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Energy.pyx
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Energy.pyx
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from pyrosetta import Pose
from pyrosetta import get_fa_scorefxn
from pyrosetta.rosetta.core.scoring import ScoreType
cimport cython
cimport openmp
import numpy as np
import random
from libc.stdio cimport printf
from libc.math cimport cos, sin, acos, sqrt, M_PI, acos, atan2, fabs, pow, exp
from libc.float cimport DBL_MAX
from cython.parallel cimport prange
from cython.parallel cimport parallel
from pyrosetta.rosetta.utility import vector1_numeric_xyzVector_double_t as vec1
"""Contains following functions
self_energy_fast -> ligand_ligand_reduced
pair_energy_fast -> ligand_environment_reduced
-> bond
-> angle
self_matrices -> ligand_ligand_matrices
pair_matrices -> ligand_environment_matrices
pair_collision -> ligand_environment_collision
-> switch
-> col_epsi
selectTorsionsByDistance -> select_torsions_by_distance
-> angular_distance_squared
"""
@cython.boundscheck(False)
@cython.wraparound(False)
@cython.nonecheck(False)
@cython.cdivision(True)
@cython.profile(True)
def reduced_energy(double [:, :] ligand_xyz,
double[:, :] environment_xyz,
double [:] ligand_qij,
double [:] ligand_dljr,
double [:] ligand_dljep,
double [:, :] ligand_sol_prefactor,
double [:] ligand_lklam,
int [:, :] ligand_nonbonded,
double [:] ligand_nonbondedWeights,
double ligand_coupling,
double [:] ligand_weights,
double[:] ligand_mask,
double[:, :] ligand_environment_qij,
double[:, :] ligand_environment_dljr,
double[:, :] ligand_environment_dljep,
double[:, :, :] ligand_environment_sol_prefactor,
double [:] environmen_lklam,
int [:] environment_sideChainFalgs,
int [:] environment_mainChainFalgs,
double sideChainCoupling,
double mainChainCoupling,
double [:] ligand_environment_weights):
cdef double energy = 0.0
energy += ligand_ligand_reduced(ligand_xyz,
ligand_qij,
ligand_dljr,
ligand_dljep,
ligand_sol_prefactor,
ligand_lklam,
ligand_nonbonded,
ligand_nonbondedWeights,
ligand_coupling,
ligand_weights)
# Compute ligand_environment Energy
energy += ligand_environment_reduced(ligand_xyz,
environment_xyz,
ligand_mask,
ligand_environment_qij,
ligand_environment_dljr,
ligand_environment_dljep,
ligand_environment_sol_prefactor,
ligand_lklam,
environmen_lklam,
environment_sideChainFalgs,
environment_mainChainFalgs,
sideChainCoupling,
mainChainCoupling,
ligand_environment_weights)
return energy
@cython.boundscheck(False)
@cython.wraparound(False)
@cython.nonecheck(False)
@cython.cdivision(True)
@cython.profile(True)
cdef double ligand_ligand_reduced(double [:, :] ligand_xyz,
double [:] ligand_qij,
double [:] ligand_dljr,
double [:] ligand_dljep,
double [:, :] ligand_sol_prefactor,
double [:] ligand_lklam,
int [:, :] ligand_nonbonded,
double [:] ligand_nonbondedWeights,
double coupling,
double [:] weights) nogil:
"""
Computes the reduced ligand ligand potential energy from precomputed matrices
"""
cdef int N = ligand_nonbonded.shape[0]
cdef int i, j
cdef double dx, dy, dz, dij, dlj, dlj_dij, dlj_dij3, dlj_dij6, dlj_dij12, epsilon, s
cdef double sol_expfactor_i, sol_expfactor_j
cdef double rep, atr, col, sol, C
C = 0.0
for pair in range(N):
#i = ligand_nonbonded[pair, 0]
#j = ligand_nonbonded[pair, 1]
dx = ligand_xyz[ligand_nonbonded[pair, 0], 0] - ligand_xyz[ligand_nonbonded[pair, 1], 0]
dy = ligand_xyz[ligand_nonbonded[pair, 0], 1] - ligand_xyz[ligand_nonbonded[pair, 1], 1]
