aaanalysis.load_scales

class aaanalysis.load_scales(name='scales', just_aaindex=False, unclassified_out=False, top60_n=None)[source]

Bases:

Load amino acid scales or their classification (AAontology).

The amino acid scales (name='scales_raw') encompass all scales from AAindex ([Kawashima08]) along with two additional data sources. These scales were min-max normalized (name='scales') and organized in a two-level classification called AAontology (name='scales_cat'), as detailed in [Breimann24b]. The first 20 principal components (PCs) of all compressed scales are provided (name='scales_pc') and were used for an in-depth analysis of redundancy-reduced scale subsets obtained by AAclust ([Breimann24a]). The top 60 scale sets from this analysis are available either collectively (name='top60') or individually (top60_n='1-60'), accompanied by their evaluations in the 'top60_eval' dataset.

Added in version 0.1.0.

Parameters:

name (str, default='scales') –
Name of the loaded dataset:
- scales_raw: All amino acid scales.
- scales: Min-max normalized raw scales.
- scales_cat: Two-level classification (AAontology).
- scales_pc: First 20 PCs of compressed scales.
- top60: Selection of 60 best performing scale sets.
- top60_eval: Evaluation of 60 best performing scale sets.
Or Number between 1 and 60 to select the i-th top60 dataset.
just_aaindex (bool, default=False) – If True, returns only scales from AAindex. Relevant only for ‘scales’, ‘scales_raw’, or ‘scales_cat’.
unclassified_out (bool, default=False) – Determines exclusion of unclassified scales. Relevant only for ‘scales’, ‘scales_raw’, or ‘scales_cat’.
top60_n (int or str, optional) – Select the n-th scale set from top60 sets and return it for ‘scales’, ‘scales_raw’, or ‘scales_cat’. Allowed strings are AAclust ids (e.g., ‘AAC01’).

Returns:

df – DataFrame containing the chosen dataset, recommended to be named by their name suffix (df_scales, df_cat).

Return type:

pandas.DataFrame

Notes

df_cat includes the following columns:
- ‘scale_id’: ID of scale (from AAindex or following the same naming convention).
- ‘category’: Category of scale (defined in AAontology).
- ‘subcategory’: Subcategory of scale (AAontology).
- ‘scale_name’: Name of scale derived from scale description.
- ‘scale_description’: Description of scale (derived from AAindex).
Scales under the ‘Others’ category are considered unclassified.

See also

Overview of all loading options: Amino Acid Scale Datasets.
AAontology: Categories and Subcategories tables.
Step-by-step guide in the Scale Loading Tutorial.
AAclust for customizing redundancy-reduced scale sets.

Examples

You can load all scales from AAontology (see [Breimann24b]) and their description using the load_scales() function. The min-max normalized numerical scales are provided by default:

import aaanalysis as aa
df_scales = aa.load_scales()
print(len(df_scales.T), "scales exist in AAontology")
aa.display_df(df_scales, n_cols=5)

586 scales exist in AAontology

	ANDN920101	ARGP820101	ARGP820102	ARGP820103	BEGF750101
AA
A	0.494000	0.230000	0.355000	0.504000	1.000000
C	0.864000	0.404000	0.579000	0.387000	0.000000
D	1.000000	0.174000	0.000000	0.000000	0.404000
E	0.420000	0.177000	0.019000	0.032000	0.713000
F	0.877000	0.762000	0.601000	0.670000	0.574000
G	0.025000	0.026000	0.138000	0.170000	0.309000
H	0.840000	0.230000	0.082000	0.053000	0.574000
I	0.000000	0.838000	0.440000	0.543000	0.713000
K	0.506000	0.434000	0.003000	0.004000	0.574000
L	0.272000	0.577000	1.000000	0.989000	1.000000
M	0.704000	0.445000	0.824000	1.000000	1.000000
N	0.988000	0.023000	0.057000	0.046000	0.309000
P	0.605000	0.736000	0.223000	0.220000	0.000000
Q	0.519000	0.000000	0.211000	0.131000	0.404000
R	0.531000	0.226000	0.047000	0.110000	0.489000
S	0.679000	0.019000	0.289000	0.238000	0.309000
T	0.494000	0.019000	0.248000	0.273000	0.404000
V	0.000000	0.498000	0.324000	0.355000	0.809000
W	0.926000	1.000000	0.226000	0.333000	0.713000
Y	0.802000	0.709000	0.107000	0.191000	0.404000

To retrieve the un-normalized scales, set name='scales_raw':

df_scales = aa.load_scales(name="scales_raw")
aa.display_df(df_scales, n_cols=5, n_rows=5)

	ANDN920101	ARGP820101	ARGP820102	ARGP820103	BEGF750101
AA
A	4.350000	0.610000	1.180000	1.560000	1.000000
C	4.650000	1.070000	1.890000	1.230000	0.060000
D	4.760000	0.460000	0.050000	0.140000	0.440000
E	4.290000	0.470000	0.110000	0.230000	0.730000
F	4.660000	2.020000	1.960000	2.030000	0.600000

The first 20 PCs of all compressed scales can be retrieved by name='scales_pca':

df_pc = aa.load_scales(name="scales_pc")
aa.display_df(df_pc, show_shape=True)

DataFrame shape: (20, 20)

