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Docking.py
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Docking.py
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import os
import sys
import yaml
import re
import math
import itertools
import copy, time
import numpy as np
from numpy import exp, sum, array, where, sqrt, any, std, mean, dot, zeros, ones
from numpy.random import randint, normal
from numpy.linalg import norm
from io import StringIO
from mpi4py import MPI
from random import choice, sample
from math import ceil
from multiprocessing import Pool, cpu_count
# PyRosetta import
import pyrosetta as pr
# PyRosetta mover and pakcer imports
from pyrosetta import Pose
from pyrosetta.rosetta.core.id import AtomID
from pyrosetta.rosetta.core.scoring import EMapVector
from pyrosetta.rosetta.core.kinematics import MoveMap
from pyrosetta.rosetta.core.pack.task.operation import ReadResfile
from pyrosetta.rosetta.core.pack.task import TaskFactory
from pyrosetta.rosetta.protocols.minimization_packing import PackRotamersMover, MinMover
from pyrosetta.rosetta.protocols.constraint_movers import ClearConstraintsMover
from pyrosetta.rosetta.protocols.relax import FastRelax
from pyrosetta.rosetta.protocols.denovo_design.movers import FastDesign
from pyrosetta.rosetta.core.scoring.constraints import BoundFunc, AtomPairConstraint, AmbiguousConstraint, \
ResidueTypeConstraint
from pyrosetta.rosetta.core.scoring import ScoreType
from pyrosetta.rosetta.std import ostringstream, istringstream
from pyrosetta.rosetta.protocols.geometry import centroid_by_residues, centroid_by_chain
from pyrosetta.rosetta.core.scoring import score_type_from_name
from pyrosetta.rosetta.protocols.docking import setup_foldtree
from pyrosetta.rosetta.protocols.grafting import delete_region, replace_region, return_region, insert_pose_into_pose
from pyrosetta.rosetta.protocols.rigid import RigidBodyPerturbMover
from pyrosetta.rosetta.protocols.toolbox.pose_manipulation import rigid_body_move
from pyrosetta.rosetta.protocols.simple_moves import ShearMover
from pyrosetta.rosetta.core.kinematics import FoldTree
from pyrosetta.rosetta.numeric import xyzVector_double_t
from pyrosetta.rosetta.core.select.residue_selector import ChainSelector, NeighborhoodResidueSelector, \
ResidueIndexSelector
# PyRosetta vectors
from pyrosetta.rosetta.utility import vector1_std_shared_ptr_const_core_conformation_Residue_t
from pyrosetta.rosetta.numeric import xyzVector_double_t
from pyrosetta.rosetta.utility import vector1_core_id_AtomID
from pyrosetta.rosetta.utility import vector1_numeric_xyzVector_double_t
from pyrosetta.rosetta.utility import vector1_bool
from scipy.spatial.distance import pdist, cdist, squareform
# Biotite import
from biotite.structure.io.pdb import PDBFile
from biotite.structure import sasa, annotate_sse, apply_residue_wise
# MD Analysis imports
from MDAnalysis.lib.NeighborSearch import AtomNeighborSearch
# BIO Python imports
from Bio.PDB.Polypeptide import one_to_three
# Local imports
import Energy
import Constants
from BaseClases import BaseConstraint, Residue, AminoAcids, DesignDomainWildCards, Ligand
from MiscellaneousUtilityFunctions import killProccesses, getKT, printDEBUG, getScoreFunction
class LigandPackingMover(object):
def __init__(self, ligand: Ligand = None,
pose: Pose = None,
nonStandardNeighbors: list = None,
neighborDistCutoff: float = 15.0,
mainChainCoupling: float = 1.0,
sideChainCoupling: float = 0.0,
sideChainCouplingMax: float = 1.0,
sideChainCouplingExcludedPoseIndex: list = None,
sideChainsGridPoints: list = None,
sideChainsGridLimit: list = None,
maxGrid: int = 12,
minGrid: int = 6,
gridInterval: float = 360.0,
numberOfGridNeighborhood=2,
packingLoops: int = 1,
minimization: bool = True,
fullEnergy=False,
nRandomTorsionPurturbation= 1,
nproc=1):
# self.aminoAcids = AminoAcids()
self.sideChainsGridRanges = None
self.neighborDistCutoff = None
self.neighborsPoseIndex = None
self.neighborsAtomId = vector1_core_id_AtomID()
self.ligandAtomId = vector1_core_id_AtomID()
self.neighborsCharges = None
self.neighborsLjRadii = None
self.neighborsLjEps = None
self.neighborsLkdG = None
self.neighborsLkLam = None
