Utils

"""

Computes putative binding pockets on protein.

"""

__author__ = "Bharath Ramsundar"

__copyright__ = "Copyright 2017, Stanford University"

__license__ = "MIT"

import os

import logging

import tempfile

import numpy as np

from subprocess import call

from scipy.spatial import ConvexHull

from deepchem.feat.binding_pocket_features import BindingPocketFeaturizer

from deepchem.feat.fingerprints import CircularFingerprint

from deepchem.models.sklearn_models import SklearnModel

from deepchem.utils import rdkit_util

from deepchem.utils import coordinate_box_utils as box_utils

from deepchem.utils.fragment_util import get_contact_atom_indices

logger = logging.getLogger(__name__)

def extract_active_site(protein_file, ligand_file, cutoff=4):

  """Extracts a box for the active site."""

  protein_coords = rdkit_util.load_molecule(

      protein_file, add_hydrogens=False)[0]

  ligand_coords = rdkit_util.load_molecule(

      ligand_file, add_hydrogens=True, calc_charges=True)[0]

  num_ligand_atoms = len(ligand_coords)

  num_protein_atoms = len(protein_coords)

  pocket_inds = []

  pocket_atoms = set([])

  for lig_atom_ind in range(num_ligand_atoms):

    lig_atom = ligand_coords[lig_atom_ind]

    for protein_atom_ind in range(num_protein_atoms):

      protein_atom = protein_coords[protein_atom_ind]

      if np.linalg.norm(lig_atom - protein_atom) < cutoff:

        if protein_atom_ind not in pocket_atoms:

          pocket_atoms = pocket_atoms.union(set([protein_atom_ind]))

  # Should be an array of size (n_pocket_atoms, 3)

  pocket_atoms = list(pocket_atoms)

  n_pocket_atoms = len(pocket_atoms)

  pocket_coords = np.zeros((n_pocket_atoms, 3))

  for ind, pocket_ind in enumerate(pocket_atoms):

    pocket_coords[ind] = protein_coords[pocket_ind]

  """Extracts a box for the active site.

  Params

  ------

  protein_file: str

    Location of protein PDB

  ligand_file: str

    Location of ligand input file

  cutoff: int, optional

    The distance in angstroms from the protein pocket to

    consider for featurization.

  """

  protein = rdkit_util.load_molecule(

      protein_file, add_hydrogens=False)

  ligand = rdkit_util.load_molecule(

      ligand_file, add_hydrogens=True, calc_charges=True)

  protein_contacts, ligand_contacts = get_contact_atom_indices([protein, ligand], cutoff=cutoff)

  protein_coords = protein[0]

  pocket_coords = protein_coords[protein_contacts]

  x_min = int(np.floor(np.amin(pocket_coords[:, 0])))

  x_max = int(np.ceil(np.amax(pocket_coords[:, 0])))

  y_max = int(np.ceil(np.amax(pocket_coords[:, 1])))

  z_min = int(np.floor(np.amin(pocket_coords[:, 2])))

  z_max = int(np.ceil(np.amax(pocket_coords[:, 2])))

  return (((x_min, x_max), (y_min, y_max), (z_min, z_max)), pocket_atoms,

          pocket_coords)

def compute_overlap(mapping, box1, box2):

  """Computes overlap between the two boxes.

  Overlap is defined as % atoms of box1 in box2. Note that

  overlap is not a symmetric measurement.

  """

  atom1 = set(mapping[box1])

  atom2 = set(mapping[box2])

  return len(atom1.intersection(atom2)) / float(len(atom1))

  box = box_utils.CoordinateBox((x_min, x_max), (y_min, y_max), (z_min, z_max))

  return (box, pocket_coords)

def get_all_boxes(coords, pad=5):

  """Get all pocket boxes for protein coords.

  We pad all boxes the prescribed number of angstroms.

  TODO(rbharath): It looks like this may perhaps be non-deterministic?