	PC1	PC2	PC3	PC4	PC5	PC6	PC7	PC8	PC9	PC10	PC11	PC12	PC13	PC14	PC15	PC16	PC17	PC18	PC19	PC20
AA
A	0.073190	0.259030	-0.270090	-0.374730	-0.009310	0.159360	-0.090600	0.193800	0.106010	0.165780	0.127210	-0.161270	-0.035360	-0.133400	0.084230	-0.136950	-0.503810	-0.441080	-0.235460	-0.115310
C	0.205620	0.130650	-0.164980	0.302520	0.539970	0.264480	0.488150	0.051980	0.287850	-0.268360	-0.083770	-0.121410	-0.109260	-0.091250	-0.008110	0.108830	-0.001420	0.002640	-0.049160	-0.090410
D	-0.264620	0.245490	-0.055430	0.063120	0.125590	0.255040	-0.203960	-0.491080	-0.010360	-0.073790	0.119410	0.009770	-0.070680	0.337390	0.401750	0.068220	0.266940	-0.350000	-0.008320	0.021170
E	-0.195640	0.341280	0.127870	-0.314750	-0.018020	0.337190	-0.088000	-0.325790	0.083820	-0.160650	0.150770	-0.083790	0.078590	-0.304560	-0.185970	0.050030	-0.122970	0.526190	0.093960	-0.049910
F	0.348890	0.125190	0.034520	0.118960	-0.091320	0.009140	-0.197540	-0.011910	-0.212210	0.005670	0.030470	0.057490	-0.231740	-0.003790	0.022030	0.772250	-0.266000	-0.022190	0.162590	0.046080
G	-0.145180	0.043160	-0.570540	0.098060	0.142450	-0.225710	-0.419920	0.279690	0.152640	-0.258630	0.331190	0.123200	0.159960	-0.040240	-0.082410	0.126100	0.172820	0.143800	-0.021170	-0.013710
H	0.011380	0.269740	0.159750	0.175360	0.140680	0.039330	-0.029280	0.243770	-0.640950	-0.052370	0.149440	-0.454060	0.277910	-0.122510	-0.045280	-0.071300	0.183940	-0.094790	0.008380	-0.069890
I	0.374000	0.121890	-0.126860	-0.065250	-0.155980	-0.140290	0.056690	-0.245830	-0.074940	-0.230000	-0.066920	0.126430	0.278090	0.317410	-0.136170	-0.116940	-0.048480	-0.011570	0.106600	-0.643530
K	-0.202260	0.363130	0.183790	-0.111210	-0.171380	-0.214410	0.039890	0.320010	0.256790	-0.247370	-0.467600	-0.097600	0.259350	0.079820	0.361420	0.200120	0.001610	0.031450	0.022120	0.025380
L	0.329240	0.177160	-0.139820	-0.286860	-0.194360	0.007340	-0.111360	0.074030	0.021080	-0.084450	-0.184180	-0.240430	-0.529920	-0.014800	-0.095810	-0.092210	0.533010	0.065930	-0.124290	0.027420
M	0.289160	0.251510	0.091470	-0.045450	0.115730	0.289220	-0.027170	0.324950	-0.160960	0.102270	0.098870	0.568290	0.015620	0.155680	0.312940	-0.242760	0.031370	0.270270	-0.003700	0.121800
N	-0.204120	0.231170	-0.097670	0.214020	0.165010	-0.073740	-0.200140	-0.064790	-0.228230	-0.089820	-0.412980	0.046050	-0.217960	0.321250	-0.358480	-0.182220	-0.346170	0.128620	-0.247410	0.172590
P	-0.166120	0.037590	-0.276880	0.384140	-0.691610	0.405000	0.245850	0.129850	-0.038030	-0.107960	0.076900	-0.010790	-0.025870	0.046690	-0.030560	-0.027880	-0.038210	0.019500	-0.022540	0.032680
Q	-0.119850	0.334480	0.116780	0.002460	-0.024550	0.011320	0.091330	0.022790	0.054670	0.106180	-0.094670	0.476790	0.092050	-0.269600	-0.491950	0.112910	0.272950	-0.434320	0.026510	0.014460
R	-0.146320	0.295020	0.283520	0.027240	-0.055660	-0.400980	0.290780	0.098030	0.116960	-0.054230	0.562180	-0.022100	-0.359900	0.268070	-0.071670	-0.043500	-0.086680	0.028750	0.001300	-0.049780
S	-0.134520	0.220350	-0.315510	0.084300	0.067610	-0.126640	0.060490	-0.000750	-0.029870	0.390700	-0.158630	-0.068780	-0.175300	-0.085460	0.095610	-0.162100	-0.032190	0.065770	0.729960	-0.085650
T	-0.024920	0.194840	-0.219810	0.062620	-0.014410	-0.171330	0.220850	-0.164790	-0.032160	0.593290	-0.017750	-0.030370	0.163560	0.008680	0.093110	0.238680	0.145260	0.287260	-0.489980	-0.115160
V	0.310250	0.155950	-0.230960	-0.132630	-0.074050	-0.208070	0.242330	-0.272770	-0.034070	-0.123100	0.093210	-0.064920	0.304670	0.058230	-0.044470	-0.078980	-0.046460	-0.060360	0.107190	0.689670
W	0.287410	0.142550	0.249530	0.355490	-0.053760	0.136800	-0.371840	0.031690	0.498030	0.300260	0.064360	-0.257830	0.200040	0.189230	-0.198120	-0.114360	0.021200	0.008150	0.065530	0.076070
Y	0.160400	0.150020	0.081790	0.400500	-0.128640	-0.293600	-0.164880	-0.258810	0.019610	-0.154980	-0.015850	0.121660	-0.130260	-0.567950	0.309210	-0.269350	-0.099280	-0.021080	-0.166870	-0.049600