self.neighborsLkVol = None
self.self_qij = None
self.self_dljr = None
self.self_dljep = None
self.self_self_sol_prefactor = None
self.pair_qij = None
self.pair_dljr = None
self.pair_dljep = None
self.pair_sol_prefactor = None
self.neighborsSideChainFlagVector = None
self.neighborsBackboneFlagVector = None
self.neighborsXYZVector1 = vector1_numeric_xyzVector_double_t()
self.ligandXYZVector1 = vector1_numeric_xyzVector_double_t()
self.neighborsXYZ = None
self.ligandXYZ = None
self.ligandNonBondedXYZIndex = None
self.ligandNonBondedWeights = None
self.ligand = None
self.nonStandardNeighborsDict = None
self.minimization = minimization
self.sideChainCoupling = sideChainCoupling
self.sideChainCouplingMax = sideChainCouplingMax
self.mainChainCoupling = mainChainCoupling
self.sideChainCouplingExcludedPoseIndex = sideChainCouplingExcludedPoseIndex
self.solWeight = 0.035
self.atrWeight = 1.000
self.elcWeight = 1.000
self.repWeight_self_0 = 0.500
self.repWeight_self_1 = 0.500
self.repWeight_self_2 = 0.500
self.repWeight_pair_0 = 0.150
self.repWeight_pair_1 = 0.300
self.repWeight_pair_2 = 0.500
self.bondConstraintForce = 1000
self.angleConstraintForce = 500
self.bondXYZIndex = None
self.bondsLenghts = None
self.bondConstraints = None
self.angleConstraints = None
self._packingLoops = None
self.packingLoops = packingLoops
self.fullEnergy = fullEnergy
self.maxGrid = maxGrid
self.minGrid = minGrid
self.gridInterval = gridInterval
self.numberOfGridNeighborhood = numberOfGridNeighborhood
self.neighborDistCutoff = neighborDistCutoff
self.sideChainPackingScore = pr.get_fa_scorefxn()
self.ligandPackingScore = pr.get_fa_scorefxn()
self.minimizer = MinMover()
self.movemap = MoveMap()
self.minimizationCutoff = 0.1
self.max_cycle = 100
self.max_line_search_step = 0.25
self.nRandomTorsionPurturbation = nRandomTorsionPurturbation
self.nproc = nproc
os.environ["OMP_NUM_THREADS"] = str(nproc)
if ligand:
self.initiate(ligand, pose, nonStandardNeighbors, neighborDistCutoff, sideChainsGridPoints,
sideChainsGridLimit)
def initiate(self, ligand,
pose,
nonStandardNeighbors,
neighborDistCutoff=15.0,
sideChainsGridPoints=None,
sideChainsGridLimit=None,
sideChainCouplingExcludedPoseIndex=None):
self.ligand = ligand
self.neighborDistCutoff = neighborDistCutoff
# initiate grid ranges for each torsion level of each side chain
if ligand.numberOfSideChains > 0:
self.getSideChainsGridRanges(sideChainsGridPoints, sideChainsGridLimit)
self.sideChainCouplingExcludedPoseIndex = []
if sideChainCouplingExcludedPoseIndex is not None:
self.sideChainCouplingExcludedPoseIndex = sideChainCouplingExcludedPoseIndex
if pose is not None:
# initiate the score function
self.sideChainPackingScore(pose)
self.getLigandPoseIndex(pose)
self.getNonStandardNeighborsDict(nonStandardNeighbors)
self.getligandProperties(pose)
self.getNeighborsPoseIndex(pose)
self.getNeighborsProperties(pose)
self.getMininizerMovemap()
@property
def packingLoops(self):
return self._packingLoops
@packingLoops.setter
def packingLoops(self, packingLoops):
self._packingLoops = packingLoops
# Allocate a list of poses for keeping the results of sidechain packing for each packing loop
self.packingFinalPoses = list()
for i in range(self.packingLoops):
self.packingFinalPoses.append(Pose())
def getMininizerMovemap(self):
self.movemap.clear()
# Add the neighbors
# for index in self.neighborsPoseIndex:
# if self.backboneMinimization:
# self.movemap.set_bb(index)
# self.movemap.set_chi(index)
# Add the ligand
self.movemap.set_bb(self.ligand.poseIndex)
self.movemap.set_chi(self.ligand.poseIndex)
self.minimizer.set_movemap(self.movemap)
self.minimizer.score_function(self.ligandPackingScore)
def getSideChainsGridRanges(self, sideChainsGridPoints=None, sideChainsGridLimit=None):
"""
sideChainsGridPoints is a dictionaries that contains chi index as key and a tuple containing
number of grid and interval (n, I) as values, e.g.
{ChiIndex: (n,I), ChiIndex: (n,I), ChiIndex: (n,I)}
sideChainsGridLimit is a list of tuple for each side chain containing (MaxGrid, MinGrid, Interval)
[(Max, Min, Interval),
[(Max, Min, Interval),
[(Max, Min, Interval)]
If neither is given, maxGrid, minGrid, gridInterval is used for all side chains.
