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- builtins.object
-
- MaxLikelyFit
class MaxLikelyFit(builtins.object) |
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MaxLikelyFit(atmSelect, verbose, mSelect=1)
A class which implements Maximum Likelihood Superpositioning of
protein structures.
Written by
Robin Augustine Thottugal
08/08/2007
based on:
Theobald D.L. and Wuttke D.S., Empirical Bayes hierarchical
models for regularizing maximum likelihood estimation in the matrix
Gaussian Procrustes problem, Proc. of the National Academy of
Sciences 103, 18521-18527 (2006a)
Theobald D.L. and Wuttke D.S., THESEUS: Maximum likelihood
superpositioning and analysis of macromolecular structures,
Bioinformatics 22, 2171-2172 (2006)
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Methods defined here:
- MatToVec(s, Structure)
- Function which takes a cdsMatrix and
convert into a cdsVector
- NormalRMSD(s, X_Rot, MStruct, NStruct, Natoms)
- Calculates the Average RMSD of N Structure
- PDBWrite(s, structs, wStruct, suffix='.mleFit')
- Writing the Co-ords into PDB files with filenames of the input
structures plus the specified suffix.
Also, average coordinates will also be written to "MLE-Average.pdb"
with the variance of each atom in the B-factor field.
- RMSD(s, X_Rot, MStruct, NStruct, Natoms)
- Calculates the variance of selected atoms
- WriteRotTransMat(s, NStruct)
- Writing the Rotation and Translation Matrix
- __init__(s, atmSelect, verbose, mSelect=1)
- atmSelect - specify which atoms will be used in the fit
mSelect - specify method of regularizing the
inverse of the covariance matrix -
if 0 use perturbative approach - add a small value to
the diagonal. If 1 use diagonal elements as ``eigenvalues,''
and follow the scheme of ref. FIX ME.
verbose - if True, print out intermediate values of log likelihood
and RMSD.
- atmToMat(s, atm_list)
- Function which converts a list of atom object to a cdsMatrix
- calcVariance(s, fitted)
- Function which calculates the variance of all atoms
which goes into the Bfactor column of the average
structure.
As a
- cnorm(s, M, pM)
- Calculates the RMSD between two matrix
- estimateEgien(s, Segnval, egnval, NStruct)
- Function which implements the Expectation Maximization algorithm to
fit all the eigen value according to an inverse gamma distribution
- fit(s, coords)
- perform the maximum likelihood fitting procedure.
- fittedCoords(s)
- return an array of all atomic coordinates with the most recent
fit translate+rotation applied
- loglikelihood(s, CovMat, X, MStruct, RotMat, transVec, NStruct, gamma, alpha)
- Function which evaluvates the log likelihood function
- meanCoords(s, coords)
- Return straight mean of the passed coordinates
- newton(s, x, Mean_inv, inv_egnval)
- Newton Rapson Method for root finding.This function is
used to find gamma
- orderedResidues(s, threshold, selection=None)
- Return a list of tuples describing the range of ordered residues, each
of which contains (segid,(first,last)), where first and last denote
first and last resid in the ordered range.
A residue is defined as ordered if the average positional RMSD of
selected atoms of that residue are less than threshold.
If selection is not specified, self.atmSelect will be used.
- rotate(s, MStruct, CovMat, X_cap, NStruct)
- Function to find the ML estimate of optimal Rotation
- translate(s, CovMat, X, NStruct)
- Function to find the ML estimate of optimal translation
- vecToMat(s, Vector)
- Fuction which takes a cdsVector and
convert it into a matrix
Data descriptors defined here:
- __dict__
dictionary for instance variables (if defined)
- __weakref__
list of weak references to the object (if defined)
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