dz = ligand_xyz[ligand_nonbonded[pair, 0], 2] - ligand_xyz[ligand_nonbonded[pair, 1], 2]
dij = dx * dx + dy * dy + dz * dz
dij = sqrt(dij)
#dlj = ligand_dljr[pair]
#eij = ligand_dljep[pair]
# Compute rep
if dij <= 0.6 * ligand_dljr[pair]:
dlj_dij = 1/0.6
dlj_dij3 = dlj_dij * dlj_dij * dlj_dij
dlj_dij6 = dlj_dij3 * dlj_dij3
dlj_dij12 = dlj_dij6 * dlj_dij6
rep = (ligand_dljep[pair] * (dlj_dij12 - (2 * dlj_dij6))) #- (dlj_dij*dij)
elif 0.6 * ligand_dljr[pair] < dij and dij <= ligand_dljr[pair]:
dlj_dij = ligand_dljr[pair]/dij
dlj_dij3 = dlj_dij * dlj_dij * dlj_dij
dlj_dij6 = dlj_dij3 * dlj_dij3
dlj_dij12 = dlj_dij6 * dlj_dij6
rep = ligand_dljep[pair] * (dlj_dij12 - (2 * dlj_dij6))
elif ligand_dljr[pair] < dij:
rep = 0
# Compute atr
if dij <= ligand_dljr[pair]:
atr = -ligand_dljep[pair]
elif ligand_dljr[pair] < dij and dij <= 4.5:
dlj_dij = ligand_dljr[pair]/dij
dlj_dij3 = dlj_dij * dlj_dij * dlj_dij
dlj_dij6 = dlj_dij3 * dlj_dij3
dlj_dij12 = dlj_dij6 * dlj_dij6
atr = ligand_dljep[pair] * (dlj_dij12 - (2 * dlj_dij6))
elif 4.5 < dij and dij <= 6.0:
dlj_dij = ligand_dljr[pair]/dij
dlj_dij3 = dlj_dij * dlj_dij * dlj_dij
dlj_dij6 = dlj_dij3 * dlj_dij3
dlj_dij12 = dlj_dij6 * dlj_dij6
atr = ligand_dljep[pair] * (dlj_dij12 - (2 * dlj_dij6)) * switch(dij, 4.5, 6.0)
elif 6.0 < dij:
atr = 0
# compute electrostatic
#qij = ligand_qij[pair]
#epsilon = col_epsi(dij)
epsilon = 6
if dij < 1.45:
col = (ligand_qij[pair]/ (1.45 * epsilon))
elif 1.45 <= dij and dij < 1.85:
s = switch(dij, 1.45, 1.85)
col = ((ligand_qij[pair]/ (1.45 * epsilon)) * s) + ((ligand_qij[pair]/epsilon) * ((1 / (dij*dij)) - (1 / (5.5*5.5))) * (1 - s))
elif 1.85 <= dij and dij < 4.5:
col = (ligand_qij[pair]/epsilon) * ((1 / (dij*dij)) - (1 / (5.5*5.5)))
elif 4.5 <= dij and dij < 5.5:
col = (ligand_qij[pair]/epsilon) * ((1 / (dij*dij)) - (1 / (5.5*5.5))) * switch(dij, 4.5, 5.5)
else:
col = 0
# compute solvation
sol = 0
if weights[3] != 0:
if dij <= ligand_dljr[pair] - 0.3:
sol += ligand_sol_prefactor[pair, 0]
sol += ligand_sol_prefactor[pair, 1]
elif ligand_dljr[pair] - 0.3 < dij and dij <= ligand_dljr[pair] + 0.2:
s = switch(dij, ligand_dljr[pair] - 0.3, ligand_dljr[pair] + 0.2)
sol_expfactor_i = (dij - ligand_dljr[pair]) / ligand_lklam[ligand_nonbonded[pair, 0]]
sol_expfactor_j = (dij - ligand_dljr[pair]) / ligand_lklam[ligand_nonbonded[pair, 1]]
sol += (ligand_sol_prefactor[pair, 0] * s) + ((ligand_sol_prefactor[pair, 0] * exp(-1 * (sol_expfactor_i * sol_expfactor_i))) * (1-s))
sol += (ligand_sol_prefactor[pair, 1] * s) + ((ligand_sol_prefactor[pair, 1] * exp(-1 * (sol_expfactor_j * sol_expfactor_j))) * (1-s))
elif ligand_dljr[pair] + 0.2 < dij and dij <= 4.5:
sol_expfactor_i = (dij - ligand_dljr[pair]) / ligand_lklam[ligand_nonbonded[pair, 0]]
sol_expfactor_j = (dij - ligand_dljr[pair]) / ligand_lklam[ligand_nonbonded[pair, 1]]
sol += ligand_sol_prefactor[pair, 0] * exp(-1 * (sol_expfactor_i * sol_expfactor_i))
sol += ligand_sol_prefactor[pair, 1] * exp(-1 * (sol_expfactor_j * sol_expfactor_j))
elif 4.5 < dij and dij <= 6.0:
s = switch(dij, 4.5, 6.0)
sol_expfactor_i = (dij - ligand_dljr[pair]) / ligand_lklam[ligand_nonbonded[pair, 0]]
sol_expfactor_j = (dij - ligand_dljr[pair]) / ligand_lklam[ligand_nonbonded[pair, 1]]
sol += ligand_sol_prefactor[pair, 0] * exp(-1 * (sol_expfactor_i * sol_expfactor_i)) * s
sol += ligand_sol_prefactor[pair, 1] * exp(-1 * (sol_expfactor_j * sol_expfactor_j)) * s
else:
sol = 0
#print(rep, atr, col, sol)
