AMBER

Last updated
Assisted Model Building with Energy Refinement (AMBER)
Original author(s) Peter Kollman, Version1: +Paul Weiner. Version 2: + U Chandra Singh; V3. + David Pearlman, James Caldwell, William Ross, Thomas Cheatham, Stephen Debolt, David Ferguson, George Seibel; Later versions: David Case, Tom Cheatham, Ken Merz, Adrian Roitberg, Carlos Simmerling, Ray Luo, Junmei Wang, Ross Walker
Developer(s) University of California, San Francisco
Initial release1981;43 years ago (1981)
Stable release
Amber23, AmberTools23 [1] / April 21, 2023;15 months ago (2023-04-21)
Written in C, C++, Fortran
Operating system Windows, OS X, Linux, Unix, CNK
Platform x86, Nvidia GPUs, Blue Gene
Size Varies
Available inEnglish
Type Molecular dynamics
License Amber: Proprietary
AmberTools: GPL, public domain, other open-source
Website ambermd.org
AMBER is used to minimize the bond stretching energy of this ethane molecule. Bond stretching energy.png
AMBER is used to minimize the bond stretching energy of this ethane molecule.

Assisted Model Building with Energy Refinement (AMBER) is the name of a widely-used molecular dynamics software package originally developed by Peter Kollman's group at the University of California, San Francisco. It has also, subsequently, come to designate a family of force fields for molecular dynamics of biomolecules that can be used both within the AMBER software suite and with many modern computational platforms.

Contents

The original version of the AMBER software package was written by Paul Weiner as a post-doc in Peter Kollman's laboratory, and was released in 1981. [2]

Subsequently, U Chandra Singh expanded AMBER as a post-doc in Kollman's laboratory, adding molecular dynamics and free energy capabilities.

The next iteration of AMBER was started around 1987 by a group of developers in (and associated with) the Kollman lab, including David Pearlman, David Case, James Caldwell, William Ross, Thomas Cheatham, Stephen DeBolt, David Ferguson, and George Seibel. [3] This team headed development for more than a decade and introduced a variety of improvements, including significant expansion of the free energy capabilities, accommodation for modern parallel and array processing hardware platforms (Cray, Star, etc.), restructuring of the code and revision control for greater maintainability, PME Ewald summations, tools for NMR refinement, and many others.

Currently, AMBER is maintained by an active collaboration between David Case at Rutgers University, Tom Cheatham at the University of Utah, Adrian Roitberg at University of Florida, Ken Merz at Michigan State University, Carlos Simmerling at Stony Brook University, Ray Luo at UC Irvine, and Junmei Wang at University of Pittsburgh.

Force field

The term AMBER force field generally refers to the functional form used by the family of AMBER force fields. This form includes several parameters; each member of the family of AMBER force fields provides values for these parameters and has its own name.

Functional form

The functional form of the AMBER force field is [4]

Despite the term force field, this equation defines the potential energy of the system; the force is the derivative of this potential relative to position.

The meanings of right hand side terms are:

The form of the van der Waals energy is calculated using the equilibrium distance () and well depth (). The factor of ensures that the equilibrium distance is . The energy is sometimes reformulated in terms of , where , as used e.g. in the implementation of the softcore potentials.

The form of the electrostatic energy used here assumes that the charges due to the protons and electrons in an atom can be represented by a single point charge (or in the case of parameter sets that employ lone pairs, a small number of point charges.)

Parameter sets

To use the AMBER force field, it is necessary to have values for the parameters of the force field (e.g. force constants, equilibrium bond lengths and angles, charges). A fairly large number of these parameter sets exist, and are described in detail in the AMBER software user manual. Each parameter set has a name, and provides parameters for certain types of molecules.

Software

The AMBER software suite provides a set of programs to apply the AMBER forcefields to simulations of biomolecules. It is written in the programming languages Fortran 90 and C, with support for most major Unix-like operating systems and compilers. Development is conducted by a loose association of mostly academic labs. New versions are released usually in the spring of even numbered years; AMBER 10 was released in April 2008. The software is available under a site license agreement, which includes full source, currently priced at US$500 for non-commercial and US$20,000 for commercial organizations.

