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837ee6d | 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 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 | import numpy as np
def zscale_padding(seq_list, padding):
zscale = {
'A': [0.24, -2.32, 0.60, -0.14, 1.30],
'C': [0.84, -1.67, 3.71, 0.18, -2.65],
'D': [3.98, 0.93, 1.93, -2.46, 0.75],
'E': [3.11, 0.26, -0.11, -0.34, -0.25],
'F': [-4.22, 1.94, 1.06, 0.54, -0.62],
'G': [2.05, -4.06, 0.36, -0.82, -0.38],
'H': [2.47, 1.95, 0.26, 3.90, 0.09],
'I': [-3.89, -1.73, -1.71, -0.84, 0.26],
'K': [2.29, 0.89, -2.49, 1.49, 0.31],
'L': [-4.28, -1.30, -1.49, -0.72, 0.84],
'M': [-2.85, -0.22, 0.47, 1.94, -0.98],
'N': [3.05, 1.62, 1.04, -1.15, 1.61],
'P': [-1.66, 0.27, 1.84, 0.70, 2.00],
'Q': [1.75, 0.50, -1.44, -1.34, 0.66],
'R': [3.52, 2.50, -3.50, 1.99, -0.17],
'S': [2.39, -1.07, 1.15, -1.39, 0.67],
'T': [0.75, -2.18, -1.12, -1.46, -0.40],
'V': [-2.59, -2.64, -1.54, -0.85, -0.02],
'W': [-4.36, 3.94, 0.59, 3.44, -1.59],
'Y': [-2.54, 2.44, 0.43, 0.04, -1.47],
'-': [0.00, 0.00, 0.00, 0.00, 0.00] # For gaps or unknown amino acids
}
feat_list = []
for seq in seq_list:
feat = [zscale.get(aa, zscale['-']) for aa in seq]
feat += [[0] * 5] * (padding - len(seq))
feat_list.append(feat)
return np.array(feat_list)
def one_hot_padding(seq_list, padding):
feat_list = []
one_hot = {}
aa = ['A', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'K', 'L', 'M', 'N', 'P', 'Q', 'R', 'S', 'T', 'V', 'W', 'Y']
for i in range(len(aa)):
one_hot[aa[i]] = [0] * 20
one_hot[aa[i]][i] = 1
for seq in seq_list:
feat = [one_hot[aa] for aa in seq] + [[0] * 20] * (padding - len(seq))
feat_list.append(feat)
return np.array(feat_list)
def position_encoding(padding, d=20, b=1000):
pos_encoding = np.zeros((padding, d))
for pos in range(padding):
for i in range(d // 2):
pos_encoding[pos, 2 * i] = np.sin(pos / (b ** (2 * i / d)))
pos_encoding[pos, 2 * i + 1] = np.cos(pos / (b ** (2 * i / d)))
return pos_encoding
blosum62 = {
'A': [4, -1, -2, -2, 0, -2, -1, 0, -2, -1, -1, -1, -1, -1, -1, 1, 0, 0, -3, -2],
'C': [-1, 9, -3, -4, -2, -3, -3, -3, -3, -1, -1, -3, -3, -3, -3, -1, -1, -1, -2, -2],
'D': [-2, -3, 6, 2, -3, 1, -1, -1, -1, -4, -3, 1, 0, -3, -3, 0, -1, -3, -4, -3],
'E': [-2, -4, 2, 5, -3, 0, -2, -1, 1, -3, -2, 0, 0, -3, -2, 0, -1, -2, -3, -2],
'F': [0, -2, -3, -3, 6, -3, -1, -3, -1, 0, 0, -3, -4, -3, -3, -2, -2, -3, 1, 3],
'G': [-2, -3, 1, 0, -3, 6, -2, -4, -2, -4, -3, 0, -2, -3, -2, 0, -2, -3, -2, -3],
'H': [-1, -3, -1, -2, -1, -2, 8, -3, -1, -3, -2, -1, -2, -1, -2, -2, -2, -2, 2, 2],
'I': [0, -3, -1, -1, -3, -4, -3, 4, -3, -3, -3, -1, -3, -3, -3, -3, -1, 2, -3, -1],
'K': [-2, -3, -1, 1, -1, -2, -1, -3, 5, -2, -1, 0, -1, 1, 0, -1, 0, -2, -3, -2],
'L': [-1, -1, -4, -3, 0, -4, -3, -3, -2, 4, 2, -3, -3, -2, -2, -2, -1, 1, -2, -1],
'M': [-1, -1, -3, -2, 0, -3, -2, -3, -1, 2, 5, -2, -2, 0, -1, -2, -1, 1, -1, -1],
'N': [-1, -3, 1, 0, -3, 0, -1, -1, 0, -3, -2, 6, 2, -3, -3, 1, 0, -3, -4, -2],
'P': [-1, -3, 0, 0, -4, -2, -2, -3, -1, -3, -2, 2, 7, -1, -1, -1, -1, -2, -4, -3],
'Q': [-1, -3, -3, -3, -3, -3, -1, -3, 1, -2, 0, -3, -1, 5, 2, -1, 0, -2, -2, -1],
'R': [-1, -3, -3, -2, -3, -2, -2, -3, 0, -2, -1, -3, -1, 2, 5, -1, 0, -2, -3, -2],
'S': [1, -1, 0, 0, -2, 0, -2, -3, -1, -2, -2, 1, -1, -1, -1, 4, 1, -2, -3, -2],
'T': [0, -1, -1, -1, -2, -2, -2, -1, 0, -1, -1, 0, -1, 0, 0, 1, 5, 0, -2, -2],
'V': [0, -1, -3, -2, -3, -3, -2, 2, -2, 1, 1, -3, -2, -2, -2, -2, 0, 4, -3, -1],
'W': [-3, -2, -4, -3, 1, -2, 2, -3, -3, -2, -1, -4, -4, -2, -3, -3, -2, -3, 11, 2],
'Y': [-2, -2, -3, -2, 3, -3, 2, -1, -2, -1, -1, -2, -3, -1, -2, -2, -2, -1, 2, 7],
'X': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] # For unknown amino acids
}
def blosum62_padding(seq_list, padding):
feat_list = []
for seq in seq_list:
feat = [blosum62.get(aa, blosum62['X']) for aa in seq] + [[0] * 20] * (padding - len(seq))
feat_list.append(feat)
return np.array(feat_list)
def combined_encoding(seq_list, padding):
one_hot_encoded = one_hot_padding(seq_list, padding)
pos_encoded = position_encoding(padding)
blosum_encoded = blosum62_padding(seq_list, padding)
zscale_encoded = zscale_padding(seq_list, padding)
pos_encoded_expanded = np.expand_dims(pos_encoded, axis=0).repeat(len(seq_list), axis=0)
combined_encoding = np.concatenate(
(one_hot_encoded, pos_encoded_expanded, blosum_encoded, zscale_encoded), axis=2
)
return combined_encoding
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