#print((weights[0] * rep, weights[1] * atr, weights[2] * col, weights[3] * sol))
#print(coupling)
C += coupling * ligand_nonbondedWeights[pair] * (weights[0] * rep + weights[1] * atr + weights[2] * col + weights[3] * sol)
return C
@cython.boundscheck(False)
@cython.wraparound(False)
@cython.nonecheck(False)
@cython.cdivision(True)
@cython.profile(True)
cdef double ligand_environment_reduced(double[:, :] ligand_xyz,
double[:, :] environment_xyz,
double[:] ligand_mask,
double[:, :] ligand_environment_qij,
double[:, :] ligand_environment_dljr,
double[:, :] ligand_environment_dljep,
double[:, :, :] ligand_environment_sol_prefactor,
double [:] ligand_lklam,
double [:] environmen_lklam,
int [:] environment_sideChainFalgs,
int [:] environment_mainChainFalgs,
double sideChainCoupling,
double mainChainCoupling,
double [:] weights) nogil:
cdef int N = ligand_xyz.shape[0]
cdef int M = environment_xyz.shape[0]
cdef int i, j
cdef double dx, dy, dz, dij, dlj, dlj_dij, dlj_dij3, dlj_dij6, dlj_dij12, epsilon, s
cdef double sol_expfactor_i, sol_expfactor_j, coupling
cdef double rep, atr, col, sol, C = 0.0
for i in range(N):
if ligand_mask[i] == 0:
continue
for j in range(M):
coupling = (environment_sideChainFalgs[j] * sideChainCoupling) + (environment_mainChainFalgs[j] * mainChainCoupling)
if coupling == 0:
continue
#print('couplinfs', coupling, overlap)
dx = ligand_xyz[i, 0] - environment_xyz[j, 0]
dy = ligand_xyz[i, 1] - environment_xyz[j, 1]
dz = ligand_xyz[i, 2] - environment_xyz[j, 2]
dij = dx * dx + dy * dy + dz * dz
dij = sqrt(dij)
#dlj = ligand_environment_dljr[i, j]
#eij = ligand_environment_dljep[i, j]
# Compute rep
if dij <= 0.6 * ligand_environment_dljr[i, j]:
dlj_dij = 1/0.6
dlj_dij3 = dlj_dij * dlj_dij * dlj_dij
dlj_dij6 = dlj_dij3 * dlj_dij3
dlj_dij12 = dlj_dij6 * dlj_dij6
rep = (ligand_environment_dljep[i, j] * (dlj_dij12 - (2 * dlj_dij6))) - (dlj_dij*dij)
elif 0.6 * ligand_environment_dljr[i, j] < dij and dij <= ligand_environment_dljr[i, j]:
dlj_dij = ligand_environment_dljr[i, j]/dij
dlj_dij3 = dlj_dij * dlj_dij * dlj_dij
dlj_dij6 = dlj_dij3 * dlj_dij3
dlj_dij12 = dlj_dij6 * dlj_dij6
rep = ligand_environment_dljep[i, j] * (dlj_dij12 - (2 * dlj_dij6))
elif ligand_environment_dljr[i, j] < dij:
rep = 0
# Compute atr
if dij <= ligand_environment_dljr[i, j]:
atr = -ligand_environment_dljep[i, j]
elif ligand_environment_dljr[i, j] < dij and dij <= 4.5:
dlj_dij = ligand_environment_dljr[i, j]/dij
dlj_dij3 = dlj_dij * dlj_dij * dlj_dij
dlj_dij6 = dlj_dij3 * dlj_dij3
dlj_dij12 = dlj_dij6 * dlj_dij6
atr = ligand_environment_dljep[i, j] * (dlj_dij12 - (2 * dlj_dij6))
elif 4.5 < dij and dij <= 6.0:
dlj_dij = ligand_environment_dljr[i, j]/dij
dlj_dij3 = dlj_dij * dlj_dij * dlj_dij
dlj_dij6 = dlj_dij3 * dlj_dij3
dlj_dij12 = dlj_dij6 * dlj_dij6
atr = ligand_environment_dljep[i, j] * (dlj_dij12 - (2 * dlj_dij6)) * switch(dij, 4.5, 6.0)
elif 6.0 < dij:
atr = 0
#if atr > 0:
# print(i, j, atr, dij, dlj, eij)
# compute electrostatic
#epsilon = col_epsi(dij)
epsilon = 6
if dij < 1.45:
col = (ligand_environment_qij[i, j]/ (1.45 * epsilon))
elif 1.45 <= dij and dij < 1.85:
s = switch(dij, 1.45, 1.85)
col = ((ligand_environment_qij[i, j]/ (1.45 * epsilon)) * s) + ((ligand_environment_qij[i, j]/epsilon) * ((1 / (dij*dij)) - (1 / (5.5*5.5))) * (1 - s))
elif 1.85 <= dij and dij < 4.5:
col = (ligand_environment_qij[i, j]/epsilon) * ((1 / (dij*dij)) - (1 / (5.5*5.5)))
elif 4.5 <= dij and dij < 5.5:
