- Molecular dynamics
- Langevin dynamics
- Self-guided MD and LD
- Rigid bodies
- Rigid molecular dynamics
- Rigid minimization

MODELLER currently implements a Beale restart conjugate gradients
algorithm [Shanno & Phua, 1980,Shanno & Phua, 1982] and a molecular dynamics procedure with the
leap-frog Verlet integrator [Verlet, 1967].
The conjugate gradients optimizer is usually
used in combination with the variable target function method
[Braun & Gõ, 1985] which is
implemented with the `automodel` class (Section A.4).
The molecular
dynamics procedure can be used in a simulated annealing protocol that
is also implemented with the `automodel` class.

Force in MODELLER is obtained by equating the objective function
with internal energy in kcal/mole. The atomic masses are all set to
that of C^{12}
(MODELLER unit is kg/mole). The initial velocities
at a given temperature are obtained from a Gaussian random number
generator with a mean and standard deviation of:

0 | (A.7) | ||

(A.8) |

where is the Boltzmann constant, is the mass of one C

The Newtonian equations of motion are integrated by the leap-frog Verlet
algorithm [Verlet, 1967]:

where is the position of atom . In addition, velocity is capped at a maximum value, before calculating the shift, such that the maximal shift along one axis can only be

(A.11) | |||

(A.12) | |||

(A.13) |

where is the Boltzmann constant, the number of degrees of freedom, the current kinetic energy and the current kinetic temperature.

Langevin dynamics (LD) are implemented as in [Loncharich *et al.*, 1992]. The equations
of motion (Equation A.9) are modified as follows:

where is a friction factor (in ) and a random force, chosen to have zero mean and standard deviation

(A.15) |

MODELLER also implements the self-guided MD [Wu & Wang, 1999] and LD [Wu & Brooks, 2003]
methods. For self-guided MD, the equations of motion (Equation A.9)
are modified as follows:

where is the guiding factor (the same for all atoms), the guide time in femtoseconds, and a guiding force, set to zero at the start of the simulation. (Position is updated in the usual way.)

For self-guided Langevin dynamics, the guiding forces are determined as follows (terms are as defined in Equation A.14):

(A.18) |

A scaling parameter χ
is then determined by first making an unconstrained
half step:

(A.19) | |||

(A.20) | |||

(A.21) |

Finally, the velocities are advanced using the scaling factor:

Where rigid bodies are used, these are optimized separately from the other atoms in the system. This has the additional advantage of reducing the number of degrees of freedom.

where the sum operates over all atoms in the rigid body, and is the position of atom in real space.

For the rotational motion, the orientation quaternions are again integrated using the same Verlet equations. For this, the quaternion accelerations are calculated using the following relation [Rapaport, 1997]:

(A.24) |

where is the orthogonal matrix

(A.25) |

and is the first derivative of the angular velocity (in the body-fixed frame) about axis -

(A.26) |

(Similar equations exist for the and components.) The angular velocities are obtained from the quaternion velocities:

(A.27) |

The torque, , in the body-fixed frame, is calculated as

(A.28) |

and is the rotation matrix to convert from world space to body space

(A.29) |

and finally the component of the inertia tensor, , is given by

(A.30) |

where is the position of each atom in body space (

The kinetic energy of each rigid body (used for temperature control) is given as a combination of translation and rotational components:

(A.31) |

Initial translational and rotational velocities of each rigid body are set in the same way as for atomistic dynamics.

(A.32) |

The transformation matrices are given as:

(A.33) |

(A.34) |

(A.35) |

The atomic positions are reconstructed when necessary from the body's orientation by means of the following relation:

(A.36) |

where is the rotation matrix

(A.37) |

Automatic builds 2019-06-19