  """

  hull = ConvexHull(coords)

  boxes = []

  for triangle in hull.simplices:

    # coords[triangle, 0] gives the x-dimension of all triangle points

    # Take transpose to make sure rows correspond to atoms.

    points = np.array(

        [coords[triangle, 0], coords[triangle, 1], coords[triangle, 2]]).T

    # We voxelize so all grids have integral coordinates (convenience)

    x_min, x_max = np.amin(points[:, 0]), np.amax(points[:, 0])

    x_min, x_max = int(np.floor(x_min)) - pad, int(np.ceil(x_max)) + pad

    y_min, y_max = np.amin(points[:, 1]), np.amax(points[:, 1])

    y_min, y_max = int(np.floor(y_min)) - pad, int(np.ceil(y_max)) + pad

    z_min, z_max = np.amin(points[:, 2]), np.amax(points[:, 2])

    z_min, z_max = int(np.floor(z_min)) - pad, int(np.ceil(z_max)) + pad

    boxes.append(((x_min, x_max), (y_min, y_max), (z_min, z_max)))

  return boxes

def boxes_to_atoms(atom_coords, boxes):

  """Maps each box to a list of atoms in that box.

  TODO(rbharath): This does a num_atoms x num_boxes computations. Is

  there a reasonable heuristic we can use to speed this up?

class BindingPocketFinder(object):

  """Abstract superclass for binding pocket detectors

  Many times when working with a new protein or other macromolecule,

  it's not clear what zones of the macromolecule may be good targets

  for potential ligands or other molecules to interact with. This

  abstract class provides a template for child classes that

  algorithmically locate potential binding pockets that are good

  potential interaction sites.

  Note that potential interactions sites can be found by many

  different methods, and that this abstract class doesn't specify the

  technique to be used.

  """

  mapping = {}

  for box_ind, box in enumerate(boxes):

    box_atoms = []

    (x_min, x_max), (y_min, y_max), (z_min, z_max) = box

    logger.info("Handing box %d/%d" % (box_ind, len(boxes)))

    for atom_ind in range(len(atom_coords)):

      atom = atom_coords[atom_ind]

      x_cont = x_min <= atom[0] and atom[0] <= x_max

      y_cont = y_min <= atom[1] and atom[1] <= y_max

      z_cont = z_min <= atom[2] and atom[2] <= z_max

      if x_cont and y_cont and z_cont:

        box_atoms.append(atom_ind)

    mapping[box] = box_atoms

  return mapping

  def find_pockets(self, molecule):

    """Finds potential binding pockets in proteins.

def merge_boxes(box1, box2):

  """Merges two boxes."""

  (x_min1, x_max1), (y_min1, y_max1), (z_min1, z_max1) = box1

  (x_min2, x_max2), (y_min2, y_max2), (z_min2, z_max2) = box2

  x_min = min(x_min1, x_min2)

  y_min = min(y_min1, y_min2)

  z_min = min(z_min1, z_min2)

  x_max = max(x_max1, x_max2)

  y_max = max(y_max1, y_max2)

  z_max = max(z_max1, z_max2)

  return ((x_min, x_max), (y_min, y_max), (z_min, z_max))

def merge_overlapping_boxes(mapping, boxes, threshold=.8):

  """Merge boxes which have an overlap greater than threshold.

  TODO(rbharath): This merge code is terribly inelegant. It's also quadratic

  in number of boxes. It feels like there ought to be an elegant divide and

  conquer approach here. Figure out later...

    Parameters

    ----------

    molecule: object

      Some representation of a molecule.

    """

  num_boxes = len(boxes)

  outputs = []

  for i in range(num_boxes):

    box = boxes[0]

    new_boxes = []

    new_mapping = {}

    # If overlap of box with previously generated output boxes, return

    contained = False

    for output_box in outputs:

      # Carry forward mappings

      new_mapping[output_box] = mapping[output_box]

      if compute_overlap(mapping, box, output_box) == 1:

        contained = True

    if contained:

      continue

    # We know that box has at least one atom not in outputs

    unique_box = True

    for merge_box in boxes[1:]:

      overlap = compute_overlap(mapping, box, merge_box)

      if overlap < threshold:

        new_boxes.append(merge_box)

        new_mapping[merge_box] = mapping[merge_box]

      else:

        # Current box has been merged into box further down list.

        # No need to output current box

        unique_box = False

        merged = merge_boxes(box, merge_box)

        new_boxes.append(merged)

        new_mapping[merged] = list(

            set(mapping[box]).union(set(mapping[merge_box])))

    if unique_box:

      outputs.append(box)

      new_mapping[box] = mapping[box]

    boxes = new_boxes

    mapping = new_mapping

  return outputs, mapping

class BindingPocketFinder(object):

  """Abstract superclass for binding pocket detectors"""

  def find_pockets(self, protein_file, ligand_file):

    """Finds potential binding pockets in proteins."""

    raise NotImplementedError

  Based on https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4112621/pdf/1472-6807-14-18.pdf

  """

  def __init__(self, pad=5):

  def __init__(self, scoring_model=None, pad=5):

    """Initialize the pocket finder.

