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convergence_plot.py
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convergence_plot.py
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import random
import os
import shutil
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from kmer_parser import setup_directory
from rich import box, print as rich_print
from rich.panel import Panel
from rich.progress import BarColumn, Progress, SpinnerColumn, MofNCompleteColumn, TaskID, TextColumn, TimeElapsedColumn
from rich.table import Table
from rich.align import Align
from generate_peptide import default_progress
from generate_peptide import load_descriptors_score, score_kmers, pep_physical_analysis
from generate_peptide import generate_prob_dict_from_excel, generate_tango_script, generate_fasta_file
print(
" "
)
print(
" "
)
print(
" ==========---- "
)
print(
" ++++++++++******++= "
)
print(
" -+++++++++********##- "
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print(
" --------- --- --- ----------- --- ---------- --- --- --- --- --------- ---------- --- ++++++++++*********+ "
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print(
" #@@@@@@@@@#-#@@#= +@@=@@@@@@@@@@% @@@ *@@@@@@@@@@=*@@+ +@@#=@@@ @@@ #@@@@@@@@@#=+%@@@@@@@@%+=@@# =+++++++++*******+++= "
)
print(
" @@@=====%@@-#@@@#= +@@-===+@@%==== @@@ %@@+=======-*@@+ +@@#=@@@% @@@ @@@+====#%%=#@@*====*@@*=@@# +=======++***+++++++ "
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print(
" @@@ %@@-#@@@@#- +@@* -@@# @@@ %@@- @@+ +@@#=@@@@% @@@ @@@- #@@+ +@@*=@@# -=+++++++++++= "
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print(
" @@@@@@@@@@@-#@@#@#=+@@ -@@# @@@ %@@@@@@@= *@@+ +@@#=@@%+%@*-@@@ @@@- #@@@#-#@@@@@@@@@@*=@@# =+***+++++++++++ "
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print(
" @@@+++++%@@-#@@= @@@@@ -@@# @@@ %@@+++++- *@@+ +@@#=@@% -@@@@@@ @@@- =+%@@=#@@#++++#@@*=@@# =+******++++++++=- "
)
print(
" @@@ %@@-#@@= @@@@ -@@# @@@ %@@- *@@+ +@@#=@@% @@@@@ @@@- #@@=#@@+ +@@*=@@# -+**********+++++- "
)
print(
" @@@ %@@-#@@= @@@ -@@# @@@ %@@- *@@@@@@@@@@#=@@% @@@@ @@@@@@@@@@@=#@@+ +@@*=@@@@@@@@@@@-##***********+- "
)
print(
" +++ +++-=++- =++= -+++ +++ +++ =++++++++= -+++ +++ +++++++++- =++- -++= -++++++++++ *#********+- "
)
print(
" =##******++***++++++===- "
)
print(
" *##**+++++++**********##+ "
)
print(
" -#*++++++++++***********+- "
)
print(
" -*******--*******-*******-+*******-*+-******+ =******+-******+ ==++++++++++********+++++ "
)
print(
" -@%+++#@*#@*+++++-@%+++#@*=++@@*++=@%+@#+++%@=%@+++++++@#+++#%= --===+++*****++++++++- "
)
print(
" -@%+++#@#@*+++ -@%+++#@ %@- =@%+@* %@=%@*++= +@#++++= =**++++++++++= "
)
print(
" -@@****-#@#**+ -@%*****- %@- =@%+@ %@=%@***= *****%@= =+***+++++++++++ "
)
print(
" -@% #@*=====-@% %@- =@%+@#===%@=%@+======#*===%@= =+*******+++++++=- "
)
print(
" -#* -#######-#* # -#*=######*-+######*-*##### +***********++++- "
)
print(
" *#***********=- "
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print(
" +##********+=---- "
)
print(
" -##******++++*********++= "
)
print(
" +###*++++++++**********#*- "
)
print(
" #*++++++++++***********++ "
)
print(
" -====++++++++********++++= "
)
print(
" --==******++++++ "
)
print(
" *#****++++++ "
)
print(
" #*****+++- "
)
print(
" =#****+- "
)
print(
" **+- "
)
print(
" "
)
print(
" "
)
print(
"\n \n \n #######################################################################################################################################################"
)
# inputs and outputs
SCORE_FILE = "results/descriptors_activity_scores.tsv"
PAM2_EXCEL_FILE = "resources/PAM_2_substitution_probabilities_formated.xlsx"
output_file = 'results/generated_peptides.fasta'
tango_dir="".join(os.getcwd() +'/tango_results/')
tango_output="".join(tango_dir +'generated_peptides_tango.sh')
AA_ORDER = "ARNDCQEGHILKMFPSTWYV"
DEFAULT_PEPTIDE = "RGLRRLGRKIAHGVKKYG"
NB_PEPTIDE = 20
NB_ITERATIONS = 1000
# Selected reduction dictionary
REDUCE = 6
def generate_peptides(
aa_probs: dict, NB_PEPTIDE: int = NB_PEPTIDE, DEFAULT_PEPTIDE: str = DEFAULT_PEPTIDE, bootstrap: int = NB_ITERATIONS
):
Align(rich_print(Panel(f"Generation of {NB_PEPTIDE} peptides", style ="light_slate_blue", expand = False)))
# Test
boot: list[int] = []
score_evolution: list[float] = []
helix_propensity_evolution: list[float] = []
charge_evolution: list[float] = []
periodicity_evolution: list[float] = []
hydrophobic_moment_evolution: list[float] = []
gravy_evolution: list[float] = []
