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).
__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 – 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.
Return type:list of dict
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 – Transformed array.
Return type:

numpy array of shape [n_samples, n_features_new]
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 – Parameter names mapped to their values.
Return type:mapping of string to any
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 – 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.
Return type:np.ndarray, dtype=float, shape=(n_samples, n_features)

See also

transform()
simultaneously featurize a collection of MD trajectories
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 latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns:
Return type: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 – The featurized trajectories. features[i] is the featurized version of traj_list[i] and has shape (n_samples_i, n_features)
Return type:list(np.ndarray), length = len(traj_list)