def perform_dock(work_name, center_x, center_y, center_z, size_x, size_y,
size_z, pdb_path, lig_path, res_path, email):
"""
用户指定对接中心以及盒子大小
:param work_name:
:param center_x:
:param center_y:
:param center_z:
:param size_x:
:param size_y:
:param size_z:
:param pdb_path:
:param lig_path:
:param res_path:
:return:
"""
dock_status(work_name=work_name, status='computing')
res = os.path.join(res_path, 'res')
if not os.path.exists(res):
os.mkdir(res)
os.system("python %s/extra_apps/vina/prepare_receptor4.py -r %s"
" -A checkhydrogens " % (BASE_DIR, pdb_path))
pdbqt = pdb_path.split('/')[-1].split('.')[0] + '.pdbqt'
if os.path.exists(pdbqt):
os.system("mv %s %s" % (pdbqt, res_path))
os.system("python %s/extra_apps/vina/prepare_ligand4.py -l %s -v" %
(BASE_DIR, lig_path))
ligqt = lig_path.split('/')[-1].split('.')[0] + '.pdbqt'
if os.path.exists(ligqt):
os.system("mv %s %s" % (ligqt, res_path))
os.system(
"%s --receptor %s/%s --ligand %s/%s --center_x %s --center_y %s --center_z %s --size_x %s --size_y %s"
" --size_z %s" % ('vina', res_path, pdbqt, res_path, ligqt, center_x,
center_y, center_z, size_x, size_y, size_z))
outqt = ligqt.split('.')[0] + '_out.pdbqt'
outqt_path = os.path.join(res_path, outqt)
if os.path.exists(outqt_path):
reg = 'REMARK VINA RESULT:(.*?)\n'
re_reg = re.compile(reg)
with open(outqt_path, 'r') as f:
data = f.read()
out_lst = re_reg.findall(data)
if out_lst:
med = []
for model in out_lst:
med.append(float(model.split()[0]))
affinity = str(min(med))
dock_affinity(work_name=work_name, affinity=affinity)
os.system("mv %s %s" % (outqt_path, res))
os.system("python %s/extra_apps/vina/pdbqt_to_pdb.py -f %s -v" %
(BASE_DIR, os.path.join(res, outqt)))
out_path = os.path.join(res.split('media/')[1], outqt[:-2])
dock_out(work_name=work_name, out_path=out_path)
dock_status(work_name=work_name, status='completed')
email_status(email)
def perform_gbsa(work_name, pdb_path, lig_path, complex_path, res_path, email):
"""
:param work_name:
:param pdb_path:
:param lig_path:
:param complex_path:
:param res_path:
:param email:
:return:
"""
gbsa_status(work_name=work_name, status='computing')
res = os.path.join(res_path, 'res')
if not os.path.exists(res):
os.mkdir(res)
# os.system()
gbsa_status(work_name=work_name, status='completed')
email_status(email)
def perform_sea(work_name, smiles, email):
"""
:param work_name:
:param smiles:
:return:
"""
screen_status(work_name=work_name, status='computing')
results = pred(smiles, target_list=TargetList)
insert_lst = []
if results:
targets = []
for n in results:
targets.append(n['chembl_id'])
tar_lst = ';'.join(targets)
insert_lst.append(
SeaTarget(work_name=work_name, smiles=smiles, target=tar_lst))
else:
insert_lst.append(
SeaTarget(work_name=work_name, smiles=smiles, target='null'))
SeaTarget.objects.bulk_create(insert_lst)
screen_status(work_name=work_name, status='completed')
email_status(email)
# coding=utf-8
from extra_apps.utils.email_send import email_status
email_status('*****@*****.**')
def perform_virscreen(work_name, user_target, mol_db, pdb_path, resn, res_path,
email):
"""
:param work_name:
:param pdb_path:
:param lig_path:
:param resn:
:param res_path:
:return:
"""
screen_status(work_name=work_name, status='computing')
res = '%s/res' % res_path
