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feb41b4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
def plot_attention_map(csv_file, output_file=None, figsize=(20, 10)):
data = pd.read_csv(csv_file)
mutations = data['mutant']
scores = data['DMS_score']
sequence_length = max(int(mutation[1:-1]) for mutation in mutations)
original_aa = {}
for mutation in mutations:
pos = int(mutation[1:-1])
original_aa[pos] = mutation[0]
amino_acids = list('ACDEFGHIKLMNPQRSTVWY')
score_matrix = np.zeros((len(amino_acids), sequence_length))
for mutation, score in zip(mutations, scores):
position = int(mutation[1:-1]) - 1
target = mutation[-1]
if target in amino_acids:
score_matrix[amino_acids.index(target), position] = score
plt.figure(figsize=figsize)
ax = sns.heatmap(score_matrix, cmap='viridis', cbar=True,
xticklabels=range(1, sequence_length+1),
yticklabels=amino_acids)
plt.title('Amino Acid Substitution Scores', fontsize=16)
plt.xlabel('Position in Protein Sequence', fontsize=14)
plt.ylabel('Substituted Amino Acid', fontsize=14)
if output_file:
plt.savefig(output_file, dpi=300, bbox_inches='tight')
plt.show()
plot_attention_map('scores/phi29_42.csv', output_file='amino_acid_attention_map.png')
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