col = (ligand_environment_qij[i, j]/epsilon) * ((1 / (dij*dij)) - (1 / (5.5*5.5))) * switch(dij, 4.5, 5.5)
else:
col = 0
# compute solvation
sol = 0
if weights[3] != 0:
#print('sol')
if dij <= ligand_environment_dljr[i, j] - 0.3:
sol += ligand_environment_sol_prefactor[i, j, 0]
sol += ligand_environment_sol_prefactor[i, j, 1]
elif ligand_environment_dljr[i, j] - 0.3 < dij and dij <= ligand_environment_dljr[i, j] + 0.2:
s = switch(dij, ligand_environment_dljr[i, j] - 0.3, ligand_environment_dljr[i, j] + 0.2)
sol_expfactor_i = (dij - ligand_environment_dljr[i, j]) / ligand_lklam[i]
sol_expfactor_j = (dij - ligand_environment_dljr[i, j]) / environmen_lklam[j]
sol += (ligand_environment_sol_prefactor[i, j, 0] * s) + ((ligand_environment_sol_prefactor[i, j, 0] * exp(-1 * (sol_expfactor_i * sol_expfactor_i))) * (1-s))
sol += (ligand_environment_sol_prefactor[i, j, 1] * s) + ((ligand_environment_sol_prefactor[i, j, 1] * exp(-1 * (sol_expfactor_j * sol_expfactor_j))) * (1-s))
elif ligand_environment_dljr[i, j] + 0.2 < dij and dij <= 4.5:
sol_expfactor_i = (dij - ligand_environment_dljr[i, j]) / ligand_lklam[i]
sol_expfactor_j = (dij - ligand_environment_dljr[i, j]) / environmen_lklam[j]
sol += ligand_environment_sol_prefactor[i, j, 0] * exp(-1 * (sol_expfactor_i * sol_expfactor_i))
sol += ligand_environment_sol_prefactor[i, j, 1] * exp(-1 * (sol_expfactor_j * sol_expfactor_j))
elif 4.5 < dij and dij <= 6.0:
s = switch(dij, 4.5, 6.0)
sol_expfactor_i = (dij - ligand_environment_dljr[i, j]) / ligand_lklam[i]
sol_expfactor_j = (dij - ligand_environment_dljr[i, j]) / environmen_lklam[j]
sol += ligand_environment_sol_prefactor[i, j, 0] * exp(-1 * (sol_expfactor_i * sol_expfactor_i)) * s
sol += ligand_environment_sol_prefactor[i, j, 1] * exp(-1 * (sol_expfactor_j * sol_expfactor_j)) * s
C += coupling * ( weights[0] * rep + weights[1] * atr + weights[2] * col + weights[3] * sol)
return C
@cython.boundscheck(False)
@cython.wraparound(False)
@cython.nonecheck(False)
@cython.cdivision(True)
@cython.profile(True)
def bond(double [:, :] ligand_xyz,
int [:, :] bond_atomIndex,
double [:] equilibrium_distance,
double [:] force_constance):
"""
Computes bond potential
"""
cdef int i, j, ibond, N = bond_atomIndex.shape[0]
cdef double dx, dy, dz, dji, C
C = 0.0
for ibond in range(N):
i = bond_atomIndex[ibond, 0]
j = bond_atomIndex[ibond, 1]
dx = ligand_xyz[i, 0] - ligand_xyz[j, 0]
dy = ligand_xyz[i, 1] - ligand_xyz[j, 1]
dz = ligand_xyz[i, 2] - ligand_xyz[j, 2]
dji = dx * dx + dy * dy + dz * dz
dji = sqrt(dji)
d = dji - equilibrium_distance[ibond]
C += force_constance[ibond] * d * d
return C
@cython.boundscheck(False)
@cython.wraparound(False)
@cython.nonecheck(False)
@cython.cdivision(True)
@cython.profile(True)
def angle(double [:, :] ligand_xyz,
int [:, :] angle_atomIndex,
double [:] equilibrium_angle,
double [:] force_constance):
"""
Computes bond potential
"""
cdef int i, j, iangle, N = angle_atomIndex.shape[0]
cdef double dx, dy, dz, dji, djk, djidjk, teta, dteta, C
C = 0.0
for iangle in range(N):
i = angle_atomIndex[iangle, 0]
j = angle_atomIndex[iangle, 1]
k = angle_atomIndex[iangle, 2]
dxji = ligand_xyz[i, 0] - ligand_xyz[j, 0]
dyji = ligand_xyz[i, 1] - ligand_xyz[j, 1]
dzji = ligand_xyz[i, 2] - ligand_xyz[j, 2]
dxjk = ligand_xyz[k, 0] - ligand_xyz[j, 0]
dyjk = ligand_xyz[k, 1] - ligand_xyz[j, 1]
dzjk = ligand_xyz[k, 2] - ligand_xyz[j, 2]
dji = dxji * dxji + dyji * dyji + dzji * dzji
dji = sqrt(dji)
djk = dxjk * dxjk + dyjk * dyjk + dzjk * dzjk
djk = sqrt(djk)