    Parameters

    ----------

    scoring_model: `dc.models.Model`, optional

      If specified, use this model to prune pockets.

    pad: float, optional

      The number of angstroms to pad around a binding pocket's atoms

      to get a binding pocket box.

    """

    self.scoring_model = scoring_model

    self.pad = pad

  def find_all_pockets(self, protein_file):

    """Find list of binding pockets on protein."""

    # protein_coords is (N, 3) tensor

    coords = rdkit_util.load_molecule(protein_file)[0]

    return get_all_boxes(coords, self.pad)

  def find_pockets(self, protein_file, ligand_file):

    """Find list of suitable binding pockets on protein."""

    protein_coords = rdkit_util.load_molecule(

        protein_file, add_hydrogens=False, calc_charges=False)[0]

    ligand_coords = rdkit_util.load_molecule(

        ligand_file, add_hydrogens=False, calc_charges=False)[0]

    boxes = get_all_boxes(protein_coords, self.pad)

    mapping = boxes_to_atoms(protein_coords, boxes)

    pockets, pocket_atoms_map = merge_overlapping_boxes(mapping, boxes)

    pocket_coords = []

    for pocket in pockets:

      atoms = pocket_atoms_map[pocket]

      coords = np.zeros((len(atoms), 3))

      for ind, atom in enumerate(atoms):

        coords[ind] = protein_coords[atom]

      pocket_coords.append(coords)

    return pockets, pocket_atoms_map, pocket_coords

class RFConvexHullPocketFinder(BindingPocketFinder):

  """Uses pre-trained RF model + ConvexHulPocketFinder to select pockets."""

    """Find list of binding pockets on protein.

  def __init__(self, pad=5):

    self.pad = pad

    self.convex_finder = ConvexHullPocketFinder(pad)

    # Load binding pocket model

    self.base_dir = tempfile.mkdtemp()

    logger.info("About to download trained model.")

    # TODO(rbharath): Shift refined to full once trained.

    call((

        "wget -nv -c http://deepchem.io.s3-website-us-west-1.amazonaws.com/trained_models/pocket_random_refined_RF.tar.gz"

    ).split())

    call(("tar -zxvf pocket_random_refined_RF.tar.gz").split())

    call(("mv pocket_random_refined_RF %s" % (self.base_dir)).split())

    self.model_dir = os.path.join(self.base_dir, "pocket_random_refined_RF")

    Parameters

    ----------

    protein_file: str

      Protein to load in.

    """

    coords, _ = rdkit_util.load_molecule(protein_file)

    return box_utils.get_face_boxes(coords, self.pad)

    # Fit model on dataset

    self.model = SklearnModel(model_dir=self.model_dir)

    self.model.reload()

  def find_pockets(self, macromolecule_file):

    """Find list of suitable binding pockets on protein.

    # Create featurizers

    self.pocket_featurizer = BindingPocketFeaturizer()

    self.ligand_featurizer = CircularFingerprint(size=1024)

    This function computes putative binding pockets on this protein.

    This class uses the `ConvexHull` to compute binding pockets. Each

    face of the hull is converted into a coordinate box used for

    binding.

  def find_pockets(self, protein_file, ligand_file):

    """Compute features for a given complex

    Params

    ------

    macromolecule_file: str

      Location of the macromolecule file to load

    TODO(rbharath): This has a log of code overlap with

    compute_binding_pocket_features in

    examples/binding_pockets/binding_pocket_datasets.py. Find way to refactor

    to avoid code duplication.

