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Pydoc: module maxLikelyFit

maxLikelyFit

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Classes

builtins.object

MaxLikelyFit

class MaxLikelyFit(builtins.object)

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)

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)