djidjk = dxji * dxjk + dyji * dyjk + dzji * dzjk
djidjk = djidjk / (dji * djk)
if djidjk > 1.0: djidjk = 1
if djidjk < -1.0: djidjk = -1
teta = acos(djidjk)
dteta = teta - equilibrium_angle[iangle]
C += force_constance[iangle] * dteta * dteta
return C
@cython.boundscheck(False)
@cython.wraparound(False)
@cython.nonecheck(False)
@cython.cdivision(True)
@cython.profile(True)
def ligand_ligand_matrices(double [:] ligand_q,
double [:] ligand_qij,
double [:] ligand_ljr,
double [:] ligand_dljr,
double [:] ligand_ljep,
double [:] ligand_dljep,
double [:] ligand_lkdg,
double [:] ligand_lklam,
double [:] ligand_lkvol,
double [:, :] ligand_sol_prefactor,
int [:, :] ligand_nonbonded):
cdef int N = ligand_nonbonded.shape[0]
cdef int i, j
#cdef double dx, dy, dz, dij, dij_v, dij_col, dij_sol, dlj, epij
#cdef double dlj_dij, dlj_dij_3, dlj_dij_6, dlj_dij_12
#cdef double sol_prefactor_i, sol_prefactor_j, sol_expfactor_i, sol_expfactor_j
#cdef double vdw, coupling, col, sol, C = 0.0
cdef double pi_3_2 = pow(M_PI, 3/2)
for pair in range(N):
i = ligand_nonbonded[pair, 0]
j = ligand_nonbonded[pair, 1]
ligand_qij[pair] = 332 * ligand_q[i] * ligand_q[j]
ligand_dljr[pair] = ligand_ljr[i] + ligand_ljr[j]
ligand_dljep[pair] = sqrt((ligand_ljep[i] * ligand_ljep[j]))
ligand_sol_prefactor[pair, 0] = (-ligand_lkvol[j] * ligand_lkdg[i]) / (ligand_lklam[i] * ligand_ljr[i] * ligand_ljr[i] * 2 * pi_3_2)
ligand_sol_prefactor[pair, 1] = (-ligand_lkvol[i] * ligand_lkdg[j]) / (ligand_lklam[j] * ligand_ljr[j] * ligand_ljr[j] * 2 * pi_3_2)
@cython.boundscheck(False)
@cython.wraparound(False)
@cython.nonecheck(False)
@cython.cdivision(True)
@cython.profile(True)
def ligand_environment_matrices(double [:] ligand_q,
double [:] environment_q,
double [:, :] ligand_environment_qij,
double [:] ligand_ljr,
double [:] environment_ljr,
double [:, :] ligand_environment_dljr,
double [:] ligand_ljep,
double [:] environment_ljep,
double [:, :] ligand_environment_ljep,
double [:] ligand_lkdg,
double [:] environment_lkdg,
double [:] ligand_lklam,
double [:] environment_lklam,
double [:] ligand_lkvol,
double [:] environment_lkvol,
double [:, :, :] ligand_environment_sol_prefactor):
cdef int N = ligand_q.shape[0]
cdef int M = environment_q.shape[0]
cdef int i, j
# cdef double dx, dy, dz, dij, dij_v, dij_col, dij_sol, dlj, epij
# cdef double dlj_dij, dlj_dij_3, dlj_dij_6, dlj_dij_12
#cdef double sol_prefactor_i, sol_prefactor_j, sol_expfactor_i, sol_expfactor_j
#cdef double vdw, coupling, col, sol, C = 0.0
cdef double pi_3_2 = pow(M_PI, 3 / 2)
for i in range(N):
for j in range(M):
ligand_environment_dljr[i, j] = ligand_ljr[i] + environment_ljr[j]
ligand_environment_ljep[i, j] = sqrt((ligand_ljep[i] * environment_ljep[j]))
ligand_environment_qij[i, j] = 332 * ligand_q[i] * environment_q[j]
ligand_environment_sol_prefactor[i, j, 0] = (-environment_lkvol[j] * ligand_lkdg[i]) / (ligand_lklam[i] * ligand_ljr[i] * ligand_ljr[i] * 2 * pi_3_2)
ligand_environment_sol_prefactor[i, j, 1] = (-ligand_lkvol[i] * environment_lkdg[j]) / (environment_lklam[j] * environment_ljr[j] * environment_ljr[j] * 2 * pi_3_2)
#print(i, j, ligand_environment_sol_prefactor[i, j, 0], ligand_lkdg[i])
#print(i, j, ligand_environment_sol_prefactor[i, j, 1], Blkdg[j])
@cython.boundscheck(False)
@cython.wraparound(False)
@cython.nonecheck(False)
@cython.cdivision(True)
@cython.profile(True)
def ligand_environment_collision(double[:, :] ligand_xyz,
double[:, :] environment_xyz,
double[:] ligand_lj,