    Returns

    -------

    List of pockets. Each pocket is a `CoordinateBox`

    """

    # if not ligand_file.endswith(".sdf"):

    #   raise ValueError("Only .sdf ligand files can be featurized.")

    # ligand_basename = os.path.basename(ligand_file).split(".")[0]

    # ligand_mol2 = os.path.join(

    #     self.base_dir, ligand_basename + ".mol2")

    #

    # # Write mol2 file for ligand

    # obConversion = ob.OBConversion()

    # conv_out = obConversion.SetInAndOutFormats(str("sdf"), str("mol2"))

    # ob_mol = ob.OBMol()

    # obConversion.ReadFile(ob_mol, str(ligand_file))

    # obConversion.WriteFile(ob_mol, str(ligand_mol2))

    #

    # # Featurize ligand

    # mol = Chem.MolFromMol2File(str(ligand_mol2), removeHs=False)

    # if mol is None:

    #   return None, None

    # # Default for CircularFingerprint

    # n_ligand_features = 1024

    # ligand_features = self.ligand_featurizer.featurize([mol])

    #

    # # Featurize pocket

    # pockets, pocket_atoms_map, pocket_coords = self.convex_finder.find_pockets(

    #     protein_file, ligand_file)

    # n_pockets = len(pockets)

    # n_pocket_features = BindingPocketFeaturizer.n_features

    #

    # features = np.zeros((n_pockets, n_pocket_features+n_ligand_features))

    # pocket_features = self.pocket_featurizer.featurize(

    #     protein_file, pockets, pocket_atoms_map, pocket_coords)

    # # Note broadcast operation

    # features[:, :n_pocket_features] = pocket_features

    # features[:, n_pocket_features:] = ligand_features

    # dataset = NumpyDataset(X=features)

    # pocket_preds = self.model.predict(dataset)

    # pocket_pred_proba = np.squeeze(self.model.predict_proba(dataset))

    #

    # # Find pockets which are active

    # active_pockets = []

    # active_pocket_atoms_map = {}

    # active_pocket_coords = []

    # for pocket_ind in range(len(pockets)):

    #   #################################################### DEBUG

    #   # TODO(rbharath): For now, using a weak cutoff. Fix later.

    #   #if pocket_preds[pocket_ind] == 1:

    #   if pocket_pred_proba[pocket_ind][1] > .15:

    #   #################################################### DEBUG

    #     pocket = pockets[pocket_ind]

    #     active_pockets.append(pocket)

    #     active_pocket_atoms_map[pocket] = pocket_atoms_map[pocket]

    #     active_pocket_coords.append(pocket_coords[pocket_ind])

    # return active_pockets, active_pocket_atoms_map, active_pocket_coords

    # # TODO(LESWING)

    raise ValueError("Karl Implement")

    coords = rdkit_util.load_molecule(

        macromolecule_file, add_hydrogens=False, calc_charges=False)[0]

    boxes = box_utils.get_face_boxes(coords, self.pad)

    boxes = box_utils.merge_overlapping_boxes(boxes)

    return boxes

"""

Docks protein-ligand pairs

Docks Molecular Complexes 

"""

__author__ = "Bharath Ramsundar"

__copyright__ = "Copyright 2016, Stanford University"

__license__ = "MIT"

import logging

import numpy as np

import os

import tempfile

from deepchem.data import DiskDataset

from deepchem.models import SklearnModel

from deepchem.models import MultitaskRegressor

from deepchem.dock.pose_scoring import GridPoseScorer

from deepchem.dock.pose_generation import VinaPoseGenerator

from sklearn.ensemble import RandomForestRegressor

from subprocess import call

logger = logging.getLogger(__name__)

class Docker(object):

  """Abstract Class specifying API for Docking."""

  def dock(self,

           protein_file,

           ligand_file,

           centroid=None,

           box_dims=None,

           dry_run=False):

    raise NotImplementedError

  """A generic molecular docking class

class VinaGridRFDocker(Docker):

  """Vina pose-generation, RF-models on grid-featurization of complexes."""

  This class provides a docking engine which uses provided models for

  featurization, pose generation, and scoring. Most pieces of docking

  software are command line tools that are invoked from the shell. The

  goal of this class is to provide a python clean API for invoking

  molecular docking programmatically.

  def __init__(self, exhaustiveness=10, detect_pockets=False):

    """Builds model."""

    self.base_dir = tempfile.mkdtemp()

    logger.info("About to download trained model.")

    call((

        "wget -nv -c http://deepchem.io.s3-website-us-west-1.amazonaws.com/trained_models/random_full_RF.tar.gz"

    ).split())

    call(("tar -zxvf random_full_RF.tar.gz").split())

    call(("mv random_full_RF %s" % (self.base_dir)).split())

    self.model_dir = os.path.join(self.base_dir, "random_full_RF")