The AAontolgy two-level classification can be retrieved by name='scales_cat'

df_cat = aa.load_scales(name="scales_cat")
aa.display_df(df_cat, n_rows=50, show_shape=True)

DataFrame shape: (586, 5)

	scale_id	category	subcategory	scale_name	scale_description
1	LINS030110	ASA/Volume	Accessible surface area (ASA)	ASA (folded coil/turn)	Total median ac...s et al., 2003)
2	LINS030113	ASA/Volume	Accessible surface area (ASA)	ASA (folded coil/turn)	% total accessi...s et al., 2003)
3	JANJ780101	ASA/Volume	Accessible surface area (ASA)	ASA (folded protein)	Average accessi...n et al., 1978)
4	JANJ780103	ASA/Volume	Accessible surface area (ASA)	ASA (folded protein)	Percentage of e...n et al., 1978)
5	LINS030104	ASA/Volume	Accessible surface area (ASA)	ASA (folded protein)	Total median ac...s et al., 2003)
6	LINS030107	ASA/Volume	Accessible surface area (ASA)	ASA (folded protein)	% total accessi...s et al., 2003)
7	CHOC760102	ASA/Volume	Accessible surface area (ASA)	ASA (folded proteins)	Residue accessi...(Chothia, 1976)
8	LINS030116	ASA/Volume	Accessible surface area (ASA)	ASA (folded β-strand)	Total median ac...s et al., 2003)
9	LINS030119	ASA/Volume	Accessible surface area (ASA)	ASA (folded β-strand)	% total accessi...s et al., 2003)
10	LINS030103	ASA/Volume	Accessible surface area (ASA)	Hydrophilic ASA	Hydrophilic acc...s et al., 2003)
11	LINS030112	ASA/Volume	Accessible surface area (ASA)	Hydrophilic ASA...lded coil/turn)	Hydrophilic med...s et al., 2003)
12	LINS030115	ASA/Volume	Accessible surface area (ASA)	Hydrophilic ASA...lded coil/turn)	% Hydrophilic a...s et al., 2003)
13	LINS030106	ASA/Volume	Accessible surface area (ASA)	Hydrophilic ASA...olded proteins)	Hydrophilic med...s et al., 2003)
14	LINS030109	ASA/Volume	Accessible surface area (ASA)	Hydrophilic ASA...olded proteins)	% Hydrophilic a...s et al., 2003)
15	LINS030118	ASA/Volume	Accessible surface area (ASA)	Hydrophilic ASA...olded β-strand)	Hydrophilic med...s et al., 2003)
16	LINS030121	ASA/Volume	Accessible surface area (ASA)	Hydrophilic ASA...olded β-strand)	% Hydrophilic a...s et al., 2003)
17	LINS030114	ASA/Volume	Accessible surface area (ASA)	Hydrophobic ASA...lded coil/turn)	% Hydrophobic a...s et al., 2003)
18	LINS030108	ASA/Volume	Accessible surface area (ASA)	Hydrophobic ASA...folded protein)	% Hydrophobic a...s et al., 2003)
19	LINS030120	ASA/Volume	Accessible surface area (ASA)	Hydrophobic ASA...olded β-strand)	% Hydrophobic a...s et al., 2003)
20	GUYH850104	ASA/Volume	Accessible surface area (ASA)	Partition energy	Apparent partit...dex (Guy, 1985)
21	GUYH850105	ASA/Volume	Accessible surface area (ASA)	Partition energy	Apparent partit...dex (Guy, 1985)
22	RACS770103	ASA/Volume	Accessible surface area (ASA)	Side chain orientation	Side chain orie...Scheraga, 1977)
23	VHEG790101	ASA/Volume	Accessible surface area (ASA)	TFE to lipophilic phase	Transfer free e...Blomberg, 1979)
24	BIOV880101	ASA/Volume	Buried	Buriability	Information val...u et al., 1988)
25	BIOV880102	ASA/Volume	Buried	Buriability	Information val...u et al., 1988)
26	WERD780101	ASA/Volume	Buried	Buriability	Propensity to b...Scheraga, 1978)
27	ZHOH040103	ASA/Volume	Buried	Buriability	Buriability (Zhou-Zhou, 2004)
28	ARGP820103	ASA/Volume	Buried	Buried	Membrane-buried...s et al., 1982)
29	CHOC760103	ASA/Volume	Buried	Buried	Proportion of r...(Chothia, 1976)
30	CHOC760104	ASA/Volume	Buried	Buried	Proportion of r...(Chothia, 1976)
31	JANJ780102	ASA/Volume	Buried	Buried	Percentage of b...n et al., 1978)
32	JANJ790101	ASA/Volume	Buried	Buried	Ratio of buried...s (Janin, 1979)
33	OLSK800101	ASA/Volume	Buried	Buried	Average interna...s (Olsen, 1980)
34	NISK800101	ASA/Volume	Buried	Interactivity	8 A contact num...kawa-Ooi, 1980)
35	WARP780101	ASA/Volume	Buried	Interactivity	Average interac...e-Morgan, 1978)
36	LINS030111	ASA/Volume	Hydrophobic ASA	Hydrophobic ASA...lded coil/turn)	Hydrophobic med...s et al., 2003)
37	LINS030105	ASA/Volume	Hydrophobic ASA	Hydrophobic ASA...olded proteins)	Hydrophobic med...s et al., 2003)
38	LINS030117	ASA/Volume	Hydrophobic ASA	Hydrophobic ASA...olded β-strand)	Hydrophobic med...s et al., 2003)
39	PONP800107	ASA/Volume	Partial specific volume	Accessibility reduction ratio	Accessibility r...y et al., 1980)
40	ZIMJ680102	ASA/Volume	Partial specific volume	Bulkiness	Bulkiness (Zimm...n et al., 1968)
41	LINS030102	ASA/Volume	Partial specific volume	Hydrophobic ASA	Hydrophobic acc...s et al., 2003)
42	BASU050101	ASA/Volume	Partial specific volume	Interactivity	Interactivity s...a et al., 2005)
43	BASU050103	ASA/Volume	Partial specific volume	Interactivity	Interactivity s...a et al., 2005)
44	NISK860101	ASA/Volume	Partial specific volume	Interactivity	14 A contact nu...kawa-Ooi, 1986)
45	BULH740102	ASA/Volume	Partial specific volume	Partial specific volume	Apparent partia...l-Breese, 1974)
46	COHE430101	ASA/Volume	Partial specific volume	Partial specific volume	Partial specifi...n-Edsall, 1943)
47	PLIV810101	ASA/Volume	Partial specific volume	Partition coefficient	Partition Coeff...a et al., 1981)
48	CHOC760101	ASA/Volume	Volume	Accessible surface area (ASA)	Residue accessi...(Chothia, 1976)
49	LINS030101	ASA/Volume	Volume	Accessible surface area (ASA)	Total accessibl...s et al., 2003)
50	RADA880106	ASA/Volume	Volume	Accessible surface area (ASA)	Accessible surf...olfenden, 1988)

