Proper treatment of nonbonded interactions
is essential for the
accuracy of molecular dynamics (MD) simulations, especially in studies
of lipid bilayers. The use of the CHARMM36 force field (C36 FF) in
different MD simulation programs can result in disagreements with
published simulations performed with CHARMM due to differences in
the protocols used to treat the long-range and 1-4 nonbonded interactions.
In this study, we systematically test the use of the C36 lipid FF
in NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM. A wide range of
Lennard-Jones (LJ) cutoff schemes and integrator algorithms were tested
to find the optimal simulation protocol to best match bilayer properties
of six lipids with varying acyl chain saturation and head groups.
MD simulations of a 1,2-dipalmitoyl-sn-phosphatidylcholine
(DPPC) bilayer were used to obtain the optimal protocol for each program.
MD simulations with all programs were found to reasonably match the
DPPC bilayer properties (surface area per lipid, chain order parameters,
and area compressibility modulus) obtained using the standard protocol
used in CHARMM as well as from experiments. The optimal simulation
protocol was then applied to the other five lipid simulations and
resulted in excellent agreement between results from most simulation
programs as well as with experimental data. AMBER compared least favorably
with the expected membrane properties, which appears to be due to
its use of the hard-truncation in the LJ potential versus a force-based
switching function used to smooth the LJ potential as it approaches
the cutoff distance. The optimal simulation protocol for each program
has been implemented in CHARMM-GUI. This protocol is expected to be
applicable to the remainder of the additive C36 FF including the proteins,
nucleic acids, carbohydrates, and small molecules.
OpenMM is a molecular dynamics simulation toolkit with a unique focus on extensibility. It allows users to easily add new features, including forces with novel functional forms, new integration algorithms, and new simulation protocols. Those features automatically work on all supported hardware types (including both CPUs and GPUs) and perform well on all of them. In many cases they require minimal coding, just a mathematical description of the desired function. They also require no modification to OpenMM itself and can be distributed independently of OpenMM. This makes it an ideal tool for researchers developing new simulation methods, and also allows those new methods to be immediately available to the larger community.
OpenMM is a software toolkit for performing molecular simulations on a range of high performance computing architectures. It is based on a layered architecture: the lower layers function as a reusable library that can be invoked by any application, while the upper layers form a complete environment for running molecular simulations. The library API hides all hardware-specific dependencies and optimizations from the users and developers of simulation programs: they can be run without modification on any hardware on which the API has been implemented. The current implementations of OpenMM include support for graphics processing units using the OpenCL and CUDA frameworks. In addition, OpenMM was designed to be extensible, so new hardware architectures can be accommodated and new functionality (e.g., energy terms and integrators) can be easily added.
We describe a complete implementation of all-atom protein molecular dynamics running entirely on a graphics processing unit (GPU), including all standard force field terms, integration, constraints, and implicit solvent. We discuss the design of our algorithms and important optimizations needed to fully take advantage of a GPU. We evaluate its performance, and show that it can be more than 700 times faster than a conventional implementation running on a single CPU core.
OpenMM is a molecular dynamics simulation toolkit with a unique focus on extensibility. It allows users to easily add new features, including forces with novel functional forms, new integration algorithms, and new simulation protocols. Those features automatically work on all supported hardware types (including both CPUs and GPUs) and perform well on all of them. In many cases they require minimal coding, just a mathematical description of the desired function. They also require no modification to OpenMM itself and can be distributed independently of OpenMM. This makes it an ideal tool for researchers developing new simulation methods, and also allows those new methods to be immediately available to the larger community.
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