"""
# Check the sideChainsGridLimit
if sideChainsGridLimit is not None:
if len(sideChainsGridLimit) != self.ligand.numberOfSideChains:
raise ValueError(
'Bad sideChainsGridLimit, Ligand {}{} contains {} side chains, {} GridLimits are given'.
format(self.ligand.chain, self.ligand.ID, self.ligand.numberOfSideChains,
len(sideChainsGridPoints)))
# for each side chain compute a gridRangeMatrix.
self.sideChainsGridRanges = []
self.sideChainsRandomInterval = []
for sideChainIndex in range(self.ligand.numberOfSideChains):
if sideChainsGridLimit is not None:
sideChainGridLimit = sideChainsGridLimit[sideChainIndex]
else:
sideChainGridLimit = None
self.getGridRanges(sideChainIndex, sideChainsGridPoints, sideChainGridLimit)
# print(self.sideChainsGridRanges)
def getGridRanges(self, sideChainIndex, sideChainGridPoints=None, sideChainGridLimit=None):
"""
Initiates the grid Ranges
:param sideChainIndex: index of ligand side chain
:param sideChainGridPoints: list of tup
:param gridsPoints: list of grid points for torsion level
"""
# self.gridRanges = list()
# MaxDepth = 10
# Sort out limit if given.
if sideChainGridLimit is None:
maxGrid = self.maxGrid
minGrid = self.minGrid
gridInterval = self.gridInterval
else:
maxGrid = sideChainGridLimit[0]
minGrid = sideChainGridLimit[1]
gridInterval = sideChainGridLimit[2]
# Make sure all gridPoints should be even
maxGrid += maxGrid % 2
minGrid += minGrid % 2
# Generate the side chain grid ranges
gridRanges = []
randomInterval = []
currentGrid = maxGrid
for levelIndex, level in enumerate(self.ligand.sideChainsOrderedIndex[sideChainIndex]):
for torsionIndex, torsionPoseIndex in enumerate(level):
result = []
# Read if given explicitly
if sideChainGridPoints is not None and torsionPoseIndex in sideChainGridPoints.keys():
gridpoint, interval = sideChainGridPoints[torsionPoseIndex]
# Or set by limits
else:
gridpoint, interval = currentGrid, gridInterval
# Set the random interval torsions if interval number is given
if interval < 0:
# save the interval number
randomInterval.append(-1 * interval)
# convert interval number to interval in degrees
interval = -360/interval
else:
randomInterval.append(0)
# Get the grid range
self._getGridRangeRecursivly(gridpoint, interval, result)
gridRanges.append(result)
# Update current grid for each level
currentGrid -= 2
if currentGrid < minGrid:
currentGrid = minGrid
# Convert it to a list of arrays
sideChainGridRanges = []
gridRanges = array(gridRanges, dtype=object).transpose()
for packingLevel in gridRanges:
sideChainGridRanges.append([])
for gridRange in packingLevel:
sideChainGridRanges[-1].append(array(gridRange))
# print('BBBB', sideChainGridRanges)
# for i in sideChainGridRanges:
# for j in i:
# print(len(j), ['{:.1f}'.format(k) for k in j])
self.sideChainsGridRanges.append(sideChainGridRanges)
self.sideChainsRandomInterval.append(array(randomInterval, dtype=np.int))
def _getGridRangeRecursivly(self, numberOfGridPoints, interval=360.0, result=[], level=0):
# Max minimization level
MaxLevel = 2
spacing = interval / numberOfGridPoints
if level == 0: # The first level interval corresponds to 360 degrees, hence 180 == -180
lowerBound = -int(numberOfGridPoints / 2)
higherBound = int(numberOfGridPoints / 2)
else:
lowerBound = -int(numberOfGridPoints / 2)
higherBound = int(numberOfGridPoints / 2) + 1
result.append([spacing * i for i in range(lowerBound, higherBound)])
if level < MaxLevel:
level += 1
numberOfGridPoints = int(numberOfGridPoints / 1.5)
self._getGridRangeRecursivly(numberOfGridPoints, 2 * spacing, result, level)
def getSideChainGrid(self, sideChainIndex, minimizationLevel, torsionCenters=None):
sideChainChiIndices = []
sideChainGrid = None
gridRanges = []
# print('torsionCenters', torsionCenters)
# print('sideChainOrderedIndex', sideChainOrderedIndex)
torsionIndex = 0
for levelIndex, level in enumerate(self.ligand.sideChainsOrderedIndex[sideChainIndex]):
for torsionLevelIndex, torsionPoseIndex in enumerate(level):
# Keep indices in order
sideChainChiIndices.append(torsionPoseIndex)
# get the center of grid range
if torsionCenters is not None:
center = torsionCenters[torsionIndex]
else:
center = 0.0
# print('****center', center, torsionIndex)
gridRanges.append(self.sideChainsGridRanges[sideChainIndex][minimizationLevel][torsionIndex] + center)
torsionIndex += 1
# wrap the angles
for gridRangeIndex in range(len(gridRanges)):
for angleIndex in range(len(gridRanges[gridRangeIndex])):
angle = gridRanges[gridRangeIndex][angleIndex]
if angle >= 180:
gridRanges[gridRangeIndex][angleIndex] = angle - 360
elif angle < -180:
gridRanges[gridRangeIndex][angleIndex] = angle + 360
# print(minimizationLevel, gridRanges[gridRangeIndex])
# Add one more column for energies
gridRanges.append([0.0])
# print(gridRanges)
sideChainGrid = array(np.meshgrid(*gridRanges), dtype=np.float).T.reshape(-1, len(gridRanges))
# print(gridRanges)
# print(sideChainGrid, len(sideChainGrid))
return array(sideChainChiIndices, dtype=np.intc), sideChainGrid
def updateProperties(self, pose):
self.getLigandPoseIndex(pose)
self.getNeighborsPoseIndex(pose)
self.getligandProperties(pose)
self.getNeighborsProperties(pose)
def apply(self, pose, sideChainIndex=None):
# Check n side chains
self.updateProperties(pose)
self.getLigandXYZ(pose)
self.getNeighborsXYZ(pose)
if sideChainIndex is None:
for i in range(self.packingLoops):
