def __init__(self, pdbfile, statistics_collector):
self.pe_instance = PoseEnergies()
self.pe_instance.loadFile(pdbfile)
self.statistics_collector = statistics_collector
self.z_score_dict = {}
self.position_positive_shift = 0.0
self.value_pos = 0.0
self.position_negative_shift = 0.0
self.value_neg = 0.0
self.result = ''
self.z_sorted = []
FileList = ['12as_nohet_1_relax.pdb', '12e8_nohet_1_relax.pdb', '12ca_nohet_1_relax.pdb', '12gs_nohet_1_relax.pdb']
FileList_modified = ['12as_nohet_1_relax.pdb', '12e8_nohet_1_relax.pdb', 'test_modified_12ca_nohet_1_relax.pdb', '12gs_nohet_1_relax.pdb']
statistics_collector = ResTypesStatisticsCollector()
statistics_collector_mod = ResTypesStatisticsCollector()
for name in FileList:
filename = name
pe_instance = PoseEnergies()
pe_instance.loadFile(filename)
statistics_collector.add_pose_energies(pe_instance)
for name in FileList_modified:
filename = name
pe_instance = PoseEnergies()
pe_instance.loadFile(filename)
statistics_collector_mod.add_pose_energies(pe_instance)
#best score terms
#print statistics_collector.restype_av_scores['GLY'].get_best_score('fa_rep')
#print statistics_collector_mod.restype_av_scores['GLY'].get_best_score('fa_rep')
if float(statistics_collector_mod.restype_av_scores['GLY'].get_best_score('fa_rep')[0]) == -3.333333333:
print 'No error seen, when trying to find the best score!'
import sys
sys.path.append('../../src')
from ResidueEnergies import ResidueEnergies, PoseEnergies,critical_distance_squared
from ResTypeAverageScores import ResTypeAverageScores
from ResTypesStatisticsCollector import ResTypesStatisticsCollector
from constants import *
import numpy as np
statistics_collector = ResTypesStatisticsCollector()
pdbfile1 = "../stddev_and_mean/avetest_mock.pdb"
pdbfile2 = "../stddev_and_mean/avetest_mock2.pdb"
pe_instance_1 = PoseEnergies()
pe_instance_1.loadFile( pdbfile1 )
pe_instance_2 = PoseEnergies()
pe_instance_2.loadFile( pdbfile2)
statistics_collector.add_pose_energies( pe_instance_1)
statistics_collector.add_pose_energies( pe_instance_2)
#########################################
error_combined_scored = False
#combination of score terms
score_terms_to_be_combined = ['faketerm1', 'faketerm2']
for aminoacid in aminoacids:
from ResidueEnergies import ResidueEnergies, PoseEnergies,critical_distance_squared from ResTypeAverageScores import ResTypeAverageScores from constants import * import cPickle import numpy as np import sys ############################################################################# #calculate number of neighbors error4_seen = False filename = '10mh_nohet_1_relax.pdb' pe_instance = PoseEnergies() pe_instance.loadFile(filename) if pe_instance.res_e_list[209].res_type != 'LYS' or pe_instance.res_e_list[209].number_of_neighbors != 8: error4_seen = True elif pe_instance.res_e_list[117].res_type != 'MET' or pe_instance.res_e_list[117].number_of_neighbors != 16: error4_seen = True if not error4_seen: print 'No error when counting neighbors' else: print 'ERROR: Error in counting neighbors'
class ZScoreCalculator(object):
def __init__(self, pdbfile, statistics_collector):
self.pe_instance = PoseEnergies()
self.pe_instance.loadFile(pdbfile)
self.statistics_collector = statistics_collector
self.z_score_dict = {}
self.position_positive_shift = 0.0
self.value_pos = 0.0
self.position_negative_shift = 0.0
self.value_neg = 0.0
self.result = ''
self.z_sorted = []
def calculate_z_scores(self, score_term):
for re in self.pe_instance.res_e_list:
score = 0.0
if '+' in score_term:
score_terms = score_term.split("+", 1)
score = re.score_dict[score_terms[0]] - re.score_dict[score_terms[1]]
elif '-' in score_term:
score_terms = score_term.split("-", 1)
score = re.score_dict[score_terms[0]] - re.score_dict[score_terms[1]]
else:
score = re.score_dict[score_term]
neighbor_situation = determine_neighbor_situation(re.res_type, re.number_of_neighbors)
stats = self.statistics_collector.get_mean_and_stddev(re.res_type, score_term, re.number_of_neighbors)
mean = stats[0]
stddev = stats[1]
zscore = (score - mean)/stddev
self.z_score_dict[re.res_num] = [zscore, re.res_type, mean, stddev, score, re.number_of_neighbors]
sort = sorted(self.z_score_dict, key=self.z_score_dict.get, reverse = True)
for key in sort:
self.z_sorted.append([key, self.z_score_dict[key][1], round(self.z_score_dict[key][0], 4)])
return self.z_score_dict
def calculate_differences_in_z_scores(self, other_instance, score_term):
if len(self.pe_instance.res_e_list) != len(other_instance.pe_instance.res_e_list):
sys.exit('ERROR: The compared sequences do not have the same length!')
