input and name_screen

input_seq.txt

+EVMLVESGGGLVMPGGSLKLSCAASAMSWVRQIPEKRLEWVATITYFPDSVKGRFTISRDNAKNTLYLRMSSLRSEDTAMYYCVRWGQGTTLTVSS
+GFTFSNY
+SIGGHF
+HEGYGRPYFDY
\ No newline at end of file

name_screen.py

+import pandas as pd
+import sys
+sys.path.append('.')
+
+def screen(specie_txt):
+    with open(specie_txt, "r") as f:
+        tar_specie = f.read()
+    csv_file = "./heavy_safe85.csv"
+    csv_data = pd.read_csv(csv_file, low_memory=False)
+    csv_df = pd.DataFrame(csv_data)
+    # 筛选物种
+    df1 = csv_df[csv_df["species"].map(lambda x: x == tar_specie)]
+    startnum = df1["Startnum"].tolist()
+    specie = df1["species"].tolist()
+    # 获取物种对应FR区片段  list中每个FR序列是str
+    seq_ori = (df1['myFR1'] + df1['myFR2'] + df1['myFR3'] + df1['myFR4']).tolist()
+    seq = []
+    # 先补齐FR1的第一个氨基酸
+    for i in range(len(seq_ori)):
+        aa = seq_ori[i]
+        if startnum[i] == 'H2':
+            aa = '#' + seq_ori[i]
+        seq.append(aa)
+    # 创建新dataframe，每列对应一个氨基酸
+    df = pd.DataFrame()
+    j = 0
+    for i in range(25):
+        a_1 = [aa[j] for aa in seq]
+        j = j + 1
+        df[f'X_{i + 1}'] = a_1
+    for i in range(32, 51):
+        a_1 = [aa[j] for aa in seq]
+        j = j + 1
+        df[f'X_{i + 1}'] = a_1
+    for i in range(56, 82):
+        a_1 = [aa[j] for aa in seq]
+        j = j + 1
+        df[f'X_{i + 1}'] = a_1
+    a_1 = [aa[j] for aa in seq]
+    df[f'X_82A'] = a_1
+    j = j + 1
+    a_1 = [aa[j] for aa in seq]
+    df[f'X_82B'] = a_1
+    j = j + 1
+    a_1 = [aa[j] for aa in seq]
+    df[f'X_82C'] = a_1
+    j = j + 1
+    for i in range(82, 94):
+        a_1 = [aa[j] for aa in seq]
+        j = j + 1
+        df[f'X_{i + 1}'] = a_1
+    for i in range(102, 113):
+        a_1 = [aa[j] for aa in seq]
+        j = j + 1
+        df[f'X_{i + 1}'] = a_1
+    df['specie'] = specie
+    outputpath = specie[0] + '.csv'
+    df.to_csv(outputpath, sep=',', index=False, header=True)
+
+
+if __name__ == "__main__":
+    specie_txt = './specie_target.txt'
+    screen(specie_txt)
+
+
+
+