if not os.path.exists(res):
os.mkdir(res)
with open(pdb_path, 'r') as f:
lines = f.readlines()
lines = [n.rstrip() for n in lines if n.startswith("ATOM")]
resn_lst = resn.split(";")
x, y, z = [], [], []
for n in lines:
if n.split()[5] in resn_lst:
x.append(float(n.split()[6]))
y.append(float(n.split()[7]))
z.append(float(n.split()[8]))
center_x = float('%.3f' % (sum(x) / len(x)))
center_y = float('%.3f' % (sum(y) / len(y)))
center_z = float('%.3f' % (sum(z) / len(z)))
size_x = max(x) - min(x)
size_y = max(y) - min(y)
size_z = max(z) - min(z)
screen_out = 'screen_out.csv'
res = os.path.join(res_path, 'res')
ligand_db = os.path.join(drugdb, mol_db)
os.system("python %s/extra_apps/vina/prepare_receptor4.py -r /%s"
" -A checkhydrogens " % (BASE_DIR, pdb_path))
pdbqt = pdb_path.split('/')[-1].split('.')[0] + '.pdbqt'
if os.path.exists(pdbqt):
os.system("mv %s %s" % (pdbqt, res_path))
target_list = os.listdir(os.path.join(TARGET_FOLDER_BASE, 'maccs'))
smile_file = os.path.join(ligand_db, 'smiles.csv')
df = pd.read_csv(smile_file, header=None, encoding='utf-8')
smile_data = df.values.tolist()
curr_proc = mp.current_process()
curr_proc.daemon = False
p = mp.Pool(processes=mp.cpu_count())
curr_proc.daemon = True
pool_lst = []
for ligand in smile_data:
smiles = ligand[1]
targets = p.apply_async(pred, args=(smiles, target_list))
pool_lst.append([ligand[0], targets])
p.close()
p.join()
pool_lst = [[n[0], n[1].get()] for n in pool_lst]
for target in pool_lst:
if target[1]:
pred_target = []
for pred_tar in target[1]:
pred_target.append(pred_tar['chembl_id'])
same_target = [l for l in pred_target if l == user_target]
if same_target:
ligand = target[0].split('.')[0] + '.pdbqt'
ligand_path = ligand_db + '/' + ligand
os.system(
"%s --receptor %s/%s --ligand %s --center_x %s --center_y %s"
" --center_z %s --size_x %s --size_y %s --size_z %s" %
('vina', res_path, pdbqt, ligand_path, center_x, center_y,
center_z, size_x, size_y, size_z))
os.system("mv %s %s" %
(ligand_path.split('.')[0] + '_out.pdbqt', res))
os.system(
"python %s/extra_apps/vina/pdbqt_to_pdb.py -f %s -v" %
(BASE_DIR,
os.path.join(
res,
ligand_path.split('/')[-1].split('.')[0] +
'_out.pdbqt')))
else:
ligand_lst = os.listdir(ligand_db)
ligand_lst = [v for v in ligand_lst if v.endswith('pdbqt')]
for ligand_ in ligand_lst:
ligand_path = os.path.join(ligand_db, ligand_)
os.system(
"%s --receptor %s/%s --ligand %s --center_x %s --center_y %s"
" --center_z %s --size_x %s --size_y %s --size_z %s" %
('vina', res_path, pdbqt, ligand_path, center_x, center_y,
center_z, size_x, size_y, size_z))
os.system("mv %s %s" %
(ligand_path.split('.')[0] + '_out.pdbqt', res))
os.system(
"python %s/extra_apps/vina/pdbqt_to_pdb.py -f %s -v" %
(BASE_DIR,
os.path.join(
res,
ligand_path.split('/')[-1].split('.')[0] + '_out.pdbqt')))
res_lst = os.listdir(res)
reg = 'REMARK VINA RESULT:(.*?)\n'
re_reg = re.compile(reg)
screen_res = []
insert_lst = []
for out in res_lst[:]:
out_path = os.path.join(res, out)
with open(out_path, 'r') as f:
data = f.read()
out_lst = re_reg.findall(data)
if out_lst:
med = []
for model in out_lst:
med.append(float(model.split()[0]))
file_name = out.split('_out')[0]
screen_res.append([file_name, min(med)])
insert_lst.append(
