`aanma.pdb.Rd`

Perform all-atom elastic network model normal modes calculation of a protein structure.

aanma(...) # S3 method for pdb aanma(pdb, pfc.fun = NULL, mass = TRUE, temp = 300, keep = NULL, hessian = NULL, outmodes = "calpha", rm.wat = TRUE, reduced = FALSE, rtb = FALSE, nmer = 1, ...) rtb(hessian, pdb, mass = TRUE, nmer = 1, verbose = TRUE)

... | additional arguments to |
---|---|

pdb | an object of class |

pfc.fun | customized pair force constant (‘pfc’) function. The provided function should take a vector of distances as an argument to return a vector of force constants. If NULL, the default function ‘aaenm2’ will be employed. (See details below). |

mass | logical, if TRUE the Hessian will be mass-weighted. |

temp | numerical, temperature for which the amplitudes for scaling the atomic displacement vectors are calculated. Set ‘temp=NULL’ to avoid scaling. |

keep | numerical, final number of modes to be stored. Note that all subsequent analyses are limited to this subset of modes. This option is useful for very large structures and cases where memory may be limited. |

hessian | hessian matrix as obtained from |

outmodes | either a character (‘calpha’ or ‘noh’) or atom
indices as obtained from |

rm.wat | logical, if TRUE water molecules will be removed before calculation. |

reduced | logical, if TRUE the coarse-grained (‘4-bead’) ENM will be employed. (See details below). |

rtb | logical, if TRUE the rotation-translation block based approximate modes will be calculated. (See details below). |

nmer | numerical, defines the number of residues per block (used only
when |

verbose | logical, if TRUE print detailed processing message |

Returns an object of class ‘nma’ with the following components:

numeric matrix with columns containing the normal mode
vectors. Mode vectors are converted to unweighted Cartesian
coordinates when `mass=TRUE`

. Note that the 6 first trivial
eigenvectos appear in columns one to six.

numeric vector containing the vibrational
frequencies corresponding to each mode (for `mass=TRUE`

).

numeric vector containing the force constants
corresponding to each mode (for `mass=FALSE)`

).

numeric vector of atomic fluctuations.

numeric matrix with columns containing the raw
eigenvectors. Equals to the `modes`

component when
`mass=FALSE`

and `temp=NULL`

.

numeric vector containing the raw eigenvalues.

numeric matrix of class `xyz`

containing the
Cartesian coordinates in which the calculation was performed.

numeric vector containing the residue masses used for the mass-weighting.

numerical, temperature for which the amplitudes for scaling the atomic displacement vectors are calculated.

number of trivial modes.

number of C-alpha atoms.

the matched call.

This function builds an elastic network model (ENM) based on all
heavy atoms of input `pdb`

, and performs subsequent normal mode
analysis (NMA) in various manners. By default, the ‘aaenm2’ force
field (defining of the spring constants between atoms) is used, which was
obtained by fitting to a local energy minimum of a crambin model
derived from the AMBER99SB force field. It employs a pair force constant
function which falls as r^-6, and specific force constants for
covalent and intra-residue atom pairs. See also `load.enmff`

for other force field options.

The `outmodes`

argument controls the type of output modes. There are
two standard types of output modes: ‘noh’ and ‘calpha’.
`outmodes='noh'`

invokes regular all-atom based ENM-NMA. When
`outmodes='calpha'`

, an effective Hessian with respect to all C-alpha
atoms will be first calculated using the same formula as in Hinsen et al.
NMA is then performed on this effective C-alpha based Hessian. In addition,
users can provide their own atom selection (see `atom.select`

)
as the value of `outmodes`

for customized output modes generation.

When `reduced=TRUE`

, only a selection of all heavy atoms is used
to build the ENM. More specifically, three to five atoms per residue
constitute the model. Here the N, CA, C atoms represent the protein
backbone, and zero to two selected side chain atoms represent the side chain
(selected based on side chain size and the distance to CA). This
coarse-grained ENM has significantly improved computational efficiency and
similar prediction accuracy with respect to the all-atom ENM.

When `rtb=TRUE`

, rotation-translation block (RTB) based approximate
modes will be calculated. In this method, each residue is assumed to be a
rigid body (or ‘block’) that has only rotational and translational
degrees of freedom. Intra-residue deformation is thus ignored.
(See Durand et al 1994 and Tama et al. 2000 for more details). N residues per
block is also supported, where N=1, 2, 3, etc. (See argument `nmer`

).
The RTB method has significantly improved computational efficiency and
similar prediction accuracy with respect to the all-atom ENM.

By default the function will diagonalize the mass-weighted Hessian matrix. The resulting mode vectors are moreover scaled by the thermal fluctuation amplitudes.

Lars Skjaerven & Xin-Qiu Yao

Hinsen, K. et al. (2000) *Chem. Phys.* **261**, 25.
Durand, P. et al. (1994) *Biopolymers* **34**, 759.
Tama, F. et al. (2000) *Proteins* **41**, 1.

`nma.pdb`

for C-alpha based NMA, `aanma.pdbs`

for
ensemble all-atom NMA, `load.enmff`

for available ENM force
fields, and `fluct.nma`

, `mktrj.nma`

, and
`dccm.nma`

for various post-NMA calculations.

if (FALSE) { # All-atom NMA takes relatively long time - Don't run by default. ## Fetch stucture pdb <- read.pdb( system.file("examples/1hel.pdb", package="bio3d") ) ## Calculate all-atom normal modes modes.aa <- aanma(pdb, outmodes='noh') ## Calculate all-atom normal modes with RTB approximation modes.aa.rtb <- aanma(pdb, outmodes='noh', rtb=TRUE) ## Compare the two modes rmsip(modes.aa, modes.aa.rtb) ## Calculate C-alpha normal modes. modes <- aanma(pdb) ## Calculate C-alpha normal modes with reduced ENM. modes.cg <- aanma(pdb, reduced=TRUE) ## Calculate C-alpha normal modes with RTB approximation modes.rtb <- aanma(pdb, rtb=TRUE) ## Compare modes rmsip(modes, modes.cg) rmsip(modes, modes.rtb) ## Print modes print(modes) ## Plot modes plot(modes) ## Visualize modes #m7 <- mktrj.nma(modes, mode=7, file="mode_7.pdb", pdb=pdb) }