# it packs the side chains on the same structure an alternative approach
# would be to keep the starting pose and repack starting with that !!!
for sideChainIndex in sample(range(self.ligand.numberOfSideChains), self.ligand.numberOfSideChains):
#print("BBB Activating min and loop: ", i, " sc", sideChainIndex)
self.minimumEnergyNeighborhoodSearch(pose, sideChainIndex)
self.packingFinalPoses[i].assign(pose)
# minimize ang the the energies
poseFinalEnergies = list()
# self.getMininizerMovemap()
# counter = 0
for packingFinalPose in self.packingFinalPoses:
if self.minimization:
# packingFinalPose.dump_pdb('transformed-min1.pdb')
# E_min1 = self.ligandPackingScore(packingFinalPose)
self.ligandMinimization(packingFinalPose)
# packingFinalPose.dump_pdb('transformed-min2.pdb')
# E_min2 = self.ligandPackingScore(packingFinalPose)
# print('E_min1: ', E_min1, 'E_min2: ', E_min2)
poseFinalEnergies.append(self.ligandPackingScore(packingFinalPose))
# assign the best pose
index = np.argmin(poseFinalEnergies)
pose.assign(self.packingFinalPoses[index])
else:
self.minimumEnergyNeighborhoodSearch(pose, sideChainIndex)
if self.minimization:
self.ligandMinimization(pose)
def ligandMinimization(self, pose):
"""
Minimizes using the reduced potential
:param pose:
"""
self.updateProperties(pose)
self.getLigandXYZ(pose)
self.getNeighborsXYZ(pose)
weights_self = array([self.repWeight_self_2, self.atrWeight, self.elcWeight, self.solWeight])
weights_pair = array([self.repWeight_pair_2, self.atrWeight, self.elcWeight, self.solWeight])
# print('xyz IN', self.ligandXYZ)
if len(self.neighborsXYZ) != 0:
Energy.ligand_steep_decent(self.ligandXYZ,
self.neighborsXYZ,
self.self_qij,
self.self_dljr,
self.self_dljep,
self.self_self_sol_prefactor,
self.ligand.lkLam,
self.ligandNonBondedXYZIndex,
self.ligandNonBondedWeights,
self.mainChainCoupling,
weights_self,
self.bondXYZIndex,
self.bondsLenghts,
self.bondConstraints,
self.angleXYZIndex,
self.anglesLenghts,
self.angleConstraints,
self.ligand.ligandMasks,
self.pair_qij,
self.pair_dljr,
self.pair_dljep,
self.pair_sol_prefactor,
self.neighborsLkLam,
self.neighborsSideChainFlagVector,
self.neighborsBackboneFlagVector,
self.sideChainCoupling,
self.mainChainCoupling,
weights_pair,
self.minimizationCutoff,
self.max_cycle,
self.max_line_search_step)
# print('xyz out', self.ligandXYZ)
self.setLigandXYZ(pose)
def minimumEnergyNeighborhoodSearch(self, pose, sideChainIndex):
"""
Performs ligand side chain packing with minimum neighbor search
:param pose:
"""
# Initiate torsion with either by their current center from pose or from a random interval if it is set
# The torsion centers has one extra column
#print(self.sideChainsRandomInterval[sideChainIndex].nonzero())
sideChainRandomIntervalIndices = list(self.sideChainsRandomInterval[sideChainIndex].nonzero()[0])
if self.nRandomTorsionPurturbation > len(sideChainRandomIntervalIndices):
sampleSize = len(sideChainRandomIntervalIndices)
else:
sampleSize = self.nRandomTorsionPurturbation
selectedSideChainRandomIntervalIndices = sample(sideChainRandomIntervalIndices, sampleSize)
sideChainCenters = []
ChiIndex = 0
for levelIndex, level in enumerate(self.ligand.sideChainsOrderedIndex[sideChainIndex]):
for torsionLevelIndex, torsionPoseIndex in enumerate(level):
if ChiIndex in selectedSideChainRandomIntervalIndices:
# Choose a random interval in the neighborhood of current angle
numberOfInterval = self.sideChainsRandomInterval[sideChainIndex][ChiIndex]
interval = 360/numberOfInterval
randomIntervalNumber = np.random.randint(-1, 2)
# get current torsion center
torsionCenter = pose.chi(torsionPoseIndex, self.ligand.poseIndex)
# Move the current center to the neighboring interval (-1, 1), or don't change (0)
# for angle<0 forward direction is counter clock wise
if torsionCenter < 0:
torsionCenter = torsionCenter - (randomIntervalNumber * interval)