SeaVirScreen(work_name=work_name,
affinity=min(med),
path=os.path.join(
res.split('media/')[1], out[:-2])))
if screen_res:
SeaVirScreen.objects.bulk_create(insert_lst)
arr = np.array(screen_res)
df = pd.DataFrame(arr, columns=['id', 'Affinity (kcal/mol)'])
df = df.sort_values("Affinity (kcal/mol)", ascending=False)
df.to_csv(screen_out, index=False)
os.system("mv %s %s" % (screen_out, res))
screen_status(work_name=work_name, status='completed')
email_status(email)
def perform_dock3(work_name, pdb_path, lig_path, reference_path, res_path,
email):
"""
:param work_name:
:param pdb_path:
:param lig_path:
:param reference_path:
:param res_path:
:param email:
:return:
"""
dock_status(work_name=work_name, status='computing')
res = '%s/res' % res_path
if not os.path.exists(res):
os.mkdir(res)
with open(reference_path, 'r') as f:
lines = f.readlines()
lines = [n.rstrip() for n in lines if n.startswith("HETATM")]
x, y, z = [], [], []
for n in lines:
if n.split()[3].startswith("MK"):
x.append(float(n.split()[6]))
y.append(float(n.split()[7]))
z.append(float(n.split()[8]))
center_x = float('%.3f' % (sum(x) / len(x)))
center_y = float('%.3f' % (sum(y) / len(y)))
center_z = float('%.3f' % (sum(z) / len(z)))
size_x = max(x) - min(x)
size_y = max(y) - min(y)
size_z = max(z) - min(z)
os.system("python %s/extra_apps/vina/prepare_receptor4.py -r %s"
" -A checkhydrogens " % (BASE_DIR, pdb_path))
pdbqt = pdb_path.split('/')[-1].split('.')[0] + '.pdbqt'
if os.path.exists(pdbqt):
os.system("mv %s %s" % (pdbqt, res_path))
os.system("python %s/extra_apps/vina/prepare_ligand4.py -l %s -v" %
(BASE_DIR, lig_path))
ligqt = lig_path.split('/')[-1].split('.')[0] + '.pdbqt'
if os.path.exists(ligqt):
os.system("mv %s %s" % (ligqt, res_path))
os.system(
"%s --receptor %s/%s --ligand %s/%s --center_x %s --center_y %s --center_z %s --size_x %s --size_y %s"
" --size_z %s" % ('vina', res_path, pdbqt, res_path, ligqt, center_x,
center_y, center_z, size_x, size_y, size_z))
outqt = ligqt.split('.')[0] + '_out.pdbqt'
outqt_path = os.path.join(res_path, outqt)
if os.path.exists(outqt_path):
reg = 'REMARK VINA RESULT:(.*?)\n'
re_reg = re.compile(reg)
with open(outqt_path, 'r') as f:
data = f.read()
out_lst = re_reg.findall(data)
if out_lst:
med = []
for model in out_lst:
med.append(float(model.split()[0]))
affinity = min(med)
dock_affinity(work_name=work_name, affinity=affinity)
os.system("mv %s %s" % (outqt_path, res))
os.system("python %s/extra_apps/vina/pdbqt_to_pdb.py -f %s -v" %
(BASE_DIR, os.path.join(res, outqt)))
out_path = os.path.join(res.split('media/')[1], outqt[:-2])
dock_out(work_name=work_name, out_path=out_path)
dock_status(work_name=work_name, status='completed')
email_status(email)
def perform_dock2(work_name, pdb_path, lig_path, resn, res_path, email):
"""
:param work_name: 任务id
:param pdb_path: 用户上传受体文件
:param lig_path: 用户上传配体文件
:param resn: 用户指定的关键残基
:param res_path: 任务保存路径
:param email: 用户的邮箱
:return: None 只执行任务,无返回
"""
# 任务开始 修改任务状态为computing
dock_status(work_name=work_name, status='computing')
res = '%s/res' % res_path
# 创建任务保存路径
if not os.path.exists(res):
os.mkdir(res)
# 根据用户指定的关键残基计算对接中心和盒子大小
with open(pdb_path, 'r') as f:
lines = f.readlines()
lines = [n.rstrip() for n in lines if n.startswith("ATOM")]
resn_lst = resn.split(";")