peptides_generated: list[str] = []
for p in range(0, NB_PEPTIDE):
pep_seq = DEFAULT_PEPTIDE
with default_progress() as progress:
task_id: TaskID = progress.add_task(f"Generating peptide number {p+1} out of {NB_PEPTIDE}", total=bootstrap)
for i in range(bootstrap):
# Randomisation of mutation location in the peptide sequence should be applied to biological form (To develop)
random_index = random.randint(0, len(pep_seq) - 1)
# Replacing the amino acid selected to a new one
random_amino_acid = pep_seq[random_index]
prob = aa_probs[random_amino_acid]
new_amino_acid = random.choices(AA_ORDER)[0]
new_peptide = pep_seq[:random_index] + new_amino_acid + pep_seq[random_index + 1 :]
# Calculating scores of previous and new peptides sequences
peptide_score = score_kmers(pep_seq, REDUCE, score_dictionary)
physical_analysis: list[float] = pep_physical_analysis(pep_seq)
new_peptide_score = score_kmers(new_peptide, REDUCE, score_dictionary)
# Plot evolution of scores
boot.append(i)
score_evolution.append(peptide_score)
helix_propensity_evolution.append(physical_analysis[1])
charge_evolution.append(physical_analysis[2])
periodicity_evolution.append(physical_analysis[3])
hydrophobic_moment_evolution.append(physical_analysis[4])
gravy_evolution.append(physical_analysis[5])
peptides_generated.append(pep_seq)
# The peptide is selected if new score is higher
score_difference = new_peptide_score - peptide_score
if score_difference > 0:
pep_seq = new_peptide
# Progress bar
progress.update(task_id, advance = 1)
table = Table(
title = f"[i]Result of peptide[/] [b royal_blue1]{pep_seq}",
show_header = False,
box = box.ROUNDED,
style = "cyan",
title_style = "",
)
table.add_row("Final score", f"{score_kmers(pep_seq,REDUCE,score_dictionary)}")
table.add_row("Final helix probability", f"{round(physical_analysis[1], 2)}%")
table.add_row("Final global charge Q", f"{physical_analysis[2]}")
table.add_row("Final hydrophobicity frequency", f"{physical_analysis[3]}")
table.add_row("Final hydrophobicity moment", f"{physical_analysis[4]}")
table.add_row("Final Average hydrophobicity", f"{physical_analysis[5]}")
rich_print("\n", table, "\n")
final_df=pd.DataFrame(list(zip(peptides_generated, score_evolution, charge_evolution, periodicity_evolution , helix_propensity_evolution ,gravy_evolution , hydrophobic_moment_evolution)), columns = ["peptide_sequence", "activity_score" , "net_charge" , "hydrophobicity_periodicity" ,"helix_propensity","hydrophobicity_average", "hydrophobic_moment"])
rich_print(
Align(
Panel("Generated peptides and their respective properties", style = "light_slate_blue", expand = False),
align="center",
)
)
print(final_df)
final_df.to_excel("results/de_novo_peptide_library.xlsx")
print("Run in vivo aggregation study at http://bioinf.uab.es/aggrescan/ using generated fasta file in results/")
print("Run in vitro aggregation study using Tango algorithm using generated bash file {}".format(tango_output))
# Generate the FASTA file
#generate_fasta_file(peptides_generated, range(0, nb_peptide), output_file)
#generate_tango_script(peptides_generated, range(0, nb_peptide), tango_output)
'''
Convergence plots
'''
#define dataframe
df= {
"Score" : score_evolution ,
"Charge": charge_evolution ,
"Periodicity" : periodicity_evolution ,
"Hydrophobic moment" : hydrophobic_moment_evolution,
"Hydrophobicity average" : gravy_evolution,
"Helix propensity" : helix_propensity_evolution }
df = pd.DataFrame(df)
def transform_dataframe(df):
transformed_df = pd.DataFrame(columns=['Iterations', 'Values', 'Global descriptors'])
for col in df.columns:
variable_name = col
values = df[col].values.tolist()
x_values = list(range(NB_ITERATIONS))*NB_PEPTIDE
temp_df = pd.DataFrame({'Iterations': x_values, 'Values': values, 'Global descriptors': variable_name})
transformed_df = pd.concat([transformed_df, temp_df], ignore_index=True)
return transformed_df
df = transform_dataframe(df)
plot = sns.lineplot(x="Iterations", y="Values",
hue="Global descriptors",
data=df)
sns.move_legend(
plot, "lower center",
bbox_to_anchor=(.5, 1), ncol=3, title=None, frameon=False,
)
plt.savefig("results/Convergence_plot.png")
if __name__ == "__main__":
# Getting scores from CSV file
score_dictionary = load_descriptors_score()
# Import substitution probabilities from PAM2 data frame relative to mutation frequencies with conservation excluded
pam2_probs = generate_prob_dict_from_excel()
### generation and optimisation of a peptide sequence
generate_peptides(pam2_probs)