Additionally, we tested a plethora of scale sets (see [Breimann24a]) across 12 different protein benchmarking datasets—six at the sequence level and six at the amino acid level, each with three amino acid windows. This resulted in 24 evaluations, for which we identified the top 60 scale sets, referred to as ‘top60’. These can be accessed by setting name='top60':

df_top60 = aa.load_scales(name="top60")
aa.display_df(df_top60, n_cols=5, show_shape=True)

DataFrame shape: (60, 586)

	ANDN920101	ARGP820101	ARGP820102	ARGP820103	BEGF750101
top60_id
AAC01	0	0	0	1	0
AAC02	1	0	0	1	0
AAC03	1	0	0	1	0
AAC04	1	0	0	1	0
AAC05	1	0	0	1	0
AAC06	1	0	0	1	0
AAC07	0	0	0	1	0
AAC08	1	0	0	1	0
AAC09	1	0	0	1	0
AAC10	1	0	0	1	0
AAC11	1	0	0	1	0
AAC12	1	0	0	1	0
AAC13	1	0	0	1	0
AAC14	0	0	0	1	0
AAC15	1	1	0	1	0
AAC16	1	0	1	0	0
AAC17	0	0	0	0	0
AAC18	1	0	1	0	0
AAC19	1	1	0	1	1
AAC20	0	0	0	0	0
AAC21	1	0	0	1	0
AAC22	1	1	0	0	0
AAC23	1	0	0	0	0
AAC24	1	1	0	1	1
AAC25	0	0	0	0	0
AAC26	0	0	0	0	0
AAC27	1	0	1	0	0
AAC28	0	0	0	0	0
AAC29	1	1	1	1	1
AAC30	1	1	1	1	1
AAC31	1	0	0	0	0
AAC32	1	1	0	1	1
AAC33	0	0	0	0	0
AAC34	1	0	0	0	0
AAC35	1	0	0	0	0
AAC36	1	0	0	0	0
AAC37	1	0	0	0	0
AAC38	1	0	0	0	0
AAC39	1	0	0	1	0
AAC40	1	0	1	0	0
AAC41	0	0	0	0	0
AAC42	1	1	0	0	0
AAC43	1	0	0	0	0
AAC44	1	0	0	1	0
AAC45	1	0	0	0	0
AAC46	0	0	0	0	0
AAC47	0	0	0	0	0
AAC48	0	0	0	0	0
AAC49	0	0	0	1	0
AAC50	0	1	0	0	0
AAC51	0	0	0	0	0
AAC52	0	0	0	0	0
AAC53	0	0	0	0	0
AAC54	0	0	0	0	0
AAC55	0	0	0	0	0
AAC56	0	0	0	0	0
AAC57	0	0	0	0	0
AAC58	0	0	0	0	0
AAC59	0	0	0	0	0
AAC60	0	0	0	0	0

To obtain their evaluation, set name='top60_eval':

df_top60_eval = aa.load_scales(name="top60_eval")
aa.display_df(df_top60_eval, show_shape=True)