x, y, z = [], [], []
for n in lines:
if n.split()[5] in resn_lst:
x.append(float(n.split()[6]))
y.append(float(n.split()[7]))
z.append(float(n.split()[8]))
center_x = float('%.3f' % (sum(x) / len(x)))
center_y = float('%.3f' % (sum(y) / len(y)))
center_z = float('%.3f' % (sum(z) / len(z)))
size_x = max(x) - min(x)
size_y = max(y) - min(y)
size_z = max(z) - min(z)
# 处理受体文件
os.system("python %s/extra_apps/vina/prepare_receptor4.py -r %s"
" -A checkhydrogens " % (BASE_DIR, pdb_path))
pdbqt = pdb_path.split('/')[-1].split('.')[0] + '.pdbqt'
if os.path.exists(pdbqt):
os.system("mv %s %s" % (pdbqt, res_path))
# 处理配体文件
os.system("python %s/extra_apps/vina/prepare_ligand4.py -l %s -v" %
(BASE_DIR, lig_path))
ligqt = lig_path.split('/')[-1].split('.')[0] + '.pdbqt'
if os.path.exists(ligqt):
os.system("mv %s %s" % (ligqt, res_path))
# 执行分子对接任务
os.system(
"%s --receptor %s/%s --ligand %s/%s --center_x %s --center_y %s --center_z %s --size_x %s --size_y %s"
" --size_z %s" % ('vina', res_path, pdbqt, res_path, ligqt, center_x,
center_y, center_z, size_x, size_y, size_z))
outqt = ligqt.split('.')[0] + '_out.pdbqt'
outqt_path = os.path.join(res_path, outqt)
# 取出分子对接打分值
if os.path.exists(outqt_path):
reg = 'REMARK VINA RESULT:(.*?)\n'
re_reg = re.compile(reg)
with open(outqt_path, 'r') as f:
data = f.read()
out_lst = re_reg.findall(data)
if out_lst:
med = []
for model in out_lst:
med.append(float(model.split()[0]))
affinity = min(med)
dock_affinity(work_name=work_name, affinity=affinity)
os.system("mv %s %s" % (outqt_path, res))
os.system("python %s/extra_apps/vina/pdbqt_to_pdb.py -f %s -v" %
(BASE_DIR, os.path.join(res, outqt)))
out_path = os.path.join(res.split('media/')[1], outqt[:-2])
dock_out(work_name=work_name, out_path=out_path)
# 分子对接任务完成,把任务状态修改为completed
dock_status(work_name=work_name, status='completed')
# 分子渡劫任务完成,给用户发送邮箱
email_status(email)
def perform_virscreen(work_name, user_target, mol_db, pdb_path, resn, res_path,
email):
"""
虚拟筛选任务
:param work_name: 任务id
:param user_target: 用户选择的靶点
:param mol_db: 用户选择的数据库
:param pdb_path: 用户上传的是受体文件
:param resn: 用户选择的关键残疾确定活性位点
:param res_path: 用户任务保存的路径
:param email: 用户的email
:return: None 只执行任务无返回
"""
# 开始执行任务,把任务状态修改为computing
screen_status(work_name=work_name, status='computing')
# 创建保存结果的文件
res = '%s/res' % res_path
if not os.path.exists(res):
os.mkdir(res)
# 根据用户输入的关键残基确认活性位点中心和盒子大小
with open(pdb_path, 'r') as f:
lines = f.readlines()
lines = [n.rstrip() for n in lines if n.startswith("ATOM")]
resn_lst = resn.split(";")
x, y, z = [], [], []
for n in lines:
if n.split()[5] in resn_lst:
x.append(float(n.split()[6]))
y.append(float(n.split()[7]))
z.append(float(n.split()[8]))
center_x = float('%.3f' % (sum(x) / len(x)))
center_y = float('%.3f' % (sum(y) / len(y)))
center_z = float('%.3f' % (sum(z) / len(z)))
size_x = max(x) - min(x)
size_y = max(y) - min(y)
size_z = max(z) - min(z)
screen_out = 'screen_out.csv'
res = os.path.join(res_path, 'res')
# 找到需要筛选的数据库
ligand_db = os.path.join(drugdb, mol_db)
# 处理受体文件生成晶格文件
os.system("python %s/extra_apps/vina/prepare_receptor4.py -r /%s"
" -A checkhydrogens " % (BASE_DIR, pdb_path))
pdbqt = pdb_path.split('/')[-1].split('.')[0] + '.pdbqt'
if os.path.exists(pdbqt):
os.system("mv %s %s" % (pdbqt, res_path))