double[:] environment_lj,
int [:] environment_sideChainFalgs,
int [:] environment_mainChainFalgs,
double sideChainCoupling,
double mainChainCoupling,
double overlap):
cdef int N = ligand_xyz.shape[0]
cdef int M = environment_xyz.shape[0]
cdef int i, j, C = 0
cdef double dx, dy, dz, dij, dlj
cdef double coupling
#cdef double rep, atr, col, sol
for i in range(N):
for j in range(M):
coupling = (environment_sideChainFalgs[j] * sideChainCoupling) + (environment_mainChainFalgs[j] * mainChainCoupling)
if coupling == 0:
continue
#print('couplinfs', coupling, overlap)
dx = ligand_xyz[i, 0] - environment_xyz[j, 0]
dy = ligand_xyz[i, 1] - environment_xyz[j, 1]
dz = ligand_xyz[i, 2] - environment_xyz[j, 2]
dij = dx * dx + dy * dy + dz * dz
dij = sqrt(dij)
dlj = ligand_lj[i] + environment_lj[j]
# Terminate if collision found
if dij < dlj * overlap:
C = 1
#print('BBBB colli: i, j', i, j, dij, dlj, coupling, environment_sideChainFalgs[j], environment_mainChainFalgs[j] )
#print(ligand_xyz[i, 0], ligand_xyz[i, 1], ligand_xyz[i, 2])
#print(environment_xyz[j, 0], environment_xyz[j, 1], environment_xyz[j, 2])
return C
return C
cdef inline double switch(double x, double min, double max) nogil:
cdef double r2_a2, b2_a2, r2a2_b2a2
if x < min:
return 1
elif x > max:
return 0
else:
r2_a2 = (x * x) - (min * min)
b2_a2 = (max * max) - (min * min)
r2a2_b2a2 = r2_a2/b2_a2
return (1 + r2a2_b2a2**2 * ((2 * r2a2_b2a2) -3))
cdef inline double col_epsi(double x):
cdef double d_4, g
d_4 = x/0.4
g = (1 + d_4 + (d_4**2)/2) * exp(-d_4)
return g*6.0 + (1-g)*80
@cython.boundscheck(False)
@cython.wraparound(False)
@cython.nonecheck(False)
@cython.cdivision(True)
@cython.profile(True)
def select_torsions_by_distance(double[:, :] T, int K, double cutoff_percent):
cdef int N = T.shape[0]
cdef int M = T.shape[1]
cdef int i, j, counter, k
cdef float [:, :] D
cdef double [:, :] C
cdef float distance, distanceMax, cutoff_value
cdef int [:] C_index
# Compute distance matrix
D = np.zeros((N, N), dtype=np.float32)
distanceMax = 0.0
for i in range(N):
for j in range(i+1, N):
distance = angular_distance_squared(T[i, :-1], T[j, :-1])
D[i, j] = distance
D[j, i] = distance
if distanceMax < distance:
distanceMax = distance
# compute the cut off value for the torsion vector
cutoff_value = distanceMax * cutoff_percent
# initiate the C
C = np.zeros((K, M), dtype=np.float64)
C[0, :] = T[0, :]
counter = 1
C_index = np.zeros(K, dtype=np.int32)
# Assign
for i in range(N):
if counter >= K:
break
for j in range(counter):
distance = D[i, C_index[j]]
#print(distance, cutoff_value)
if distance >= cutoff_value:
C[counter, :] = T[i, :]
C_index[counter] = i
#print(i, j, distance, cutoff_value, counter, ['{:.1f}'.format(k) for k in np.array(T[i, :])])
counter += 1
break
return np.array(C[0:counter, :])
cdef inline double angular_distance_squared(double [:] A, double [:] B):
cdef int N = A.shape[0]
cdef int i
cdef double dx, D = 0.0
for i in range(N):
dx = A[i] - B[i]
dx = fabs(dx)
if dx <= 180:
D += dx * dx
else:
D += (360 - dx) * (360 - dx)
return D
""" Contains following functions
-> ligand_steep_decent
-> ligand_nonbonded_gradient
- ligand_enviroment_gradient
-> bond
-> angle
-> switch
"""
""" Vector definition conventions
The displacement vector between two particles i, and j is defined as:
uij = rj - ri
where the ri and rj are position vectors. Thus, force vectors for pair
potentials are defined as
fi = -g * uji
fj = -g * uij
where g is the gradient and uij = -uij.