Programs

See also

Related Research Articles

<span class="mw-page-title-main">Computational chemistry</span> Branch of chemistry

Computational chemistry is a branch of chemistry that uses computer simulations to assist in solving chemical problems. It uses methods of theoretical chemistry incorporated into computer programs to calculate the structures and properties of molecules, groups of molecules, and solids. The importance of this subject stems from the fact that, with the exception of some relatively recent findings related to the hydrogen molecular ion, achieving an accurate quantum mechanical depiction of chemical systems analytically, or in a closed form, is not feasible. The complexity inherent in the many-body problem exacerbates the challenge of providing detailed descriptions of quantum mechanical systems. While computational results normally complement information obtained by chemical experiments, it can occasionally predict unobserved chemical phenomena.

<span class="mw-page-title-main">Molecular dynamics</span> Computer simulations to discover and understand chemical properties

Molecular dynamics (MD) is a computer simulation method for analyzing the physical movements of atoms and molecules. The atoms and molecules are allowed to interact for a fixed period of time, giving a view of the dynamic "evolution" of the system. In the most common version, the trajectories of atoms and molecules are determined by numerically solving Newton's equations of motion for a system of interacting particles, where forces between the particles and their potential energies are often calculated using interatomic potentials or molecular mechanical force fields. The method is applied mostly in chemical physics, materials science, and biophysics.

GROningen MOlecular Simulation (GROMOS) is the name of a force field for molecular dynamics simulation, and a related computer software package. Both are developed at the University of Groningen, and at the Computer-Aided Chemistry Group at the Laboratory for Physical Chemistry at the Swiss Federal Institute of Technology (ETH Zurich). At Groningen, Herman Berendsen was involved in its development.

Chemistry at Harvard Macromolecular Mechanics (CHARMM) is the name of a widely used set of force fields for molecular dynamics, and the name for the molecular dynamics simulation and analysis computer software package associated with them. The CHARMM Development Project involves a worldwide network of developers working with Martin Karplus and his group at Harvard to develop and maintain the CHARMM program. Licenses for this software are available, for a fee, to people and groups working in academia.

<span class="mw-page-title-main">Molecular mechanics</span> Use of classical mechanics to model molecular systems

Molecular mechanics uses classical mechanics to model molecular systems. The Born–Oppenheimer approximation is assumed valid and the potential energy of all systems is calculated as a function of the nuclear coordinates using force fields. Molecular mechanics can be used to study molecule systems ranging in size and complexity from small to large biological systems or material assemblies with many thousands to millions of atoms.

<span class="mw-page-title-main">Molecular modelling</span> Discovering chemical properties by physical simulations

Molecular modelling encompasses all methods, theoretical and computational, used to model or mimic the behaviour of molecules. The methods are used in the fields of computational chemistry, drug design, computational biology and materials science to study molecular systems ranging from small chemical systems to large biological molecules and material assemblies. The simplest calculations can be performed by hand, but inevitably computers are required to perform molecular modelling of any reasonably sized system. The common feature of molecular modelling methods is the atomistic level description of the molecular systems. This may include treating atoms as the smallest individual unit, or explicitly modelling protons and neutrons with its quarks, anti-quarks and gluons and electrons with its photons.

Protein design is the rational design of new protein molecules to design novel activity, behavior, or purpose, and to advance basic understanding of protein function. Proteins can be designed from scratch or by making calculated variants of a known protein structure and its sequence. Rational protein design approaches make protein-sequence predictions that will fold to specific structures. These predicted sequences can then be validated experimentally through methods such as peptide synthesis, site-directed mutagenesis, or artificial gene synthesis.