    # Fit model on dataset

    model = SklearnModel(model_dir=self.model_dir)

    model.reload()

    self.pose_scorer = GridPoseScorer(model, feat="grid")

    self.pose_generator = VinaPoseGenerator(

        exhaustiveness=exhaustiveness, detect_pockets=detect_pockets)

  def dock(self,

           protein_file,

           ligand_file,

           centroid=None,

           box_dims=None,

           dry_run=False):

    """Docks using Vina and RF."""

    protein_docked, ligand_docked = self.pose_generator.generate_poses(

        protein_file, ligand_file, centroid, box_dims, dry_run)

    if not dry_run:

      score = self.pose_scorer.score(protein_docked, ligand_docked)

    else:

      score = np.zeros((1,))

    return (score, (protein_docked, ligand_docked))

'''

class VinaGridDNNDocker(object):

  """Vina pose-generation, DNN-models on grid-featurization of complexes."""

  The implementation of this class is lightweight and generic. It's

  expected that the majority of the heavy lifting will be done by pose

  generation and scoring classes that are provided to this class.

  """

  def __init__(self, exhaustiveness=10, detect_pockets=False):

    """Builds model."""

  def __init__(self,

               pose_generator,

               featurizer=None,

               scoring_model=None):

    """Builds model.

    Parameters

    ----------

    pose_generator: `PoseGenerator`

      The pose generator to use for this model

    featurizer: `ComplexFeaturizer`

      Featurizer associated with `scoring_model`

    scoring_model: `Model`

      Should make predictions on molecular complex.

    """

    self.base_dir = tempfile.mkdtemp()

    logger.info("About to download trained model.")

    call((

        "wget -nv -c http://deepchem.io.s3-website-us-west-1.amazonaws.com/trained_models/random_full_DNN.tar.gz"

    ).split())

    call(("tar -zxvf random_full_DNN.tar.gz").split())

    call(("mv random_full_DNN %s" % (self.base_dir)).split())

    self.model_dir = os.path.join(self.base_dir, "random_full_DNN")

    # Fit model on dataset

    pdbbind_tasks = ["-logKd/Ki"]

    n_features = 2052

    model = MultitaskRegressor(

        len(pdbbind_tasks),

        n_features,

        dropouts=[.25],

        learning_rate=0.0003,

        weight_init_stddevs=[.1],

        batch_size=64,

        model_dir=self.model_dir)

    model.reload()

    self.pose_scorer = GridPoseScorer(model, feat="grid")

    self.pose_generator = VinaPoseGenerator(

        exhaustiveness=exhaustiveness, detect_pockets=detect_pockets)

    self.pose_generator = pose_generator

    self.featurizer = featurizer

    self.scoring_model = scoring_model

  def dock(self,

           protein_file,

           ligand_file,

           molecular_complex,

           centroid=None,

           box_dims=None,

           dry_run=False):

    """Docks using Vina and DNNs."""

    protein_docked, ligand_docked = self.pose_generator.generate_poses(

        protein_file, ligand_file, centroid, box_dims, dry_run)

    if not dry_run:

      score = self.pose_scorer.score(protein_docked, ligand_docked)

           exhaustiveness=10,

           num_modes=9,

           num_pockets=None,

           out_dir=None):

    """Docks using Vina and RF.

    Parameters

    ----------

    molecular_complex: Object

      Some representation of a molecular complex.

    exhaustiveness: int, optional (default 10)

      Tells Autodock Vina how exhaustive it should be with pose

      generation.

    num_modes: int, optional (default 9)

      Tells Autodock Vina how many binding modes it should generate at

      each invocation.

    num_pockets: int, optional (default None)

      If specified, `self.pocket_finder` must be set. Will only

      generate poses for the first `num_pockets` returned by

      `self.pocket_finder`.

    out_dir: str, optional

      If specified, write generated poses to this directory.

    """

    complexes = self.pose_generator.generate_poses(molecular_complex,

                                                   centroid=centroid,

                                                   box_dims=box_dims,

                                                   exhaustiveness=exhaustiveness,

                                                   num_modes=num_modes,

                                                   num_pockets=num_pockets,

                                                   out_dir=out_dir)

    for posed_complex in complexes:

      if self.featurizer is not None:

        # TODO: How to handle the failure here?

        features, _ = self.featurizer.featurize_complexes([molecular_complex])

        dataset = NumpyDataset(X=features)

        score = self.model.predict(dataset)

        yield (score, posed_complex)

      else:

      score = np.zeros((1,))

    return (score, (protein_docked, ligand_docked))

'''

        yield posed_complex