# for angle>=0 forward direction is clock wise
elif torsionCenter >= 0:
torsionCenter = torsionCenter + (randomIntervalNumber * interval)
#print("BBBB", torsionCenter, randomIntervalNumber, ChiIndex)
if torsionCenter >= 180:
torsionCenter = torsionCenter - 360
elif torsionCenter < -180:
torsionCenter = torsionCenter + 360
else: # Choose the current value as the torsion center
torsionCenter = pose.chi(torsionPoseIndex, self.ligand.poseIndex)
sideChainCenters.append(pose.chi(torsionPoseIndex, self.ligand.poseIndex))
ChiIndex += 1
sideChainCenters.append(0)
# print("Docking line 493 in minimumEnergyNeighborhoodSearch: ", sideChainCenters)
# torsionCenters_curt should be list of lists for neighbourhood search
torsionCenters_curt = [sideChainCenters]
#sideChainMask = self.ligand.sideChainMasks[sideChainIndex]
for minimizationLevel in range(3):
torsionCenters_next = []
####################################
# print('minimizationLevel', minimizationLevel)
# for torsion in torsionCenters_curt:
# print(['{:.1f}'.format(i) for i in torsion])
####################################
for torsionCenter in torsionCenters_curt:
# get grids, ignore the energy column (last one)
chis, grid = self.getSideChainGrid(sideChainIndex, minimizationLevel, torsionCenter[:-1])
if self.fullEnergy:
if self.nproc > 1: # and grid.shape[0] > 5500:
torsionCenters_next.extend(self._gridSearch_parallel_fullEnergy(pose, sideChainIndex, chis, grid, minimizationLevel))
else:
torsionCenters_next.extend(self._gridSearch_fullEnergy(pose, sideChainIndex, chis, grid, minimizationLevel))
else :
if self.nproc > 1:
torsionCenters_next.extend(self._gridSearch_reduced_parallel(pose, sideChainIndex, chis, grid, minimizationLevel))
#torsionCenters_next.extend(self._gridSearch_parallel(pose, sideChainIndex, chis, grid, minimizationLevel))
else:
torsionCenters_next.extend(self._gridSearch_reduced(pose, sideChainIndex, chis, grid, minimizationLevel))
#torsionCenters_next.extend(self._gridSearch(pose, sideChainIndex, chis, grid, minimizationLevel))
torsionCenters_curt = torsionCenters_next
####################################
# print('minimizationLevel Final')
# for torsion in torsionCenters_curt:
# print(['{:.1f}'.format(i) for i in torsion])
####################################
# Sort by energy torsionCenters are python list here.
torsionCenters_curt.sort(key=lambda element: element[-1])
# for i, center in enumerate(torsionCenters_curt):
# for j in range(len(center)-1):
# pose.set_chi(chis[j], self.ligand.poseIndex, center[j])
# energy = self.score(pose)
# pose.dump_pdb('pack_cluster_{}_{}_{}.pdb'.format(minimizationLevel, i, energy))
# print('{} {} {:.1f}'.format(minimizationLevel, str(['{:.1f}'.format(i) for i in center]), energy))
center = torsionCenters_curt[0]
# Set the pose according to the best result.
for j in range(len(center) - 1):
pose.set_chi(chis[j], self.ligand.poseIndex, center[j])
def _gridSearch_reduced(self, pose, sideChainIndex, chis, grid, minimizationLevel):
"""
Performs a grid search with EDesign energy function
:param chis:
:param grid:
:param minimizationLevel:
:return:
"""
# update weights
if minimizationLevel == 0:
weights_self = array([self.repWeight_self_0, self.atrWeight, self.elcWeight, self.solWeight])
weights_pair = array([self.repWeight_pair_0, self.atrWeight, self.elcWeight, self.solWeight])
elif minimizationLevel == 1:
weights_self = array([self.repWeight_self_1, self.atrWeight, self.elcWeight, self.solWeight])
weights_pair = array([self.repWeight_pair_1, self.atrWeight, self.elcWeight, self.solWeight])
elif minimizationLevel == 2:
weights_self = array([self.repWeight_self_2, self.atrWeight, self.elcWeight, self.solWeight])
weights_pair = array([self.repWeight_pair_2, self.atrWeight, self.elcWeight, self.solWeight])
else:
raise ValueError('Max 3 minimum energy neighborhood level is allowed.')