<span class="mw-page-title-main">Force field (chemistry)</span> Concept on molecular modeling

In the context of chemistry, molecular physics, physical chemistry, and molecular modelling, a force field is a computational model that is used to describe the forces between atoms within molecules or between molecules as well as in crystals. Force fields are a variety of interatomic potentials. More precisely, the force field refers to the functional form and parameter sets used to calculate the potential energy of a system on the atomistic level. Force fields are usually used in molecular dynamics or Monte Carlo simulations. The parameters for a chosen energy function may be derived from classical laboratory experiment data, calculations in quantum mechanics, or both. Force fields utilize the same concept as force fields in classical physics, with the main difference being that the force field parameters in chemistry describe the energy landscape on the atomistic level. From a force field, the acting forces on every particle are derived as a gradient of the potential energy with respect to the particle coordinates.

The OPLS force field was developed by Prof. William L. Jorgensen at Purdue University and later at Yale University, and is being further developed commercially by Schrödinger, Inc.

In computational chemistry and computational physics, the embedded atom model, embedded-atom method or EAM, is an approximation describing the energy between atoms and is a type of interatomic potential. The energy is a function of a sum of functions of the separation between an atom and its neighbors. In the original model, by Murray Daw and Mike Baskes, the latter functions represent the electron density. The EAM is related to the second moment approximation to tight binding theory, also known as the Finnis-Sinclair model. These models are particularly appropriate for metallic systems. Embedded-atom methods are widely used in molecular dynamics simulations.

Car–Parrinello molecular dynamics or CPMD refers to either a method used in molecular dynamics or the computational chemistry software package used to implement this method.

Implicit solvation is a method to represent solvent as a continuous medium instead of individual “explicit” solvent molecules, most often used in molecular dynamics simulations and in other applications of molecular mechanics. The method is often applied to estimate free energy of solute-solvent interactions in structural and chemical processes, such as folding or conformational transitions of proteins, DNA, RNA, and polysaccharides, association of biological macromolecules with ligands, or transport of drugs across biological membranes.

<span class="mw-page-title-main">Water model</span> Aspect of computational chemistry

In computational chemistry, a water model is used to simulate and thermodynamically calculate water clusters, liquid water, and aqueous solutions with explicit solvent. The models are determined from quantum mechanics, molecular mechanics, experimental results, and these combinations. To imitate a specific nature of molecules, many types of models have been developed. In general, these can be classified by the following three points; (i) the number of interaction points called site, (ii) whether the model is rigid or flexible, (iii) whether the model includes polarization effects.

In computational chemistry, a constraint algorithm is a method for satisfying the Newtonian motion of a rigid body which consists of mass points. A restraint algorithm is used to ensure that the distance between mass points is maintained. The general steps involved are: (i) choose novel unconstrained coordinates, (ii) introduce explicit constraint forces, (iii) minimize constraint forces implicitly by the technique of Lagrange multipliers or projection methods.

In molecular mechanics, VALBOND is a method for computing the angle bending energy that is based on valence bond theory. It is based on orbital strength functions, which are maximized when the hybrid orbitals on the atom are orthogonal. The hybridization of the bonding orbitals are obtained from empirical formulas based on Bent's rule, which relates the preference towards p character with electronegativity.

<span class="mw-page-title-main">Bond order potential</span>

Bond order potential is a class of empirical (analytical) interatomic potentials which is used in molecular dynamics and molecular statics simulations. Examples include the Tersoff potential, the EDIP potential, the Brenner potential, the Finnis–Sinclair potentials, ReaxFF, and the second-moment tight-binding potentials. They have the advantage over conventional molecular mechanics force fields in that they can, with the same parameters, describe several different bonding states of an atom, and thus to some extent may be able to describe chemical reactions correctly. The potentials were developed partly independently of each other, but share the common idea that the strength of a chemical bond depends on the bonding environment, including the number of bonds and possibly also angles and bond lengths. It is based on the Linus Pauling bond order concept and can be written in the form

This is a list of computer programs that are predominantly used for molecular mechanics calculations.

The hybrid QM/MM approach is a molecular simulation method that combines the strengths of ab initio QM calculations (accuracy) and MM (speed) approaches, thus allowing for the study of chemical processes in solution and in proteins. The QM/MM approach was introduced in the 1976 paper of Warshel and Levitt. They, along with Martin Karplus, won the 2013 Nobel Prize in Chemistry for "the development of multiscale models for complex chemical systems".

