AAMut.eval

AAMut.eval(df_impact)[source]

Evaluate substitution impact by ranking scales on their mean substitution sensitivity.

Scales with a high mean absolute delta are the physicochemical properties most affected by amino acid substitutions; the ranking points to which scales drive a mutation’s impact.

Parameters:: df_impact (pd.DataFrame) – Substitution-impact table produced by AAMut.run().
Returns:: df_eval – One row per scale with the mean absolute substitution delta (mean_delta_cpp), sorted from most to least sensitive.
Return type:: pd.DataFrame, shape (n_scales, 2)

Examples

:meth:AAMut.eval ranks scales by their mean absolute substitution delta — i.e. which physicochemical properties are most sensitive to amino acid changes.

import aaanalysis as aa
aamut = aa.AAMut()
df_impact = aamut.run(from_aa=["M", "L", "K"], to_aa=["V", "A", "D"])
aa.display_df(aamut.eval(df_impact=df_impact), n_rows=10, show_shape=True)

DataFrame shape: (586, 2)

	scale_id	mean_delta_cpp
1	CHAM830108	0.666667
2	NAKH900104	0.578222
3	NAKH900106	0.574222
4	ARGP820103	0.567111
5	ARGP820102	0.532222
6	RACS820103	0.530667
7	QIAN880126	0.519667
8	FUKS010107	0.514667
9	CHAM830104	0.500000
10	PLIV810101	0.497889