mutcleaner.cleaners.archstabms_1e10_custom_cleaners#

Functions

add_wild_type_sequences_by_library(dataset, ...)

Add wild-type sequences based on library identifiers.

compute_mutations(dataset[, name_column, ...])

compute the mutations by the wt_seq and mut_seq and generate mutation column

convert_pairwise_couplings_to_ddg(dataset[, ...])

Convert pairwise energetic couplings to double-mutant free-energy changes.
mutcleaner.cleaners.archstabms_1e10_custom_cleaners.add_wild_type_sequences_by_library(dataset, library_sequences, library_column='library', sequence_column='wt_sequence')[source]#

Add wild-type sequences based on library identifiers.

Parameters:

  • dataset (DataFrame) – Input dataset.

  • library_sequences (Dict[int, str]) – Mapping from library identifiers to wild-type sequences.

  • library_column (str) – Column containing library identifiers.

  • sequence_column (str) – Output column for wild-type sequences.

Return type:

DataFrame

Returns:

Dataset with the wild-type sequence column added.

Raises:

ValueError – If any library identifier has no corresponding wild-type sequence.

mutcleaner.cleaners.archstabms_1e10_custom_cleaners.compute_mutations(dataset, name_column='name', WT_column='WT', mut_seq='mut_seq')[source]#

compute the mutations by the wt_seq and mut_seq and generate mutation column

Parameters:

  • dataset (DataFrame)

  • name_column (str) – Grouping key column.

  • WT_column (str) – column used to check whether wt or mut

  • mut_seq (str) – Column containing the amino-acid sequence for that row (treated as the mutated sequence for mutants and the WT sequence for the WT row).

Return type:

DataFrame

mutcleaner.cleaners.archstabms_1e10_custom_cleaners.convert_pairwise_couplings_to_ddg(dataset, group_columns=None, mutation_column='mut_info', label_column='label', mutation_separator=',')[source]#

Convert pairwise energetic couplings to double-mutant free-energy changes.

For a double mutant containing mutations A and B, the total free-energy change relative to the wild type is calculated as:

ΔΔG(A,B) = ΔΔG(A) + ΔΔG(B) + ΔΔΔG(A,B)

Single-mutant labels are left unchanged. Double mutants missing either constituent single-mutant label are moved to the failed dataset.

Parameters:

  • dataset (DataFrame) – Input dataset containing single- and double-mutant energy terms.

  • group_columns (Optional[List[str]]) – Columns identifying the protein and biophysical trait within which single-mutant labels are matched. Defaults to ["name"].

  • mutation_column (str) – Column containing mutation descriptions.

  • label_column (str) – Column containing inferred free-energy terms.

  • mutation_separator (str) – Separator between individual mutations.

Return type:

Tuple[DataFrame, DataFrame]

Returns:

A tuple containing:

  • successful_datasetpd.DataFrame

    Single mutants and double mutants successfully converted to ΔΔG.

  • failed_datasetpd.DataFrame

    Double mutants missing at least one constituent single-mutant label, with an additional error_message column.

Raises:

ValueError – If required columns are missing or a single mutation has multiple label measurements within the same group.

Examples

>>> df = pd.DataFrame({
...     "name": ["proteinA"] * 5,
...     "mut_info": [
...         "A0G",
...         "B1V",
...         "A0G,B1V",
...         "C2D",
...         "A0G,D3E",
...     ],
...     "label": [0.4, -0.1, 0.2, 0.3, 0.5],
... })
>>> successful, failed = convert_pairwise_couplings_to_ddg(df)
>>> successful["label"].tolist()
[0.4, -0.1, 0.5, 0.3]
>>> failed["mut_info"].tolist()
['A0G,D3E']