges. This problem was addressed by developing polarizable force fields[8][9] or using macroscopic dielectric constant. However, application of one value of dielectric constant is questionable in the highly heterogeneous environments of proteins or biological membranes, and the nature of the dielectric depends on the model used.[10]

All types of van der Waals forces are also strongly environment-dependent, because these forces originate from interactions of induced and "instantaneous" dipoles (see Intermolecular force). The original Fritz London theory of these forces can only be applied in vacuum. A more general theory of van der Waals forces in condensed media was developed by A. D. McLachlan in 1963 (this theory includes the original London's approach as a special case).[11] The McLachlan theory predicts that van der Waals attractions in media are weaker than in vacuum and follow the like dissolves like rule, which means that different types of atoms interact more weakly than identical types of atoms.[12] This is in contrast to combinatorial rules or Slater-Kirkwood equation applied for development of the classical force fields. The combinatorial rules state that interaction energy of two dissimilar atoms (e.g., C…N) is an average of the interaction energies of corresponding identical atom pairs (i.e., C…C and N…N). According to McLachlan theory, the interactions of particles in a media can even be fully repulsive, as observed for liquid helium.[11] The conclusions of McLachlan theory are supported by direct measurements of attraction forces between different materials (Hamaker constant), as explained by Jacob Israelachvili in his book Intermolecular and surface forces. It was concluded that "the interaction between hydrocarbons across water is about 10% of that across vacuum".[11] Such effects are unaccounted in standard molecular mechanics.

Another round of criticism came from practical applications, such as protein structure refinement. It was noted that Critical Assessment of protein Structure Prediction (CASP) participants did not try to refine their models to avoid "a central embarrassment of molecular mechanics, namely that energy minimization or molecular dynamics generally leads to a model that is less like the experimental structure".[13] The force fields have been applied successfully for protein structure refinement in different X-ray crystallography and NMR spectroscopy applications, especially using program XPLOR.[14] However, such refinement is driven mainly by a set of experimental constraints, whereas the interatomic potentials serve merely to remove interatomic hindrances. The results of calculations are practically the same with rigid sphere potentials implemented in program DYANA[15] (calculations from NMR data), or with programs for crystallographic refinement that do not use any energy functions. The deficiencies of the interatomic potentials remain a major bottleneck in homology modeling of proteins.[16] Such situation gave rise to development of alternative empirical scoring functions specifically for ligand docking,[17] protein folding,[18][19][20] homology model refinement,[21] computational protein design,[22][23][24] and modeling of proteins in membranes.[25]

There is also an opinion that molecular mechanics may operate with energy which is irrelevant to protein folding or ligand binding.[26] The parameters of typical force fields reproduce enthalpy of sublimation, i.e., energy of evaporation of molecular crystals. However, it was recognized that protein folding and ligand binding are thermodynamically very similar to crystallization, or liquid-solid transitions, because all these processes represent freezing of mobile molecules in condensed media.[27][28][29] Thus, free energy changes during protein folding or ligand binding are expected to represent a combination of an energy similar to heat of fusion (energy absorbed during melting of molecular crystals), a conformational entropy contribution, and solvation free energy. The heat of fusion is significantly smaller than enthalpy of sublimation.[11] Hence, the potentials describing protein folding or ligand binding must be weaker than potentials in molecular mechanics. Indeed, the energies of H-bonds in proteins are ~ -1.5 kcal/mol when estimated from protein engineering or alpha helix to coil transition data,[30][31] but the same energies estimated from sublimation enthalpy of molecular crystals were -4 to -6 kcal/mol.[32] The depths of modified Lennard-Jones potentials derived from protein engineering data were also smaller than in typical potential parameters and followed the like dissolves like rule, as predicted by McLachlan theory.[26]

Further information: Comparison of force field implementations, Molecular design software, and Comparison of software for molecular mechanics modeling

Different force fields are designed for different purposes. All are implemented in various computer software.

MM2 was developed by Norman Allinger mainly for conformational analysis of hydrocarbons and other small organic molecules. It is designed to reproduce the equilibrium covalent geometry of molecules as precisely as possible. It implements a large set of parameters that is continuously refined and updated for many different classes of organic compounds (MM3 and MM4).[33][34][35][36][37]

CFF was developed by Arieh Warshel, Lifson and coworkers as a general method for unifying studies of energies, structures and vibration of general molecules and molecular crystals. The CFF program, developed by Levitt and Warshel, is based on the Cartesian representation of all the atoms, and it served as the basis for many subsequent simulation programs.

ECEPP was developed specifically for modeling of peptides and proteins. It uses fixed geometries of amino acid residues to simplify the potential energy surface. Thus, the energy minimization is conducted in the space of protein torsion angles. Both MM2 and ECEPP include potentials for H-bonds and torsion potentials for describing rotations around single bonds. ECEPP/3 was implemented (with some modifications) in Internal Coordinate Mechanics and FANTOM.[38]

AMBER, CHARMM, and GROMOS have been developed mainly for molecular dynamics of macromolecules, although they are also commonly used for energy minimizing. Thus, the coordinates of all atoms are considered as free variables.