"""
@cython.boundscheck(False)
@cython.wraparound(False)
@cython.nonecheck(False)
@cython.cdivision(True)
@cython.profile(True)
def ligand_steep_decent(double [:, :] ligand_xyz,
double[:, :] environment_xyz,
double [:] ligand_qij,
double [:] ligand_dljr,
double [:] ligand_dljep,
double [:, :] ligand_sol_prefactor,
double [:] ligand_lklam,
int [:, :] ligand_nonbonded,
double [:] ligand_nonbondedWeights,
double ligand_coupling,
double [:] ligand_weights,
int[:, :] bond_atomIndex,
double[:] equilibrium_distance,
double[:] bond_force_constance,
int [:, :] angle_atomIndex,
double [:] equilibrium_angle,
double [:] angle_force_constance,
#double[:, :] ligand_xyz,
double[:] ligand_mask,
double[:, :] ligand_environment_qij,
double[:, :] ligand_environment_dljr,
double[:, :] ligand_environment_dljep,
double[:, :, :] ligand_environment_sol_prefactor,
#double [:] ligand_lklam,
double [:] environmen_lklam,
int [:] environment_sideChainFalgs,
int [:] environment_mainChainFalgs,
double sideChainCoupling,
double mainChainCoupling,
double [:] ligand_environment_weights,
double convergence_cutoff,
int max_cycle,
double max_line_search):
cdef int N = ligand_xyz.shape[0]
cdef int M = ligand_xyz.shape[1]
cdef double [:, :] gradient_vector, xyz_tm
cdef double E_new, E_old, g_Max, g_norm2, step
cdef int i, j, k
gradient_vector = np.zeros((N, 3), dtype=np.float64)
xyz_tm = np.zeros((N, M), dtype=np.float64)
#print('BBBB initiate')
# Compute initial energy E_old
E_old = 0.0
E_old += ligand_ligand_reduced(ligand_xyz,
ligand_qij,
ligand_dljr,
ligand_dljep,
ligand_sol_prefactor,
ligand_lklam,
ligand_nonbonded,
ligand_nonbondedWeights,
ligand_coupling,
ligand_weights)
# Compute ligand_environment Energy
E_old += ligand_environment_reduced(ligand_xyz,
environment_xyz,
ligand_mask,
ligand_environment_qij,
ligand_environment_dljr,
ligand_environment_dljep,
ligand_environment_sol_prefactor,
ligand_lklam,
environmen_lklam,
environment_sideChainFalgs,
environment_mainChainFalgs,
sideChainCoupling,
mainChainCoupling,
ligand_environment_weights)
# Compute bond Energy
E_old += bond(ligand_xyz,
bond_atomIndex,
equilibrium_distance,
bond_force_constance)
# Compute Angles Energy
E_old += angle(ligand_xyz,
angle_atomIndex,
equilibrium_angle,
angle_force_constance)
#print('BBB initial energy', E_old)
# Minimize
for i in range(max_cycle):
#print('BBBB cycle', i)
# rest stuff
g_Max = -DBL_MAX
g_norm2 = 0.0
E_new = 0.0
# Reset the gradient_vector
for j in range(N):
gradient_vector[j, 0] = 0.0
gradient_vector[j, 1] = 0.0
gradient_vector[j, 2] = 0.0
# Compute ligand ligand non-bonded gradient
ligand_ligand_gradient(ligand_xyz,
ligand_qij,
ligand_dljr,
ligand_dljep,
ligand_sol_prefactor,
ligand_lklam,
ligand_nonbonded,
ligand_nonbondedWeights,
ligand_coupling,
ligand_weights,
gradient_vector)
#for j in range(N):
# print('BBB grad ligand_ligand_gradient: ', gradient_vector[j, 0], gradient_vector[j, 1], gradient_vector[j, 2])
# Compute ligand environment non-bonded gradient
ligand_environment_gradient(ligand_xyz,
environment_xyz,
ligand_mask,
ligand_environment_qij,
ligand_environment_dljr,
ligand_environment_dljep,
ligand_environment_sol_prefactor,
ligand_lklam,
environmen_lklam,
environment_sideChainFalgs,
environment_mainChainFalgs,
sideChainCoupling,
mainChainCoupling,
ligand_environment_weights,
gradient_vector)
#for j in range(N):
# print('BBB grad ligand_environment_gradient: ', gradient_vector[j, 0], gradient_vector[j, 1], gradient_vector[j, 2])
# Compute ligand bond gradient
bond_gradient(ligand_xyz,
bond_atomIndex,
equilibrium_distance,
bond_force_constance,
gradient_vector)
#for j in range(N):
# print('BBB grad bond_gradient: ', gradient_vector[j, 0], gradient_vector[j, 1], gradient_vector[j, 2])
# Compute ligand angle gradient
angle_gradient(ligand_xyz,
angle_atomIndex,
equilibrium_angle,
angle_force_constance,
gradient_vector)
#for j in range(N):
# print('BBB grad angle_gradient: ', gradient_vector[j, 0], gradient_vector[j, 1], gradient_vector[j, 2])
# Go over the grad vector
for j in range(N):
# Compute the dot product of grad vector. Treat it as a flat matrix
g_norm2 += gradient_vector[j, 0] * gradient_vector[j, 0]
g_norm2 += gradient_vector[j, 1] * gradient_vector[j, 1]