self.getLigandXYZ(pose)
#print('BBBBB ->>>>>', grid.shape)
Energy.grid_search(grid,
self.ligandXYZ,
self.neighborsXYZ,
self.self_qij,
self.self_dljr,
self.self_dljep,
self.self_self_sol_prefactor,
self.ligand.lkLam,
self.ligandNonBondedXYZIndex,
self.ligandNonBondedWeights,
self.mainChainCoupling,
weights_self,
self.ligand.sideChainMasks[sideChainIndex],
self.ligand.sideChainsChiXYZIndices[sideChainIndex],
self.ligand.sideChainsSubTreeMasks[sideChainIndex],
self.pair_qij,
self.pair_dljr,
self.pair_dljep,
self.pair_sol_prefactor,
self.neighborsLkLam,
self.neighborsSideChainFlagVector,
self.neighborsBackboneFlagVector,
self.sideChainCoupling,
self.mainChainCoupling,
weights_pair)
# print([i for i in grid[i]])
grid = grid[grid[:, -1].argsort()]
nGridstoCluster = 50
clusters = Energy.select_torsions_by_distance(grid[:nGridstoCluster, :], K=self.numberOfGridNeighborhood,
cutoff_percent=0.02)
#print('BBBB ----E', clusters[0, :], minimizationLevel)
if minimizationLevel == 0:
return clusters[:, :]
else:
return [clusters[0, :]]
# print(torsionCenters)
def _gridSearch_reduced_parallel(self, pose, sideChainIndex, chis, grid, minimizationLevel):
"""
Performs a grid search with EDesign energy function
:param chis:
:param grid:
:param minimizationLevel:
:return:
"""
# update weights
if minimizationLevel == 0:
weights_self = array([self.repWeight_self_0, self.atrWeight, self.elcWeight, self.solWeight])
weights_pair = array([self.repWeight_pair_0, self.atrWeight, self.elcWeight, self.solWeight])
elif minimizationLevel == 1:
weights_self = array([self.repWeight_self_1, self.atrWeight, self.elcWeight, self.solWeight])
weights_pair = array([self.repWeight_pair_1, self.atrWeight, self.elcWeight, self.solWeight])
elif minimizationLevel == 2:
weights_self = array([self.repWeight_self_2, self.atrWeight, self.elcWeight, self.solWeight])
weights_pair = array([self.repWeight_pair_2, self.atrWeight, self.elcWeight, self.solWeight])
else:
raise ValueError('Max 3 minimum energy neighborhood level is allowed.')
self.getLigandXYZ(pose)
#print('BBBBB ->>>>>', grid.shape)
Energy.grid_search_parallel(grid,
self.ligandXYZ,
self.neighborsXYZ,
self.self_qij,
self.self_dljr,
self.self_dljep,
self.self_self_sol_prefactor,
self.ligand.lkLam,
self.ligandNonBondedXYZIndex,
self.ligandNonBondedWeights,
self.mainChainCoupling,
weights_self,
self.ligand.sideChainMasks[sideChainIndex],
self.ligand.sideChainsChiXYZIndices[sideChainIndex],
self.ligand.sideChainsSubTreeMasks[sideChainIndex],
self.pair_qij,
self.pair_dljr,
self.pair_dljep,
self.pair_sol_prefactor,
self.neighborsLkLam,
self.neighborsSideChainFlagVector,
self.neighborsBackboneFlagVector,
self.sideChainCoupling,
self.mainChainCoupling,
weights_pair,
self.nproc)
#print('--------------------------------', minimizationLevel)
# print([i for i in grid[i]])
grid = grid[grid[:, -1].argsort()]
nGridstoCluster = 50
clusters = Energy.select_torsions_by_distance(grid[:nGridstoCluster, :], K=self.numberOfGridNeighborhood,
cutoff_percent=0.02)
#print('BBBB ----E', clusters[0, :], minimizationLevel)
if minimizationLevel == 0:
return clusters[:, :]
else:
return [clusters[0, :]]
# print(torsionCenters)
def _gridSearch_fullEnergy(self, pose, sideChainIndex, chis, grid, minimizationLevel):
"""
Performs a grid search with EDesign energy function
:param chis:
:param grid:
:param minimizationLevel:
:return:
"""
# update weights
if minimizationLevel == 0:
weights_self = array([self.repWeight_self_0, self.atrWeight, self.elcWeight, self.solWeight])
weights_pair = array([self.repWeight_pair_0, self.atrWeight, self.elcWeight, self.solWeight])
elif minimizationLevel == 1:
weights_self = array([self.repWeight_self_1, self.atrWeight, self.elcWeight, self.solWeight])
weights_pair = array([self.repWeight_pair_1, self.atrWeight, self.elcWeight, self.solWeight])
elif minimizationLevel == 2:
weights_self = array([self.repWeight_self_2, self.atrWeight, self.elcWeight, self.solWeight])
weights_pair = array([self.repWeight_pair_2, self.atrWeight, self.elcWeight, self.solWeight])
else:
raise ValueError('Max 3 minimum energy neighborhood level is allowed.')