Classical force fields

  • Assisted Model Building and Energy Refinement (AMBER) – widely used for proteins and DNA.
  • Chemistry at HARvard Molecular Mechanics (CHARMM) – originally developed at Harvard, widely used for both small molecules and macromolecules
  • CVFF – also used broadly for small molecules and macromolecules.[citation needed]
  • COSMOS-NMR – hybrid QM/MM force field adapted to a variety of inorganic compounds, organic compounds and biological macromolecules, including semi-empirical calculation of atomic charges and NMR properties. COSMOS-NMR is optimized for NMR based structure elucidation and implemented in COSMOS molecular modelling package.[39]
  • GROningen MOlecular Simulation (GROMOS) – a force field that comes as part of the GROMOS software, a general-purpose molecular dynamics computer simulation package for the study of biomolecular systems.[40] GROMOS force field A-version has been developed for application to aqueous or apolar solutions of proteins, nucleotides, and sugars. A B-version to simulate gas phase isolated molecules is also available.
  • Optimized Potential for Liquid Simulations (OPLS, variants include OPLS-AA, OPLS-UA, OPLS-2001, OPLS-2005) – developed by William L. Jorgensen at the Yale University Department of Chemistry.
  • ECEPP[41] – first force field for polypeptide molecules - developed by F.A. Momany, H.A. Scheraga and colleagues.[42][43]
  • QCFF/PI – A general force fields for conjugated molecules.[44][45]
  • Universal Force Field (UFF) – A general force field with parameters for the full periodic table up to and including the actinoids, developed at Colorado State University.[46]
  • Consistent Force Field (CFF) – a family of forcefields adapted to a broad variety of organic compounds, includes force fields for polymers, metals, etc.
  • Merck Molecular Force Field (MMFF) – developed at Merck, for a broad range of molecules.

Polarizable force fields

  • CFF/ind and ENZYMIX – The first polarizable force field[47] which has subsequently been used in many applications to biological systems.[9]
  • DRF90 developed by P. Th. van Duijnen and coworkers.[48]
  • PIPF – The polarizable intermolecular potential for fluids is an induced point-dipole force field for organic liquids and biopolymers. The molecular polarization is based on Thole's interacting dipole (TID) model and was developed by Jiali Gao [1] at the University of Minnesota.[49][50]
  • Polarizable Force Field (PFF) – developed by Richard A. Friesner and coworkers.[citation needed]
  • SP-basis Chemical Potential Equalization (CPE) – approach developed by R. Chelli and P. Procacci.[51]
  • PHAST - polarizable potential developed by Chris Cioce and coworkers.[52]
  • CHARMM – polarizable force field developed by S. Patel (University of Delaware) and C. L. Brooks III (University of Michigan).[53][54]
  • AMBER – polarizable force field developed by Jim Caldwell and coworkers.[citation needed]
  • CHARMM – polarizable force field based on the classical Drude oscillator developed by A. MacKerell (University of Maryland, Baltimore) and B. Roux (University of Chicago).[55][56]
  • Atomic Multipole Optimized Energetics for Biomolecular Applications (AMOEBA) – force field developed by Pengyu Ren (University of Texas at Austin) and Jay W. Ponder (Washington University).[57]
  • ORIENT – procedure developed by Anthony J. Stone (Cambridge University) and coworkers.[58]
  • Non-Empirical Molecular Orbital (NEMO) – procedure developed by Gunnar Karlström and coworkers at Lund University (Sweden)[59]
  • Gaussian Electrostatic Model (GEM) – a polarizable force field based on Density Fitting developed by Thomas A. Darden and G. Andrés Cisneros at NIEHS; and Jean-Philip Piquemal at Paris VI University.[60][61][62]
  • Polarizable procedure based on the Kim-Gordon approach developed by Jürg Hutter and coworkers (University of Zürich)[citation needed]
  • Computer Simulation of Molecular Structure (COSMOS-NMR) – developed by Ulrich Sternberg and coworkers. Hybrid QM/MM force field enables explicit quantum-mechanical calculation of electrostatic properties using localized bond orbitals with fast BPT formalism.[63] Atomic charge fluctuation is possible in each molecular dynamics step.
  • Atomistic Polarizable Potential for Liquids, Electrolytes, and Polymers(APPLE&P), developed by Oleg Borogin, Dmitry Bedrov and coworkers, which is distributed by Wasatch Molecular Incorporated.[2] [3]

Reactive force fields

  • ReaxFF – reactive force field (interatomic potential) developed by Adri van Duin, William Goddard and coworkers. It is fast, transferable and is the computational method of choice for atomistic-scale dynamical simulations of chemical reactions.[64] Parallelized ReaxFF allows reactive simulations on >>1,000,000 atoms.
  • Empirical valence bond (EVB) – this reactive force field, introduced by Warshel and coworkers, is probably the most reliable and physically consistent way to use force fields in modeling chemical reactions in different environments.[according to whom?] The EVB facilitates calculating activation free energies in condensed phases and in enzymes.