# 获取所有靶点列表
target_list = os.listdir(os.path.join(TARGET_FOLDER_BASE, 'maccs'))
# 读取数据库中所有分子的smiles
smile_file = os.path.join(ligand_db, 'smiles.csv')
df = pd.read_csv(smile_file, header=None, encoding='utf-8')
smile_data = df.values.tolist()
# 使用多进程技术进行并行计算
curr_proc = mp.current_process()
curr_proc.daemon = False
p = mp.Pool(processes=mp.cpu_count())
curr_proc.daemon = True
pool_lst = []
for ligand in smile_data:
smiles = ligand[1]
# 把靶点预测任务添加到进程池中进行并行计算
targets = p.apply_async(pred, args=(smiles, target_list))
pool_lst.append([ligand[0], targets])
p.close()
p.join()
# 并行计算完成之后取出并行计算任务的结果
pool_lst = [[n[0], n[1].get()] for n in pool_lst]
for target in pool_lst:
if target[1]:
pred_target = []
for pred_tar in target[1]:
pred_target.append(pred_tar['chembl_id'])
same_target = [l for l in pred_target if l == user_target]
# 判断预测出来的靶点与用户制定的靶点是否一致
if same_target:
ligand = target[0].split('.')[0] + '.pdbqt'
ligand_path = ligand_db + '/' + ligand
# SEA算法预测完成后调用后分子对接软件进行分子对接
os.system(
"%s --receptor %s/%s --ligand %s --center_x %s --center_y %s"
" --center_z %s --size_x %s --size_y %s --size_z %s" %
('vina', res_path, pdbqt, ligand_path, center_x, center_y,
center_z, size_x, size_y, size_z))
os.system("mv %s %s" %
(ligand_path.split('.')[0] + '_out.pdbqt', res))
os.system(
"python %s/extra_apps/vina/pdbqt_to_pdb.py -f %s -v" %
(BASE_DIR,
os.path.join(
res,
ligand_path.split('/')[-1].split('.')[0] +
'_out.pdbqt')))
else:
ligand_lst = os.listdir(ligand_db)
ligand_lst = [v for v in ligand_lst if v.endswith('pdbqt')]
for ligand_ in ligand_lst:
ligand_path = os.path.join(ligand_db, ligand_)
os.system(
"%s --receptor %s/%s --ligand %s --center_x %s --center_y %s"
" --center_z %s --size_x %s --size_y %s --size_z %s" %
('vina', res_path, pdbqt, ligand_path, center_x, center_y,
center_z, size_x, size_y, size_z))
os.system("mv %s %s" %
(ligand_path.split('.')[0] + '_out.pdbqt', res))
os.system(
"python %s/extra_apps/vina/pdbqt_to_pdb.py -f %s -v" %
(BASE_DIR,
os.path.join(
res,
ligand_path.split('/')[-1].split('.')[0] + '_out.pdbqt')))
# 取出所有分子对接任务配体的多个构想打分值
res_lst = os.listdir(res)
reg = 'REMARK VINA RESULT:(.*?)\n'
re_reg = re.compile(reg)
screen_res = []
insert_lst = []
for out in res_lst[:]:
out_path = os.path.join(res, out)
with open(out_path, 'r') as f:
data = f.read()
out_lst = re_reg.findall(data)
if out_lst:
med = []
for model in out_lst:
med.append(float(model.split()[0]))
file_name = out.split('_out')[0]
screen_res.append([file_name, min(med)])
insert_lst.append(
SeaVirScreen(work_name=work_name,
affinity=min(med),
path=os.path.join(
res.split('media/')[1], out[:-2])))
# 保存虚拟筛选任务的结果
if screen_res:
SeaVirScreen.objects.bulk_create(insert_lst)
arr = np.array(screen_res)
df = pd.DataFrame(arr, columns=['id', 'Affinity (kcal/mol)'])
df = df.sort_values("Affinity (kcal/mol)", ascending=False)
df.to_csv(screen_out, index=False)
os.system("mv %s %s" % (screen_out, res))
# 虚拟筛选任务完成之后把热舞状态修改为completed
screen_status(work_name=work_name, status='completed')
# 任务完成之后给用户发邮箱提示
email_status(email)
def perform_dock(work_name, center_x, center_y, center_z, size_x, size_y,
size_z, pdb_path, lig_path, res_path, email):
"""
用户指定对接中心以及盒子大小
:param work_name: 任务id
:param center_x: 用户制定的对接中心以及盒子大小六个参数
:param center_y:
:param center_z:
:param size_x:
:param size_y:
:param size_z:
:param pdb_path: 用户上传的受体文件
:param lig_path: 用户上传的配体文件
:param res_path: 用户任务保存路径
:param email: 用户的邮箱
:return: None 只执行任务无返回
"""
# 任务开始 修改任务状态为computing
dock_status(work_name=work_name, status='computing')
# email = UserProfile.objects.filter(id=AutoDock.objects.filter(pdb_file=pdb_path.replace('/home/wz/pywork/try27/drug/media/', '')).values()[0]['user_id']).values()[0]['email']
# 创建任务保存路径
res = os.path.join(res_path, 'res')
if not os.path.exists(res):
os.mkdir(res)
# 处理受体文件
os.system("python %s/extra_apps/vina/prepare_receptor4.py -r %s"
" -A checkhydrogens " % (BASE_DIR, pdb_path))
pdbqt = pdb_path.split('/')[-1].split('.')[0] + '.pdbqt'
if os.path.exists(pdbqt):
os.system("mv %s %s" % (pdbqt, res_path))
# 处理配体文件
os.system("python %s/extra_apps/vina/prepare_ligand4.py -l %s -v" %
(BASE_DIR, lig_path))
ligqt = lig_path.split('/')[-1].split('.')[0] + '.pdbqt'
if os.path.exists(ligqt):
os.system("mv %s %s" % (ligqt, res_path))
# 执行对接
os.system(
"%s --receptor %s/%s --ligand %s/%s --center_x %s --center_y %s --center_z %s --size_x %s --size_y %s"
" --size_z %s" % ('vina', res_path, pdbqt, res_path, ligqt, center_x,
center_y, center_z, size_x, size_y, size_z))
outqt = ligqt.split('.')[0] + '_out.pdbqt'
outqt_path = os.path.join(res_path, outqt)
# 取出分子对接打分值
if os.path.exists(outqt_path):
reg = 'REMARK VINA RESULT:(.*?)\n'
re_reg = re.compile(reg)
with open(outqt_path, 'r') as f:
data = f.read()
out_lst = re_reg.findall(data)
if out_lst:
med = []
for model in out_lst:
med.append(float(model.split()[0]))
affinity = str(min(med))
dock_affinity(work_name=work_name, affinity=affinity)
os.system("mv %s %s" % (outqt_path, res))
os.system("python %s/extra_apps/vina/pdbqt_to_pdb.py -f %s -v" %
(BASE_DIR, os.path.join(res, outqt)))
out_path = os.path.join(res.split('media/')[1], outqt[:-2])
dock_out(work_name=work_name, out_path=out_path)
email_status(
email, '/home/wz/pywork/try27/drug/media/' +
out_path.encode('raw_unicode_escape'))
# 对接任务完成,把任务状态修改为completed
dock_status(work_name=work_name, status='completed')
shutil.rmtree(res_path)
def perform_screen2_user(work_name, user_target, user_db_name, pdb_path,
resi_path, res_path):
"""
用户提供数据库以及残基进行筛选
:param work_name:
:param user_db_name:
:param pdb_path:
:param resi_path:
:param res_path:
:return:
"""
screen_status2(work_name, status='computing')
email = UserProfile.objects.filter(id=VirtualScreen2.objects.filter(
pdb_file=pdb_path.replace('/home/wz/pywork/try27/drug/media/', '')
).values()[0]['user_id']).values()[0]['email']
with open(resi_path, 'r') as f:
lines = f.readlines()
lines = [n.rstrip() for n in lines if len(n) > 1]
x, y, z = [], [], []
for n in lines:
x.append(float(n[30:38]))
y.append(float(n[38:46]))
z.append(float(n[46:54]))
center_x = float('%.3f' % (sum(x) / len(x)))
center_y = float('%.3f' % (sum(y) / len(y)))
center_z = float('%.3f' % (sum(z) / len(z)))
size_x = max(x) - min(x)
size_y = max(y) - min(y)
size_z = max(z) - min(z)
screen_out = 'screen_out.csv'
user_db = os.path.join(res_path, 'userdb')
res = os.path.join(res_path, 'res')
if not os.path.exists(user_db):
os.mkdir(user_db)
if not os.path.exists(res):
os.mkdir(res)
input_file = os.path.join(res_path, user_db_name)