g_norm2 += gradient_vector[j, 2] * gradient_vector[j, 2]
#print('BBB grad vector: ', gradient_vector[j, 0], gradient_vector[j, 1], gradient_vector[j, 2])
# get g_Max
if gradient_vector[j, 0] > g_Max:
g_Max = gradient_vector[j, 0]
if gradient_vector[j, 1] > g_Max:
g_Max = gradient_vector[j, 1]
if gradient_vector[j, 2] > g_Max:
g_Max = gradient_vector[j, 2]
# Choose a Step size such that g_Max*Step = 0.5
if g_Max == 0.0:
step = 0.0
else:
step = max_line_search/g_Max
#step = 0.0
#print('BBB g_max', g_Max, 'max_line_search', max_line_search, 'step', step)
for j in range(10000):
#print('initiate line search')
#return 0
# Get the new coordinate
for k in range(N):
xyz_tm[k, 0] = ligand_xyz[k, 0] + (-1 * gradient_vector[k, 0] * step)
xyz_tm[k, 1] = ligand_xyz[k, 1] + (-1 * gradient_vector[k, 1] * step)
xyz_tm[k, 2] = ligand_xyz[k, 2] + (-1 * gradient_vector[k, 2] * step)
# compute E_new
# Compute ligand_ligand Energy
E_new = 0
E_new += ligand_ligand_reduced(xyz_tm,
ligand_qij,
ligand_dljr,
ligand_dljep,
ligand_sol_prefactor,
ligand_lklam,
ligand_nonbonded,
ligand_nonbondedWeights,
ligand_coupling,
ligand_weights)
# Compute ligand_environment Energy
E_new += ligand_environment_reduced(xyz_tm,
environment_xyz,
ligand_mask,
ligand_environment_qij,
ligand_environment_dljr,
ligand_environment_dljep,
ligand_environment_sol_prefactor,
ligand_lklam,
environmen_lklam,
environment_sideChainFalgs,
environment_mainChainFalgs,
sideChainCoupling,
mainChainCoupling,
ligand_environment_weights)
# Compute bond Energy
E_new += bond(xyz_tm,
bond_atomIndex,
equilibrium_distance,
bond_force_constance)
# Compute Angles Energy
E_new += angle(xyz_tm,
angle_atomIndex,
equilibrium_angle,
angle_force_constance)
#print(' line search step ', j, 'BBB E_new', E_new, 'step', step, 'condition', E_old)
if E_new > E_old:
step *= 0.5
continue
else: # accept the move
break
#update xyz
for k in range(N):
ligand_xyz[k, 0] = xyz_tm[k, 0]
ligand_xyz[k, 1] = xyz_tm[k, 1]
ligand_xyz[k, 2] = xyz_tm[k, 2]
#print('Final energy', E_old)
# Quite if energy converges
if fabs(E_new - E_old) < convergence_cutoff:
return 0
else:
E_old = E_new
@cython.boundscheck(False)
@cython.wraparound(False)
@cython.nonecheck(False)
@cython.cdivision(True)
@cython.profile(True)
cdef inline void ligand_ligand_gradient(double [:, :] ligand_xyz,
double [:] ligand_qij,
double [:] ligand_dljr,
double [:] ligand_dljep,
double [:, :] ligand_sol_prefactor,
double [:] ligand_lklam,
int [:, :] ligand_nonbonded,
double [:] ligand_nonbondedWeights,
double ligand_coupling,
double [:] ligand_weights,
double [:, :] gradient_vector):
cdef int N = ligand_nonbonded.shape[0]
cdef int i, j
cdef double dxij, dyij, dzij, dij, dlj, dlj_dij, dlj_dij3, dlj_dij6, dlj_dij12, epsilon, s, s_p
cdef double sol_expfactor_i, sol_expfactor_j
cdef double rep, atr, col, sol, grad_magnititude
for pair in range(N):
# Assuming fist element in pair is i and the other is j
i = ligand_nonbonded[pair, 0]
j = ligand_nonbonded[pair, 1]
dxij = ligand_xyz[j, 0] - ligand_xyz[i, 0]
dyij = ligand_xyz[j, 1] - ligand_xyz[i, 1]
dzij = ligand_xyz[j, 2] - ligand_xyz[i, 2]
dij = dxij * dxij + dyij * dyij + dzij * dzij
dij = sqrt(dij)
#dlj = ligand_dljr[pair]
#eij = ligand_dljep[pair]
# Compute rep gradient magnitude
if dij <= 0.6 * ligand_dljr[pair]:
# constant, replacing dij = 0.6*dljr
dlj_dij = 1/0.6
dlj_dij3 = dlj_dij * dlj_dij * dlj_dij
dlj_dij6 = dlj_dij3 * dlj_dij3
dlj_dij12 = dlj_dij6 * dlj_dij6
rep = (-12 * ligand_dljep[pair]/(0.6 * ligand_dljr[pair])) * (dlj_dij12 - dlj_dij6) #- (dlj_dij*dij)
elif 0.6 * ligand_dljr[pair] < dij and dij <= ligand_dljr[pair]:
dlj_dij = ligand_dljr[pair]/dij
dlj_dij3 = dlj_dij * dlj_dij * dlj_dij
dlj_dij6 = dlj_dij3 * dlj_dij3
dlj_dij12 = dlj_dij6 * dlj_dij6
rep = (-12 * ligand_dljep[pair]/dij) * (dlj_dij12 - dlj_dij6)
elif ligand_dljr[pair] < dij:
rep = 0
# Compute atr
if dij <= ligand_dljr[pair]:
#atr = ligand_dljep[pair]
atr = 0
elif ligand_dljr[pair] < dij and dij <= 4.5:
dlj_dij = ligand_dljr[pair]/dij
dlj_dij3 = dlj_dij * dlj_dij * dlj_dij
dlj_dij6 = dlj_dij3 * dlj_dij3
dlj_dij12 = dlj_dij6 * dlj_dij6
atr = (-12 * ligand_dljep[pair]/dij) * (dlj_dij12 - dlj_dij6)
elif 4.5 < dij and dij <= 6.0:
dlj_dij = ligand_dljr[pair]/dij
dlj_dij3 = dlj_dij * dlj_dij * dlj_dij