Coarse-grained force fields

  • Virtual atom molecular mechanics (VAMM) – a coarse-grained force field developed by Korkut and Hendrickson for molecular mechanics calculations such as large scale conformational transitions based on the virtual interactions of C-alpha atoms. It is a knowledge based force field and formulated to capture features dependent on secondary structure and on residue-specific contact information in proteins.[65]
  • MARTINI – a coarse-grained potential developed by Marrink and coworkers at the University of Groningen, initially developed for molecular dynamics simulations of lipids,[66] later extended to various other molecules. The force field applies a mapping of four heavy atoms to one CG interaction site and is parameterized with the aim of reproducing thermodynamic properties.
  • SIRAH – a coarse-grained force field developed by Pantano and coworkers of the Biomolecular Simulations Group, Institut Pasteur of Montevideo, Uruguay; developed for molecular dynamics of water, DNA and proteins. Free available for AMBER and GROMACS packages.
  • Dissipative particle dynamics (DPD) - This is a method commonly applied in chemical engineering. It is typically used for studying the hydrodynamics of various simple and complex fluids which require consideration of time and length scales larger than those accessible to classical Molecular dynamics. The potential was originally proposed by Hoogerbrugge and Koelman [67][68] with later modifications by Español and Warren [69] The current state of the art was well documented in a CECAM workshop in 2008.[70] Recently, work has been undertaken to capture some of the chemical subtitles relevant to solutions. This has lead to work considering automated parameterisation of the DPD interaction potentials against experimental observables. [71]

Machine learning models

  • ANI is a transferable neural network potential, built from atomic environment vectors, and able to provide DFT accuracy in terms of energies. [72]
  • FFLUX (originally QCTFF) [73] A set of trained Kriging models which operate together to provide a molecular force field trained on Atoms in molecules or Quantum chemical topology energy terms including electrostatic, exchange and electron correlation.[74] [75]
  • TensorMol a mixed model, a Neural network provides a short range potential, whilst more traditional potentials add screened long range terms.[76]
  • Δ-ML not a force field method but a model that adds learnt correctional energy terms to approximate and relatively computationally cheap quantum chemical methods in order to provide an accuracy level of a higher order, more computationally expensive quantum chemical model.[77]
  • SchNet a Neural network utilising continuous-filter convolutional layers, to predict chemical properties and potential energy surfaces.[78]

Water models

Main article: Water model

The set of parameters used to model water or aqueous solutions (basically a force field for water) is called a water model. Water has attracted a great deal of attention due to its unusual properties and its importance as a solvent. Many water models have been proposed; some examples are TIP3P, TIP4P,[79] SPC, flexible simple point charge water model (flexible SPC), ST2, and mW.[80] Other solvents and methods of solvent representation are also applied within computational chemistry and physics some examples are given on page Solvent model. Recently, novel methods for generating water models have been published.[81]

Post-translational modifications and unnatural amino acids

  • Forcefield_PTM – An AMBER-based forcefield and webtool for modeling common post-translational modifications of amino acids in proteins developed by Chris Floudas and coworkers. It uses the ff03 charge model and has several side-chain torsion corrections parameterized to match the quantum chemical rotational surface.[82]
  • Forcefield_NCAA - An AMBER-based forcefield and webtool for modeling common non-natural amino acids in proteins in condensed-phase simulations using the ff03 charge model.[83] The charges have been reported to be correlated with hydration free energies of corresponding side-chain analogs.[84]

Other

  • VALBOND - a function for angle bending that is based on valence bond theory and works for large angular distortions, hypervalent molecules, and transition metal complexes. It can be incorporated into other force fields such as CHARMM and UFF.
  • Ligand Field Molecular Mechanics (LFMM)[85] - functions for the coordination sphere around transition metals based on the angular overlap model (AOM). Implemented in the Molecular Operating Environment (MOE) as DommiMOE and in Tinker[86]

See also

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Further reading

  • Israelachvili, J. N. (1992). Intermolecular and surface forces. San Diego: Academic Press. ISBN 978-0-12-375181-2. {{cite book}}: Invalid |ref=harv (help)
  • Schlick, T. (2002). Molecular Modeling and Simulation: An Interdisciplinary Guide. Interdisciplinary Applied Mathematics: Mathematical Biology. New York: Springer-Verlag. ISBN 978-0-387-95404-2. {{cite book}}: Invalid |ref=harv (help)
  • Warshel, A. (1991). Computer Modeling of Chemical Reactions in Enzymes and Solutions. New York: John Wiley & Sons. ISBN 978-0-471-53395-5. {{cite book}}: Invalid |ref=harv (help)
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