DataFrame shape: (60, 25)

	n_scales	SEQ_AMYLO	SEQ_CAPSID	SEQ_DISULFIDE	SEQ_LOCATION	SEQ_SOLUBLE	SEQ_TAIL	AA5_CASPASE3	AA5_FURIN	AA5_LDR	AA5_MMP2	AA5_RNABIND	AA5_SA	AA9_CASPASE3	AA9_FURIN	AA9_LDR	AA9_MMP2	AA9_RNABIND	AA9_SA	AA13_CASPASE3	AA13_FURIN	AA13_LDR	AA13_MMP2	AA13_RNABIND	AA13_SA
top60_id
AAC01	183	0.761000	0.827000	0.732000	0.746000	0.646000	0.884000	0.862000	0.901000	0.612000	0.680000	0.652000	0.642000	0.816000	0.916000	0.644000	0.703000	0.659000	0.664000	0.790000	0.918000	0.694000	0.681000	0.652000	0.615000
AAC02	170	0.747000	0.830000	0.733000	0.742000	0.653000	0.886000	0.855000	0.907000	0.608000	0.688000	0.660000	0.640000	0.819000	0.915000	0.642000	0.706000	0.657000	0.671000	0.792000	0.916000	0.690000	0.676000	0.656000	0.608000
AAC03	137	0.741000	0.829000	0.734000	0.746000	0.648000	0.884000	0.857000	0.904000	0.601000	0.685000	0.661000	0.640000	0.818000	0.917000	0.636000	0.710000	0.659000	0.670000	0.791000	0.914000	0.695000	0.684000	0.656000	0.613000
AAC04	144	0.747000	0.828000	0.731000	0.747000	0.654000	0.885000	0.859000	0.906000	0.605000	0.686000	0.657000	0.639000	0.822000	0.913000	0.640000	0.714000	0.654000	0.664000	0.790000	0.915000	0.689000	0.680000	0.656000	0.610000
AAC05	138	0.739000	0.830000	0.735000	0.752000	0.646000	0.888000	0.859000	0.906000	0.601000	0.684000	0.655000	0.638000	0.823000	0.916000	0.640000	0.713000	0.658000	0.671000	0.790000	0.918000	0.689000	0.682000	0.649000	0.607000
AAC06	139	0.743000	0.827000	0.736000	0.746000	0.652000	0.883000	0.857000	0.906000	0.608000	0.684000	0.657000	0.640000	0.821000	0.914000	0.642000	0.709000	0.659000	0.665000	0.789000	0.915000	0.691000	0.680000	0.653000	0.611000
AAC07	121	0.742000	0.833000	0.736000	0.747000	0.650000	0.882000	0.858000	0.901000	0.606000	0.688000	0.655000	0.638000	0.820000	0.915000	0.638000	0.711000	0.661000	0.671000	0.789000	0.914000	0.689000	0.682000	0.655000	0.606000
AAC08	142	0.743000	0.831000	0.733000	0.746000	0.650000	0.884000	0.858000	0.903000	0.603000	0.687000	0.657000	0.640000	0.819000	0.916000	0.640000	0.710000	0.658000	0.669000	0.787000	0.916000	0.689000	0.681000	0.654000	0.608000
AAC09	263	0.753000	0.826000	0.736000	0.747000	0.647000	0.882000	0.858000	0.905000	0.608000	0.684000	0.655000	0.648000	0.820000	0.918000	0.642000	0.703000	0.653000	0.664000	0.787000	0.915000	0.690000	0.679000	0.647000	0.614000
AAC10	152	0.750000	0.828000	0.734000	0.748000	0.646000	0.886000	0.860000	0.908000	0.602000	0.684000	0.656000	0.645000	0.819000	0.913000	0.632000	0.711000	0.655000	0.670000	0.787000	0.913000	0.689000	0.676000	0.653000	0.610000
AAC11	150	0.749000	0.832000	0.732000	0.751000	0.647000	0.883000	0.860000	0.904000	0.605000	0.695000	0.650000	0.637000	0.815000	0.919000	0.638000	0.707000	0.654000	0.665000	0.781000	0.913000	0.689000	0.683000	0.652000	0.610000
AAC12	164	0.750000	0.828000	0.739000	0.746000	0.646000	0.889000	0.859000	0.904000	0.608000	0.688000	0.649000	0.637000	0.818000	0.913000	0.638000	0.708000	0.658000	0.666000	0.785000	0.917000	0.688000	0.676000	0.648000	0.613000
AAC13	125	0.744000	0.831000	0.738000	0.749000	0.650000	0.882000	0.856000	0.903000	0.600000	0.682000	0.653000	0.632000	0.818000	0.916000	0.634000	0.716000	0.657000	0.670000	0.790000	0.915000	0.688000	0.680000	0.654000	0.612000
AAC14	108	0.743000	0.831000	0.741000	0.744000	0.645000	0.884000	0.860000	0.902000	0.610000	0.673000	0.655000	0.634000	0.820000	0.914000	0.645000	0.710000	0.654000	0.666000	0.794000	0.914000	0.689000	0.678000	0.655000	0.604000
AAC15	183	0.753000	0.827000	0.732000	0.744000	0.646000	0.883000	0.857000	0.903000	0.617000	0.684000	0.659000	0.637000	0.821000	0.914000	0.632000	0.708000	0.654000	0.660000	0.785000	0.915000	0.687000	0.682000	0.649000	0.611000
AAC16	255	0.755000	0.830000	0.732000	0.741000	0.645000	0.884000	0.858000	0.902000	0.611000	0.684000	0.649000	0.648000	0.821000	0.917000	0.635000	0.702000	0.656000	0.666000	0.789000	0.915000	0.682000	0.681000	0.644000	0.611000
AAC17	101	0.740000	0.835000	0.736000	0.744000	0.644000	0.884000	0.858000	0.904000	0.606000	0.680000	0.646000	0.631000	0.817000	0.916000	0.646000	0.705000	0.651000	0.666000	0.793000	0.915000	0.691000	0.686000	0.654000	0.609000
AAC18	154	0.757000	0.826000	0.730000	0.742000	0.643000	0.881000	0.856000	0.899000	0.602000	0.684000	0.657000	0.635000	0.825000	0.922000	0.636000	0.705000	0.662000	0.658000	0.792000	0.912000	0.685000	0.679000	0.654000	0.612000
AAC19	406	0.764000	0.826000	0.730000	0.745000	0.636000	0.887000	0.860000	0.901000	0.605000	0.683000	0.649000	0.635000	0.819000	0.920000	0.633000	0.707000	0.651000	0.669000	0.789000	0.909000	0.687000	0.679000	0.654000	0.610000