for i in range(grid.shape[0]):
energy = 0.0
# generate the pose
for j in range(len(chis)):
# print(chis[j], grid[i, j])
pose.set_chi(chis[j], self.ligand.poseIndex, grid[i, j])
#if i == 0:
# print(grid[i, j], '==', pose.chi(chis[j], self.ligand.poseIndex))
self.sideChainPackingScore.set_weight(ScoreType.fa_rep, weights_pair[0])
energy = self.sideChainPackingScore(pose)
grid[i, -1] = energy
#print('BBBB ----E', grid[0, :])
grid = grid[grid[:, -1].argsort()]
nGridstoCluster = 50
clusters = Energy.select_torsions_by_distance(grid[:nGridstoCluster, :], K=self.numberOfGridNeighborhood,
cutoff_percent=0.02)
#print('BBBB ----E', clusters[0, :])
if minimizationLevel == 0:
return clusters[:, :]
else:
return [clusters[0, :]]
# print(torsionCenters)
def _gridSearch_parallel_fullEnergy(self, pose, sideChainIndex, chis, grid, minimizationLevel):
"""
Performs a grid search with EDesign energy function
:param chis:
:param grid:
:param minimizationLevel:
:return:
"""
# update weights
if minimizationLevel == 0:
weights_self = array([self.repWeight_self_0, self.atrWeight, self.elcWeight, self.solWeight])
weights_pair = array([self.repWeight_pair_0, self.atrWeight, self.elcWeight, self.solWeight])
elif minimizationLevel == 1:
weights_self = array([self.repWeight_self_1, self.atrWeight, self.elcWeight, self.solWeight])
weights_pair = array([self.repWeight_pair_1, self.atrWeight, self.elcWeight, self.solWeight])
elif minimizationLevel == 2:
weights_self = array([self.repWeight_self_2, self.atrWeight, self.elcWeight, self.solWeight])
weights_pair = array([self.repWeight_pair_2, self.atrWeight, self.elcWeight, self.solWeight])
else:
raise ValueError('Max 3 minimum energy neighborhood level is allowed.')
args = list()
#self.nproc
step = int(grid.shape[0] / self.nproc)
lb = 0
for i in range(self.nproc):
if i == self.nproc - 1: # end partition
hb = grid.shape[0]
else:
hb = i * step + step
args.append((pose, self.ligand.poseIndex, chis, grid[lb: hb], weights_pair))
lb = hb
# print(type(chis), type(grid), type(sideChainCoupling), type(mainChainCoupling))
with Pool(self.nproc) as pool:
energies = pool.starmap(LigandPackingMover._gridPointEvaluation_fullEnergy, args)
grid[:, -1] = [energy for chunk in energies for energy in chunk]
grid = grid[grid[:, -1].argsort()]
nGridstoCluster = 50
clusters = Energy.select_torsions_by_distance(grid[:nGridstoCluster, :], K=self.numberOfGridNeighborhood,
cutoff_percent=0.02)
if minimizationLevel == 0:
return clusters[:, :]
else:
return [clusters[0, :]]
# print(torsionCenters)
@staticmethod
def _gridPointEvaluation_fullEnergy(pose, ligandPoseIndex, chis, grid, weights_pair):
energies = list()
score = pr.get_fa_scorefxn()
for i in range(grid.shape[0]):
for j in range(len(chis)):
pose.set_chi(chis[j], ligandPoseIndex, grid[i, j])
score.set_weight(ScoreType.fa_rep, weights_pair[0])
energy = score(pose)
energies.append(energy)
return energies
def getNeighborsXYZ(self, pose):
"""
Get the coordinate of neighbor residues
:param pose:
"""
self.neighborsXYZVector1.clear()
pose.batch_get_xyz(self.neighborsAtomId, self.neighborsXYZVector1)
self.neighborsXYZ = array([[atom.x, atom.y, atom.z] for atom in self.neighborsXYZVector1])
def getLigandXYZ(self, pose):
"""
Get the coordinate of neighbor residues
:param pose:
"""
self.ligandXYZVector1.clear()
pose.batch_get_xyz(self.ligandAtomId, self.ligandXYZVector1)
self.ligandXYZ = array([[atom.x, atom.y, atom.z] for atom in self.ligandXYZVector1])
def setLigandXYZ(self, pose):
self.ligandXYZVector1.clear()
for ri in self.ligandXYZ:
self.ligandXYZVector1.append(xyzVector_double_t(*ri))
pose.batch_set_xyz(self.ligandAtomId, self.ligandXYZVector1)
def getLigandXYZ_direct(self, pose):
"""
Get the coordinate of neighbor residues
:param pose:
"""
ligandRes = pose.residue(self.ligand.poseIndex)
self.ligandXYZ = array(
[[ligandRes.xyz(i).x, ligandRes.xyz(i).y, ligandRes.xyz(i).z] for i in range(1, ligandRes.natoms() + 1)])
def getLigandPoseIndex(self, pose):
self.ligand.poseIndex = pose.pdb_info().pdb2pose(self.ligand.chain, self.ligand.ID)
def getligandProperties(self, pose):
"""
Computes the atom ID vector of the ligand. Other properties
are already precomputed in the ligand object.
