SeqMut.mutate

SeqMut.mutate(df_seq=None, mutations=None, df_feat=None, jmd_n_len=10, jmd_c_len=10)[source]

Apply specific point mutations to sequences and (optionally) measure their ΔCPP.

Each row of mutations edits one residue of its entry’s sequence; the mutated sequence and a human-readable mutation label are always returned, and the feature-space change is added when a df_feat is supplied.

Parameters:

  • df_seq (pd.DataFrame, shape (n_samples, n_seq_info)) – DataFrame containing an entry column with unique protein identifiers, in the position-based format (sequence, tmd_start, tmd_stop). See SequenceFeature.get_df_parts() for the full df_seq format specification.

  • mutations (pd.DataFrame, shape (n_mutations, >=3)) – Tidy mutation table with columns entry, pos (1-based position in the full sequence), and to_aa (target amino acid). from_aa is derived and checked.

  • df_feat (pd.DataFrame, optional) – CPP feature set (output of CPP.run()). If given, the per-mutation ΔCPP (delta_cpp) and shift_score toward the test-class profile are added.

  • jmd_n_len (int, default=10) – Length of JMD-N in number of amino acids.

  • jmd_c_len (int, default=10) – Length of JMD-C in number of amino acids.

Returns:

df_mut – The mutations table augmented with from_aa, mutation ("<from><pos><to>"), sequence_mut (the mutated sequence), and — when df_feat is given — delta_cpp and shift_score.

Return type:

pd.DataFrame, shape (n_mutations, n_info)

Examples

:meth:SeqMut.mutate applies specific point mutations from a tidy mutations table (entry, pos, to_aa) and, when df_feat is given, reports each mutation’s ΔCPP and its shift toward the test-class profile.

import aaanalysis as aa
aa.options["verbose"] = False

df_seq = aa.load_dataset(name="DOM_GSEC", n=10)
labels = df_seq["label"].to_list()
sf = aa.SequenceFeature()
df_parts = sf.get_df_parts(df_seq=df_seq)
split_kws = sf.get_split_kws()
cpp = aa.CPP(df_parts=df_parts, split_kws=split_kws, verbose=False)
df_feat = cpp.run(labels=labels, n_filter=25)
import pandas as pd

# A proline scan across the first TMD positions of one protein.
entry = df_seq["entry"].iloc[0]
start = int(df_seq.set_index("entry").loc[entry, "tmd_start"])
mutations = pd.DataFrame({"entry": entry, "pos": range(start, start + 12), "to_aa": "P"})
seqmut = aa.SeqMut()
aa.display_df(seqmut.mutate(df_seq=df_seq, mutations=mutations, df_feat=df_feat), n_rows=10, show_shape=True)

CPP using the Python kernel fallback — the compiled Cython extension is not available in this install. Output is bit-exact with the Cython path but ~2x slower. Reinstall via pip install --force-reinstall aaanalysis to fetch a prebuilt wheel.
DataFrame shape: (12, 8)
  entry pos to_aa from_aa mutation sequence_mut delta_cpp shift_score
1 Q14802 37 P L L37P MQKVTLGLLVFLAGF...PGETPPLITPGSAQS 0.167000 0.167000
2 Q14802 38 P Q Q38P MQKVTLGLLVFLAGF...PGETPPLITPGSAQS 0.000000 0.000000
3 Q14802 39 P V V39P MQKVTLGLLVFLAGF...PGETPPLITPGSAQS 0.160000 -0.160000
4 Q14802 40 P G G40P MQKVTLGLLVFLAGF...PGETPPLITPGSAQS 0.000000 0.000000
5 Q14802 41 P G G41P MQKVTLGLLVFLAGF...PGETPPLITPGSAQS 0.181000 0.181000
6 Q14802 42 P L L42P MQKVTLGLLVFLAGF...PGETPPLITPGSAQS 0.000000 0.000000
7 Q14802 43 P I I43P MQKVTLGLLVFLAGF...PGETPPLITPGSAQS 0.179750 -0.179750
8 Q14802 44 P C C44P MQKVTLGLLVFLAGF...PGETPPLITPGSAQS 0.248320 0.248320
9 Q14802 45 P A A45P MQKVTLGLLVFLAGF...PGETPPLITPGSAQS 0.000000 0.000000
10 Q14802 46 P G G46P MQKVTLGLLVFLAGF...PGETPPLITPGSAQS 0.000000 0.000000