gen_user_db_qt_smiles(input_file, user_db)
os.system("python %s/extra_apps/vina/prepare_receptor4.py -r /%s"
" -A checkhydrogens " % (BASE_DIR, pdb_path))
pdbqt = pdb_path.split('/')[-1].split('.')[0] + '.pdbqt'
if os.path.exists(pdbqt):
os.system("mv %s %s" % (pdbqt, res_path))
if len(user_target) > 1:
user_target = user_target.split(';')
target_list = os.listdir(os.path.join(TARGET_FOLDER_BASE, 'maccs'))
smile_file = os.path.join(user_db, 'smiles.csv')
df = pd.read_csv(smile_file, header=None, encoding='utf-8')
smile_data = df.values.tolist()
curr_proc = mp.current_process()
curr_proc.daemon = False
p = mp.Pool(processes=mp.cpu_count())
curr_proc.daemon = True
pool_lst = []
for ligand in smile_data:
smiles = ligand[1]
targets = p.apply_async(pred, args=(smiles, target_list))
pool_lst.append([ligand[0], targets])
p.close()
p.join()
pool_lst = [[n[0], n[1].get()] for n in pool_lst]
for target in pool_lst:
if target[1]:
pred_target = []
for pred_tar in target[1]:
pred_target.append(pred_tar['chembl_id'])
same_target = [l for l in pred_target if l in user_target]
if same_target:
ligand = target[0].split('.')[0] + '.pdbqt'
ligand_path = os.path.join(user_db, ligand)
if os.path.exists(ligand_path):
os.system(
"%s --receptor %s/%s --ligand %s --center_x %s --center_y %s"
" --center_z %s --size_x %s --size_y %s --size_z %s"
% ('vina', res_path, pdbqt, ligand_path, center_x,
center_y, center_z, size_x, size_y, size_z))
os.system(
"mv %s %s" %
(ligand_path.split('.')[0] + '_out.pdbqt', res))
os.system(
"python %s/extra_apps/vina/pdbqt_to_pdb.py -f %s -v"
% (BASE_DIR,
os.path.join(
res,
ligand_path.split('/')[-1].split('.')[0] +
'_out.pdbqt')))
else:
ligand_lst = os.listdir(user_db)
ligand_lst = [v for v in ligand_lst if v.endswith('pdbqt')]
for ligand_ in ligand_lst:
ligand_path = os.path.join(user_db, ligand_)
os.system(
"%s --receptor %s/%s --ligand %s --center_x %s --center_y %s"
" --center_z %s --size_x %s --size_y %s --size_z %s" %
('vina', res_path, pdbqt, ligand_path, center_x, center_y,
center_z, size_x, size_y, size_z))
os.system("mv %s %s" %
(ligand_path.split('.')[0] + '_out.pdbqt', res))
os.system(
"python %s/extra_apps/vina/pdbqt_to_pdb.py -f %s -v" %
(BASE_DIR,
os.path.join(
res,
ligand_path.split('/')[-1].split('.')[0] + '_out.pdbqt')))
res_lst = os.listdir(res)
reg = 'REMARK VINA RESULT:(.*?)\n'
re_reg = re.compile(reg)
screen_res = []
insert_lst = []
for out in res_lst[:]:
out_path = os.path.join(res, out)
with open(out_path, 'r') as f:
data = f.read()
out_lst = re_reg.findall(data)
if out_lst:
med = []
for model in out_lst:
med.append(float(model.split()[0]))
file_name = out.split('_out')[0]
screen_res.append([file_name, min(med)])
insert_lst.append(
Screen(work_name=work_name,
screen_cat='screen2',
affinity=min(med),
path=os.path.join(res.split('media/')[1], out[:-2])))
if screen_res:
Screen.objects.bulk_create(insert_lst)
arr = np.array(screen_res)
df = pd.DataFrame(arr, columns=['id', 'Affinity (kcal/mol)'])
df = df.sort_values("Affinity (kcal/mol)", ascending=False)
df.to_csv(screen_out, index=False)
os.system("mv %s %s" % (screen_out, res))
email_status(email, res_path + '/res/screen_out.csv')
else:
email_status(email, '')
screen_status2(work_name, status='completed')
