Humanizing sequences with AntPack

AntPack can also suggest mutations to “humanize” a sequence or make it more human. You can manually humanize a sequence by 1) scoring it using AntPack, 2) retrieving the closest clusters (see the “Generating new human sequences” page), 3) determining which regions of the sequence are least human, 4) mutating these and 5) rescoring the sequence. This can be however a little tedious. AntPack offers an easy way to automatically choose mutations that lie at a selected location along that tradeoff curve.

Regardless of which approach you take, we suggest you carefully review suggested mutations and re-score the altered sequence to be sure that the changes that have been made are compatible with your objectives.

To automatically humanize a sequence, start by creating a humanization tool::

from antpack import HumanizationTool
h_tool = HumanizationTool()

Next, feed it the sequence you’d like to humanize using the following function:

class antpack.HumanizationTool

Tool for humanizing sequences.

suggest_mutations(seq: str, excluded_positions: list = [], s_thresh: float = 1.25)

Takes an input sequence, scores it per position, uses the nclusters closest clusters to determine which modification would be most likely to have an impact, suggest mutations and report both the mutations and the new score. CDRs are excluded, together with user-specified excluded positions.

Parameters:

  • seq (str) – The sequence to update.

  • s_thresh (float) – The maximum percentage by which the score can shift before backmutation stops. Smaller values (closer to 1) will prioritize increasing the score over preserving the original sequence. Larger values will prioritize preserving the original sequence.

  • excluded_positions (list) – A list of strings (IMGT position numbers) indicating positions which should not be changed. This enables the user to mask key residues, Vernier zones etc if so desired.

Returns:

  • initial_score (float) – The score of the sequence pre-modification.

  • final_scores (float) – The scores of the sequence after each mutation is adopted (in sequential order).

  • mutations (list) – The suggested mutations in AA_position_newAA format, where position is the IMGT number for the mutation position. These are in sequential order (the same as final_scores).

  • updated_seq (list) – The updated sequences after each mutation with all gaps removed. (This may be a different length from the input sequence if the suggested mutation is a deletion or an insertion).

The “knob” you can turn here is s_thresh. A value <= 1 means that AntPack will basically do a straight in silico CDR graft. This is straightforward but may lose affinity. A value > 1 means that AntPack will 1) score the straight in silico CDR graft of your sequence, which will generally achieve a very high humanness score (the “optimal score”), then 2) revert as many positions as possible to the original sequence without causing the score to go over s_thresh * optimal score. Larger values of s_thresh cause more of the original sequence to be preserved at the expense of a smaller improvement in humanness.

Kabat-defined CDRs are excluded from humanization. If you want to exclude additional key positions, you can do this by passing a list of IMGT-numbered positions you would like to be excluded from consideration.