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Class for the Nstate models.














Inherited from 

Centre of mass (CoM) analysis. Keyword Arguments ~~~~~~~~~~~~~~~~~ pivot_point: The pivot point of the motions between the two domains. centre: The optional argument for manually specifying the CoM of the initial position prior to the N rotations to the positions of the N states. Description ~~~~~~~~~~~ This function is used for analysing the domain motion information content of the N states from the Nstate model. The states do not correspond to physical states, hence nothing can be extracted from the individual states. This analysis involves the calculation of the pivot to centre of mass (pivotCoM) order parameter and subsequent cone of motions. For the analysis, both the pivot point and centre of mass must be specified. The supplied pivot point must be a vector of floating point numbers of length 3. If the centre keyword argument is supplied, it must also be a vector of floating point numbers (of length 3). If the centre argument is not supplied, then the CoM will be calulcated from the selected parts of a previously loaded structure. Examples ~~~~~~~~ To perform an analysis where the pivot is at the origin and the CoM is set to the Nterminal domain of a previously loaded PDB file (the Cterminal domain has been deselected), type: relax> n_state_model.CoM() To perform an analysis where the pivot is at the origin (because the real pivot has been shifted to this position) and the CoM is at the position [0, 0, 1], type one of: relax> n_state_model.CoM(centre=[0, 0, 1]) relax> n_state_model.CoM(centre=[0.0, 0.0, 1.0]) relax> n_state_model.CoM(pivot_point=[0.0, 0.0, 0.0], centre=[0.0, 0.0, 1.0]) 
Create a PDB file representing the cone models from the centre of mass (CoM) analysis. Keyword Arguments ~~~~~~~~~~~~~~~~~ cone_type: The type of cone model to represent. scale: Value for scaling the pivotCoM distance which the size of the cone defaults to. file: The name of the PDB file. dir: The directory where the file is located. force: A flag which, if set to True, will overwrite the any preexisting file. Description ~~~~~~~~~~~ This function creates a PDB file containing an artificial geometric structure to represent the various cone models. These models include: 'diff in cone' 'diff on cone' The model can be selected by setting the cone_type argument to one of these strings. The cone is represented as an isotropic cone with its axis parallel to the average pivotCoM vector, the vertex placed at the pivot point of the domain motions, and the length of the edge of the cone equal to the pivotCoM distance multipled by the scaling argument. The resultant PDB file can subsequently read into any molecular viewer. There are four different types of residue within the PDB. The pivot point is represented as as a single carbon atom of the residue 'PIV'. The cone consists of numerous H atoms of the residue 'CON'. The average pivotCoM vector is presented as the residue 'AVE' with one carbon atom positioned at the pivot and the other at the head of the vector (after scaling by the scale argument). Finally, if Monte Carlo have been performed, there will be multiple 'MCC' residues representing the cone for each simulation, and multiple 'MCA' residues representing the varying average pivotCoM vector for each simulation. To create the diffusion in a cone PDB representation, a uniform distribution of vectors on a sphere is generated using spherical coordinates with the polar angle defined from the average pivotCoM vector. By incrementing the polar angle using an arccos distribution, a radial array of vectors representing latitude are created while incrementing the azimuthal angle evenly creates the longitudinal vectors. These are all placed into the PDB file as H atoms and are all connected using PDB CONECT records. Each H atom is connected to its two neighbours on the both the longitude and latitude. This creates a geometric PDB object with longitudinal and latitudinal lines representing the filled cone. 
Eliminate the structures or states with no probability. Examples ~~~~~~~~ Simply type: relax> n_state_model.elim_no_prob(N=8) 
Set the number of states in the Nstate model. Keyword Arguments ~~~~~~~~~~~~~~~~~ N: The number of states. Description ~~~~~~~~~~~ Prior to optimisation, the number of states in the Nstate model can be specified. If the number of states is not set, then this parameter will be equal to the number of loaded structures. Examples ~~~~~~~~ To set up an 8state model, type: relax> n_state_model.number_of_states(N=8) 
Set the reference domain for the '2domain' Nstate model. Keyword Arguments ~~~~~~~~~~~~~~~~~ ref: The domain which will act as the frame of reference. This is only valid for the '2domain' Nstate model. Description ~~~~~~~~~~~ Prior to optimisation of the '2domain' Nstate model, which of the two domains will act as the frame of reference must be specified. The Nstates will be rotations of the other domain, so to switch the frame of reference to the other domain simply transpose the rotation matrices. Examples ~~~~~~~~ To set up a 5state model with 'C' domain being the frame of reference, type: relax> n_state_model.ref_domain(ref='C') 
Select the Nstate model type and set up the model. Keyword Arguments ~~~~~~~~~~~~~~~~~ model: The name of the preset Nstate model. Description ~~~~~~~~~~~ Prior to optimisation, the Nstate model type should be selected. The preset models are: '2domain'  The Nstate model for a system of two domains, where one domain experiences a a reduced tensor. 'population'  The Nstate model whereby only populations are optimised. The structures loaded into relax are assumed to be fixed. I.e. if two domains are present, the Euler angles for each state are fixed. The parameters of the model include the weight or probability for each state and the alignment tensor  {p0, p1, ..., pN, Axx, Ayy, Axy, Axz, Ayz}. 'fixed'  The Nstate model whereby all motions are fixed and all populations are fixed to the set probabilities. The parameters of the model are simply the alignment tensor, {Axx, Ayy, Axy, Axz, Ayz}. Examples ~~~~~~~~ To analyse populations of states, type: relax> n_state_model.select_model(model='populations') 
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