self.calculate_z_scores(score_term)
other_instance.calculate_z_scores(score_term)
delta_z_scores = {}
for res_num in range(len(self.pe_instance.res_e_list)):
if not str(res_num) in self.z_score_dict.keys():
#print 'skipped %s' %res_num
continue
else:
other = other_instance.z_score_dict[str(res_num)]
sel = self.z_score_dict[str(res_num)]
mutation = '-'
if sel[1] != other[1]:
mutation = 'mutation'
if sel[2] != other[2] and mutation == '-':
#print 'adapted mean and stddev from wild type residue for %s i.e. %s at pos. %s. mean changed from %s to %s' %(sel[1], other[1], res_num, other[2], sel[2])
#print 'neighbors changed from %s to %s' %(sel[5], other[5])
other[0] = (other[4] - sel[2])/sel[3]
delta_z_scores[str(res_num)] = (other[0] - sel[0]), mutation
other_instance.position_positive_shift = max(delta_z_scores, key=delta_z_scores.get)
other_instance.value_pos = delta_z_scores[other_instance.position_positive_shift][0]
other_instance.position_negative_shift = min(delta_z_scores, key=delta_z_scores.get)
other_instance.value_neg = delta_z_scores[other_instance.position_negative_shift][0]
delta_z_scores_sorted = sorted(delta_z_scores, key=delta_z_scores.get, reverse=True)
other_instance.result = []
for key in delta_z_scores_sorted:
other_instance.result.append([key, round(delta_z_scores[key][0], 4), delta_z_scores[key][1]])
return other_instance.result
def get_goodz(self, goodz):
string_goodz = ''
for entry in self.result:
if entry[1] <= goodz:
if string_goodz =='':
string_goodz = entry[0]
else:
string_goodz = '+'.join([string_goodz, entry[0]])
print 'select goodz, resi %s' %string_goodz
def get_badz(self, badz):
string_badz = ''
for entry in self.result:
if entry[1] >= badz:
if string_badz =='':
string_badz = entry[0]
else:
string_badz = '+'.join([string_badz, entry[0]])
print 'select badz, resi %s' %string_badz
#!/usr/bin/python
import cPickle
import sys
sys.path.append('../src')
from ResidueEnergies import ResidueEnergies, PoseEnergies
from ResTypeAverageScores import ResTypeAverageScores
import numpy as np
from ResTypesStatisticsCollector import ResTypesStatisticsCollector
aminoacids = ['ALA', 'CYS', 'ASP', 'GLU', 'PHE', 'GLY', 'HIS', 'ILE', 'LYS', 'LEU', 'MET', 'ASN', 'PRO', 'GLN', 'ARG', 'SER', 'THR', 'VAL', 'TRP', 'TYR']
pose_energies = PoseEnergies() # creates instance of PoseEnergies
pose_energies.loadFile('stddev_and_mean/avetest_mock.pdb')
statistics_collector = ResTypesStatisticsCollector()
pdbfile1 = "stddev_and_mean/avetest_mock.pdb"
pdbfile2 = "stddev_and_mean/avetest_mock2.pdb"
pe_instance_1 = PoseEnergies()
pe_instance_1.loadFile( pdbfile1 )
pe_instance_2 = PoseEnergies()
pe_instance_2.loadFile( pdbfile2)
statistics_collector.add_pose_energies( pe_instance_1)
statistics_collector.add_pose_energies( pe_instance_2)