:param pose:
"""
self.getLigandPoseIndex(pose)
self.ligandAtomId.clear()
# Get atom id vector
# couter = 0
for atomIndex in self.ligand.atoms:
self.ligandAtomId.append(AtomID(atomIndex, self.ligand.poseIndex))
# print('BBB ligand atom name', couter, pose.residue(self.ligand.poseIndex).atom_name(atomIndex))
# couter += 1
# get non bonded indices
self.ligandNonBondedXYZIndex = array(self.ligand.nonBondedAtomsPairs, dtype=np.int32) - 1
self.ligandNonBondedWeights = array(self.ligand.nonBondedAtomsPairsWeights, dtype=np.float64)
# get bond indices, equilibrium length
self.bondXYZIndex = array(self.ligand.bonds, dtype=np.int32) - 1
self.bondsLenghts = array(self.ligand.bondsLengths, dtype=np.float64)
self.bondConstraints = ones(len(self.ligand.bonds)) * self.bondConstraintForce
# get bond indices, equilibrium length
self.angleXYZIndex = array(self.ligand.angles, dtype=np.int32) - 1
self.anglesLenghts = array(self.ligand.anglesLengths, dtype=np.float64)
self.angleConstraints = ones(len(self.ligand.angles)) * self.angleConstraintForce
dimension = self.ligandNonBondedXYZIndex.shape[0]
self.self_qij = zeros(dimension)
self.self_dljr = zeros(dimension)
self.self_dljep = zeros(dimension)
self.self_self_sol_prefactor = zeros((dimension, 2))
# precompute the energy matrices
Energy.ligand_ligand_matrices(self.ligand.charges, self.self_qij,
self.ligand.ljRadii, self.self_dljr,
self.ligand.ljEps, self.self_dljep,
self.ligand.lkdG, self.ligand.lkLam, self.ligand.lkVol,
self.self_self_sol_prefactor,
self.ligandNonBondedXYZIndex)
def getNeighborsProperties(self, pose):
"""
Compute the Atom ID, charges and Vdw vectors for neighbors and neighbors backbone. These
are needed for batch coordinate retrieval
:param pose:
"""
# clean the previous ones
self.neighborsAtomId.clear()
self.neighborsCharges = list()
self.neighborsLjRadii = list()
self.neighborsLjEps = list()
self.neighborsLkdG = list()
self.neighborsLkLam = list()
self.neighborsLkVol = list()
self.neighborsSideChainFlagVector = list()
self.neighborsBackboneFlagVector = list()
self.neighborsNumberOfAtoms = 0
# self.neighborsElements = list()
# Reassign new atom ids
# couter = 0
for resIndex in self.neighborsPoseIndex:
res = pose.residue(resIndex)
id, chain = pose.pdb_info().pose2pdb(resIndex).split()
resName = (int(id), chain)
# Construct neighbors vectors
for atomIndex in range(1, res.natoms() + 1):
# Skip virtual atoms
if res.atom_type(atomIndex).is_virtual():
continue
# print('BBB enviromet atom name', couter, pose.pdb_info().pose2pdb(resIndex), pose.residue(resIndex).atom_name(atomIndex))
# couter += 1
self.neighborsNumberOfAtoms += 1
# Get the atomIDs
self.neighborsAtomId.append(AtomID(atomIndex, resIndex))
# get charges
self.neighborsCharges.append(res.atomic_charge(atomIndex))
# get Vdw
# vdw = self.aminoAcids.VdwRadii[res.atom_type(atomIndex).element()]
self.neighborsLjRadii.append(res.atom_type(atomIndex).lj_radius())
self.neighborsLjEps.append(res.atom_type(atomIndex).lj_wdepth())
# get solvation parameters
self.neighborsLkdG.append(res.atom_type(atomIndex).lk_dgfree())
self.neighborsLkLam.append(res.atom_type(atomIndex).lk_lambda())
self.neighborsLkVol.append(res.atom_type(atomIndex).lk_volume())
# Get the side chain/backbone flag,
# first check if the residue is excluded from side chain coupling.