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

`describe_features`(traj)	Return a list of dictionaries describing the contacts features.
`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 derived from
`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.

__init__(contacts='all', scheme='closest-heavy', ignore_nonprotein=True)¶

Methods

`__init__`([contacts, scheme, ignore_nonprotein])
`describe_features`(traj)	Return a list of dictionaries describing the contacts features.
`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 derived from
`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.

describe_features(traj)¶

Return a list of dictionaries describing the contacts features.

Parameters:

traj : mdtraj.Trajectory

The trajectory to describe

Returns:

feature_descs : list of dict

Dictionary describing each feature with the following information about the atoms participating in each dihedral

resnames: unique names of residues

atominds: the four atom indicies

resseqs: unique residue sequence ids (not necessarily 0-indexed)

resids: unique residue ids (0-indexed)

featurizer: Contact

featuregroup: ca, heavy etc.

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.

partial_transform(traj)¶

Featurize an MD trajectory into a vector space derived from 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.