Biology Monte Carlo methods (BioMOCA) have been developed at the University of Illinois at Urbana-Champaign to simulate ion transport in an electrolyte environment through ion channels or nano-pores embedded in membranes. It is a 3-D particle-based Monte Carlo simulator for analyzing and studying the ion transport problem in ion channel systems or similar nanopores in wet/biological environments. The system simulated consists of a protein forming an ion channel (or an artificial nanopores like a Carbon Nano Tube, CNT), with a membrane (i.e. lipid bilayer) that separates two ion baths on either side. BioMOCA is based on two methodologies, namely the Boltzmann transport Monte Carlo (BTMC) and particle-particle-particle-mesh (P3M). The first one uses Monte Carlo method to solve the Boltzmann equation, while the later splits the electrostatic forces into short-range and long-range components.

<span class="mw-page-title-main">Interatomic potential</span> Functions for calculating potential energy

Interatomic potentials are mathematical functions to calculate the potential energy of a system of atoms with given positions in space. Interatomic potentials are widely used as the physical basis of molecular mechanics and molecular dynamics simulations in computational chemistry, computational physics and computational materials science to explain and predict materials properties. Examples of quantitative properties and qualitative phenomena that are explored with interatomic potentials include lattice parameters, surface energies, interfacial energies, adsorption, cohesion, thermal expansion, and elastic and plastic material behavior, as well as chemical reactions.

References

  1. Amber 2023 Reference Manual
  2. Weiner, Paul K.; Kollman, Peter A. (1981). "AMBER : Assisted model building with energy refinement. A general program for modeling molecules and their interactions". Journal of Computational Chemistry. 2 (3): 287–303. doi:10.1002/jcc.540020311. ISSN   0192-8651.
  3. Pearlman, David A.; Case, David A.; Caldwell, James W.; Ross, Wilson S.; Cheatham, Thomas E.; DeBolt, Steve; Ferguson, David; Seibel, George; Kollman, Peter (1995). "AMBER, a package of computer programs for applying molecular mechanics, normal mode analysis, molecular dynamics and free energy calculations to simulate the structural and energetic properties of molecules". Computer Physics Communications. 91 (1–3): 1–41. doi:10.1016/0010-4655(95)00041-d. ISSN   0010-4655.
  4. Cornell WD, Cieplak P, Bayly CI, Gould IR, Merz KM Jr, Ferguson DM, Spellmeyer DC, Fox T, Caldwell JW, Kollman PA (1995). "A Second Generation Force Field for the Simulation of Proteins, Nucleic Acids, and Organic Molecules". J. Am. Chem. Soc. 117 (19): 5179–5197. CiteSeerX   10.1.1.323.4450 . doi:10.1021/ja00124a002.
  5. Maier, James A; Martinez, Carmenza; Kasavajhala, Koushik; Wickstrom, Lauren; Hauser, Kevin E; Simmerling, Carlos (2015). "Ff14SB: Improving the Accuracy of Protein Side Chain and Backbone Parameters from ff99SB". Journal of Chemical Theory and Computation. 11 (8): 3696–3713. doi:10.1021/acs.jctc.5b00255. PMC   4821407 . PMID   26574453.
  6. "The Amber Force Fields".
  7. Dickson, Callum J; Madej, Benjamin D; Skjevik, Åge A; Betz, Robin M; Teigen, Knut; Gould, Ian R; Walker, Ross C (2014). "Lipid14: The Amber Lipid Force Field". Journal of Chemical Theory and Computation. 10 (2): 865–879. doi:10.1021/ct4010307. PMC   3985482 . PMID   24803855.

1. Duan, Yong; Wu, Chun; Chowdhury, Shibasish; Lee, Mathew C.; Xiong, Guoming; Zhang, Wei; Yang, Rong; Cieplak, Piotr; et al. (2003). "A point-charge force field for molecular mechanics simulations of proteins based on condensed-phase quantum mechanical calculations". Journal of Computational Chemistry. 24 (16): 1999–2012. doi:10.1002/jcc.10349. PMID   14531054. S2CID   283317.