AAC20	90	0.740000	0.833000	0.737000	0.742000	0.646000	0.887000	0.857000	0.901000	0.608000	0.677000	0.654000	0.632000	0.819000	0.915000	0.652000	0.702000	0.656000	0.669000	0.789000	0.908000	0.695000	0.675000	0.644000	0.610000
AAC21	202	0.757000	0.828000	0.730000	0.742000	0.637000	0.883000	0.853000	0.909000	0.607000	0.679000	0.657000	0.633000	0.820000	0.918000	0.640000	0.710000	0.648000	0.657000	0.789000	0.916000	0.688000	0.682000	0.651000	0.612000
AAC22	259	0.757000	0.823000	0.733000	0.739000	0.642000	0.880000	0.859000	0.903000	0.599000	0.682000	0.650000	0.638000	0.826000	0.917000	0.635000	0.700000	0.655000	0.665000	0.788000	0.917000	0.686000	0.682000	0.655000	0.611000
AAC23	219	0.756000	0.826000	0.734000	0.745000	0.644000	0.880000	0.858000	0.899000	0.601000	0.693000	0.650000	0.639000	0.823000	0.917000	0.629000	0.700000	0.655000	0.660000	0.783000	0.913000	0.689000	0.682000	0.655000	0.610000
AAC24	305	0.756000	0.824000	0.740000	0.741000	0.641000	0.880000	0.859000	0.903000	0.601000	0.685000	0.652000	0.635000	0.818000	0.919000	0.634000	0.705000	0.655000	0.663000	0.788000	0.914000	0.683000	0.677000	0.659000	0.607000
AAC25	96	0.764000	0.831000	0.732000	0.744000	0.639000	0.879000	0.862000	0.905000	0.603000	0.678000	0.659000	0.633000	0.816000	0.917000	0.636000	0.707000	0.646000	0.663000	0.790000	0.916000	0.678000	0.676000	0.656000	0.605000
AAC26	96	0.741000	0.828000	0.734000	0.745000	0.642000	0.887000	0.855000	0.904000	0.602000	0.680000	0.655000	0.634000	0.816000	0.914000	0.639000	0.705000	0.650000	0.671000	0.792000	0.914000	0.698000	0.681000	0.646000	0.601000
AAC27	127	0.753000	0.832000	0.731000	0.744000	0.640000	0.880000	0.854000	0.901000	0.607000	0.680000	0.658000	0.638000	0.820000	0.912000	0.638000	0.700000	0.643000	0.663000	0.793000	0.914000	0.683000	0.681000	0.658000	0.611000
AAC28	58	0.737000	0.821000	0.736000	0.745000	0.654000	0.883000	0.858000	0.898000	0.600000	0.675000	0.652000	0.641000	0.822000	0.910000	0.642000	0.703000	0.654000	0.670000	0.789000	0.914000	0.691000	0.679000	0.655000	0.605000
AAC29	514	0.763000	0.824000	0.731000	0.746000	0.636000	0.880000	0.857000	0.900000	0.602000	0.675000	0.646000	0.635000	0.827000	0.914000	0.632000	0.705000	0.662000	0.666000	0.796000	0.908000	0.692000	0.679000	0.647000	0.610000
AAC30	573	0.763000	0.821000	0.732000	0.746000	0.644000	0.879000	0.854000	0.898000	0.606000	0.680000	0.655000	0.632000	0.818000	0.915000	0.633000	0.701000	0.659000	0.656000	0.793000	0.911000	0.682000	0.675000	0.664000	0.616000
AAC31	92	0.757000	0.821000	0.727000	0.747000	0.631000	0.879000	0.858000	0.900000	0.601000	0.685000	0.653000	0.641000	0.819000	0.918000	0.644000	0.711000	0.654000	0.662000	0.784000	0.912000	0.686000	0.678000	0.653000	0.611000
AAC32	431	0.761000	0.827000	0.736000	0.745000	0.637000	0.880000	0.857000	0.898000	0.602000	0.675000	0.651000	0.638000	0.818000	0.917000	0.634000	0.701000	0.670000	0.658000	0.792000	0.911000	0.678000	0.682000	0.650000	0.613000
AAC33	155	0.762000	0.817000	0.732000	0.739000	0.645000	0.877000	0.860000	0.902000	0.606000	0.678000	0.654000	0.631000	0.822000	0.916000	0.635000	0.700000	0.651000	0.661000	0.785000	0.922000	0.688000	0.683000	0.655000	0.608000
AAC34	49	0.755000	0.823000	0.729000	0.741000	0.640000	0.884000	0.859000	0.900000	0.602000	0.682000	0.667000	0.624000	0.818000	0.921000	0.634000	0.707000	0.645000	0.664000	0.787000	0.915000	0.688000	0.685000	0.649000	0.608000
AAC35	91	0.758000	0.822000	0.727000	0.741000	0.639000	0.875000	0.856000	0.904000	0.605000	0.679000	0.656000	0.632000	0.821000	0.912000	0.632000	0.702000	0.658000	0.658000	0.791000	0.914000	0.683000	0.678000	0.663000	0.618000
AAC36	103	0.759000	0.824000	0.734000	0.742000	0.642000	0.874000	0.864000	0.902000	0.600000	0.680000	0.657000	0.630000	0.817000	0.918000	0.634000	0.707000	0.651000	0.664000	0.787000	0.915000	0.688000	0.677000	0.646000	0.609000
AAC37	69	0.748000	0.823000	0.732000	0.738000	0.633000	0.882000	0.858000	0.899000	0.596000	0.684000	0.653000	0.635000	0.816000	0.919000	0.647000	0.706000	0.647000	0.655000	0.789000	0.916000	0.693000	0.682000	0.646000	0.606000
AAC38	46	0.766000	0.809000	0.731000	0.744000	0.620000	0.857000	0.857000	0.898000	0.609000	0.675000	0.650000	0.642000	0.815000	0.916000	0.650000	0.701000	0.661000	0.664000	0.783000	0.921000	0.685000	0.679000	0.652000	0.618000
AAC39	114	0.755000	0.823000	0.731000	0.744000	0.653000	0.874000	0.856000	0.898000	0.600000	0.682000	0.655000	0.636000	0.820000	0.913000	0.634000	0.698000	0.643000	0.660000	0.786000	0.913000	0.687000	0.676000	0.650000	0.615000
