msmbuilder.featurizer.ContactFeaturizer

class msmbuilder.featurizer.ContactFeaturizer(contacts='all', scheme='closest-heavy', ignore_nonprotein=True)

Featurizer based on residue-residue distances

This featurizer transforms a dataset containing MD trajectories into a vector dataset by representing each frame in each of the MD trajectories by a vector of the distances between pairs of amino-acid residues.

The exact method for computing the the distance between two residues is configurable with the scheme parameter.

Parameters:

contacts : np.ndarray or ‘all’

array containing (0-indexed) indices of the residues to compute the contacts for. (e.g. np.array([[0, 10], [0, 11]]) would compute the contact between residue 0 and residue 10 as well as the contact between residue 0 and residue 11.) [NOTE: if no array is passed then ‘all’ contacts are calculated. This means that the result will contain all contacts between residues separated by at least 3 residues.]

scheme : {‘ca’, ‘closest’, ‘closest-heavy’}

scheme to determine the distance between two residues:
‘ca’ : distance between two residues is given by the distance

between their alpha carbons

‘closest’ : distance is the closest distance between any

two atoms in the residues

‘closest-heavy’ : distance is the closest distance between

any two non-hydrogen atoms in the residues

ignore_nonprotein : bool

When using contact==all, don’t compute contacts between “residues” which are not protein (i.e. do not contain an alpha carbon).

Methods

featurize(traj)
fit(traj_list[, y])
fit_transform(X[, y]) Fit to data, then transform it.
get_params([deep]) Get parameters for this estimator.
partial_transform(traj) Featurize an MD trajectory into a vector space via of residue-residue
save(filename)
set_params(**params) Set the parameters of this estimator.
summarize() Return some diagnostic summary statistics about this Markov model
transform(traj_list[, y]) Featurize a several trajectories.
partial_transform(traj)

Featurize an MD trajectory into a vector space via of residue-residue distances

Parameters:

traj : mdtraj.Trajectory

A molecular dynamics trajectory to featurize.
Returns:

features : np.ndarray, dtype=float, shape=(n_samples, n_features)

A featurized trajectory is a 2D array of shape (length_of_trajectory x n_features) where each features[i] vector is computed by applying the featurization function to the `i`th snapshot of the input trajectory.

See also

transform
simultaneously featurize a collection of MD trajectories
fit_transform(X, y=None, **fit_params)

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters:

X : numpy array of shape [n_samples, n_features]

Training set.

y : numpy array of shape [n_samples]

Target values.
Returns:

X_new : numpy array of shape [n_samples, n_features_new]

Transformed array.
get_params(deep=True)

Get parameters for this estimator.

Parameters:

deep: boolean, optional

If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:

params : mapping of string to any

Parameter names mapped to their values.
set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The former have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns:self
summarize()

Return some diagnostic summary statistics about this Markov model

transform(traj_list, y=None)

Featurize a several trajectories.

Parameters:

traj_list : list(mdtraj.Trajectory)

Trajectories to be featurized.
Returns:

features : list(np.ndarray), length = len(traj_list)

The featurized trajectories. features[i] is the featurized version of traj_list[i] and has shape (n_samples_i, n_features)
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