AAC40	50	0.756000	0.819000	0.738000	0.742000	0.629000	0.870000	0.851000	0.898000	0.602000	0.685000	0.668000	0.643000	0.814000	0.913000	0.640000	0.706000	0.655000	0.659000	0.775000	0.910000	0.682000	0.680000	0.652000	0.611000
AAC41	51	0.751000	0.822000	0.734000	0.740000	0.637000	0.882000	0.854000	0.896000	0.600000	0.684000	0.655000	0.644000	0.805000	0.914000	0.636000	0.695000	0.653000	0.661000	0.779000	0.912000	0.685000	0.691000	0.653000	0.607000
AAC42	68	0.761000	0.811000	0.731000	0.742000	0.635000	0.868000	0.861000	0.895000	0.604000	0.676000	0.648000	0.633000	0.816000	0.916000	0.636000	0.705000	0.657000	0.662000	0.799000	0.916000	0.681000	0.674000	0.646000	0.612000
AAC43	55	0.767000	0.815000	0.731000	0.740000	0.631000	0.871000	0.848000	0.899000	0.603000	0.681000	0.649000	0.639000	0.807000	0.913000	0.632000	0.701000	0.655000	0.661000	0.782000	0.914000	0.681000	0.676000	0.646000	0.625000
AAC44	65	0.758000	0.825000	0.734000	0.741000	0.621000	0.875000	0.855000	0.902000	0.588000	0.678000	0.657000	0.633000	0.810000	0.913000	0.635000	0.696000	0.658000	0.660000	0.780000	0.923000	0.682000	0.677000	0.653000	0.611000
AAC45	56	0.760000	0.823000	0.733000	0.741000	0.629000	0.873000	0.844000	0.893000	0.610000	0.681000	0.654000	0.635000	0.809000	0.907000	0.632000	0.702000	0.646000	0.672000	0.774000	0.914000	0.684000	0.681000	0.648000	0.612000
AAC46	54	0.759000	0.812000	0.733000	0.739000	0.626000	0.872000	0.843000	0.896000	0.599000	0.679000	0.644000	0.637000	0.810000	0.914000	0.631000	0.696000	0.667000	0.664000	0.779000	0.912000	0.681000	0.676000	0.650000	0.623000
AAC47	31	0.748000	0.817000	0.717000	0.738000	0.624000	0.882000	0.855000	0.896000	0.602000	0.675000	0.648000	0.627000	0.819000	0.910000	0.636000	0.694000	0.652000	0.653000	0.800000	0.916000	0.684000	0.672000	0.649000	0.605000
AAC48	30	0.771000	0.803000	0.723000	0.737000	0.626000	0.857000	0.848000	0.891000	0.603000	0.673000	0.657000	0.631000	0.813000	0.912000	0.638000	0.695000	0.662000	0.658000	0.776000	0.910000	0.674000	0.679000	0.648000	0.619000
AAC49	27	0.758000	0.807000	0.731000	0.751000	0.630000	0.850000	0.853000	0.892000	0.590000	0.675000	0.651000	0.633000	0.815000	0.914000	0.631000	0.696000	0.647000	0.660000	0.784000	0.904000	0.679000	0.677000	0.646000	0.615000
AAC50	32	0.762000	0.802000	0.728000	0.734000	0.620000	0.855000	0.831000	0.903000	0.607000	0.671000	0.645000	0.640000	0.800000	0.910000	0.641000	0.692000	0.650000	0.671000	0.773000	0.908000	0.687000	0.678000	0.649000	0.623000
AAC51	20	0.768000	0.802000	0.727000	0.737000	0.610000	0.865000	0.836000	0.894000	0.605000	0.668000	0.641000	0.632000	0.802000	0.902000	0.626000	0.697000	0.652000	0.660000	0.767000	0.903000	0.680000	0.691000	0.653000	0.614000
AAC52	14	0.755000	0.802000	0.729000	0.740000	0.613000	0.867000	0.814000	0.886000	0.601000	0.672000	0.644000	0.632000	0.796000	0.901000	0.631000	0.691000	0.650000	0.668000	0.755000	0.901000	0.683000	0.667000	0.650000	0.633000
AAC53	21	0.765000	0.798000	0.729000	0.732000	0.629000	0.862000	0.782000	0.885000	0.614000	0.661000	0.647000	0.638000	0.759000	0.905000	0.630000	0.681000	0.640000	0.658000	0.747000	0.899000	0.674000	0.671000	0.643000	0.616000
AAC54	11	0.743000	0.780000	0.699000	0.714000	0.619000	0.844000	0.840000	0.895000	0.587000	0.669000	0.622000	0.624000	0.806000	0.926000	0.628000	0.671000	0.620000	0.656000	0.770000	0.911000	0.685000	0.651000	0.623000	0.620000
AAC55	8	0.747000	0.769000	0.710000	0.726000	0.580000	0.835000	0.732000	0.832000	0.595000	0.640000	0.590000	0.630000	0.745000	0.866000	0.630000	0.667000	0.618000	0.680000	0.723000	0.843000	0.656000	0.637000	0.604000	0.620000
AAC56	4	0.782000	0.738000	0.687000	0.710000	0.570000	0.768000	0.685000	0.857000	0.596000	0.610000	0.584000	0.636000	0.687000	0.886000	0.625000	0.651000	0.606000	0.668000	0.691000	0.859000	0.670000	0.602000	0.601000	0.617000
AAC57	5	0.768000	0.734000	0.684000	0.713000	0.571000	0.774000	0.663000	0.843000	0.600000	0.613000	0.599000	0.639000	0.689000	0.873000	0.631000	0.633000	0.621000	0.668000	0.677000	0.855000	0.670000	0.619000	0.600000	0.629000
AAC58	6	0.776000	0.775000	0.701000	0.715000	0.594000	0.817000	0.662000	0.800000	0.599000	0.628000	0.546000	0.635000	0.675000	0.825000	0.629000	0.653000	0.568000	0.671000	0.666000	0.803000	0.665000	0.633000	0.556000	0.619000
AAC59	3	0.794000	0.725000	0.680000	0.710000	0.570000	0.802000	0.600000	0.797000	0.600000	0.589000	0.565000	0.628000	0.632000	0.859000	0.619000	0.592000	0.562000	0.670000	0.623000	0.815000	0.649000	0.585000	0.568000	0.615000
AAC60	3	0.789000	0.653000	0.656000	0.675000	0.563000	0.772000	0.636000	0.783000	0.590000	0.626000	0.535000	0.635000	0.670000	0.830000	0.637000	0.639000	0.529000	0.677000	0.670000	0.813000	0.645000	0.613000	0.546000	0.613000

You can for example select the scale set with the best performance for the ‘SEQ_CAPSID’ dataset as follows:

# Sort 'df_top60_eval' by 'SEQ_CAPSID' and get the index of the top row
top_id = df_top60_eval.sort_values(by="SEQ_CAPSID", ascending=False).index[0]
df_scales_top =  aa.load_scales(top60_n=top_id)
# Select a specific set using integers
df_cat_top20 = aa.load_scales(name="scales_cat", top60_n=20)
aa.display_df(df_cat_top20, n_rows=6, show_shape=True)

DataFrame shape: (90, 5)

	scale_id	category	subcategory	scale_name	scale_description
1	BULH740102	ASA/Volume	Partial specific volume	Partial specific volume	Apparent partia...l-Breese, 1974)
2	DIGM050101	Composition	AA composition (surface)	Hydrostatic pressure	Hydrostatic Pre...i Giulio, 2005)
3	FUKS010103	Composition	AA composition (surface)	Proteins of mesophiles (EXT)	Surface composi...ishikawa, 2001)
4	GRAR740101	Composition	Unclassified (Composition)	Substitution Frequency	Composition (Grantham, 1974)
5	CHOP780207	Conformation	Coil	Non helical reg...on (C-terminal)	Normalized freq...-Fasman, 1978b)
6	ROBB760107	Conformation	Coil (C-term)	Coil (C-terminal)	Information mea...n-Suzuki, 1976)

Two optional filtering steps are provided by the just_aaindex and unclassified_out parameters:

n_all_scales = len(aa.load_scales().T)
n_just_aaindex = len(aa.load_scales(just_aaindex=True).T)
n_classified = len(aa.load_scales(unclassified_out=True).T)
n_both_filter_steps = len(aa.load_scales(just_aaindex=True, unclassified_out=True).T)
print(n_all_scales, " scales")
print(n_just_aaindex, " from AAindex")
print(n_classified, " are classified in AAontology")
print(n_both_filter_steps, " fulfill both.")

scales
from AAindex
are classified in AAontology
fulfill both.