A minimal CPP analysis

The shortest complete loop in AAanalysis: load a dataset, run Comparative Physicochemical Profiling (CPP) with its defaults, and read out the physicochemical signature that separates the two groups — first as a ranked list, then as a feature map.

We use the domain-level γ-secretase dataset (DOM_GSEC: substrates vs. non-substrates) and lean on defaults throughout: CPP loads a default amino-acid scale set for you, so there is nothing to select or tune. For the broader tour (machine learning, SHAP feature impact, the comparison harness) see the Quick start tutorial.

You will learn to use the tool below — its inputs, outputs, key parameters, and how to inspect the result.

import matplotlib.pyplot as plt

import aaanalysis as aa
aa.options["verbose"] = False
aa.options["random_state"] = 42

1. Load a dataset

load_dataset() returns a sequence table (df_seq) with the TMD bounds. Its binary label column is the A-vs-B grouping that CPP contrasts.

df_seq = aa.load_dataset(name="DOM_GSEC", n=50)
labels = df_seq["label"].to_list()
aa.display_df(df=df_seq, n_rows=10, show_shape=True)

DataFrame shape: (100, 8)
  entry sequence label tmd_start tmd_stop jmd_n tmd jmd_c
1 Q14802 MQKVTLGLLVFLAGF...PGETPPLITPGSAQS 0 37 59 NSPFYYDWHS LQVGGLICAGVLCAMGIIIVMSA KCKCKFGQKS
2 Q86UE4 MAARSWQDELAQQAE...SPKQIKKKKKARRET 0 50 72 LGLEPKRYPG WVILVGTGALGLLLLFLLGYGWA AACAGARKKR
3 Q969W9 MHRLMGVNSTAAAAA...AIWSKEKDKQKGHPL 0 41 63 FQSMEITELE FVQIIIIVVVMMVMVVVITCLLS HYKLSARSFI
4 P53801 MAPGVARGPTPYWRL...GLFKEENPYARFENN 0 97 119 RWGVCWVNFE ALIITMSVVGGTLLLGIAICCCC CCRRKRSRKP
5 Q8IUW5 MAPRALPGSAVLAAA...EVPATPVKRERSGTE 0 59 81 NDTGNGHPEY IAYALVPVFFIMGLFGVLICHLL KKKGYRCTTE
6 P01135 MVPSAGQLALFALGI...LLKGRTACCHSETVV 0 99 121 AVVAASQKKQ AITALVVVSIVALAVLIITCVLI HCCQVRKHCE
7 O43914 MGGLEPCSRLLLLPL...SDVYSDLNTQRPYYK 0 42 64 DCSCSTVSPG VLAGIVMGDLVLTVLIALAVYFL GRLVPRGRGA
8 P05556 MNLQPIFWIGLISSV...KSAVTTVVNPKYEGK 0 729 751 ENPECPTGPD IIPIVAGVVAGIVLIGLALLLIW KLLMIIHDRR
9 P16234 MGTSHPAFLVLGCLL...DIGIDSSDLVEDSFL 0 527 549 VAPTLRSELT VAAAVLVLLVIVIISLIVLVVIW KQKPRYEIRW
10 P50895 MEPPDAPAQARGAPR...SGGARGGSGGFGDEC 0 549 571 TVSPQTSQAG VAVMAVAVSVGLLLLVVAVFYCV RRKGGPCCRQ

2. Run CPP

SequenceFeature builds the parts (the TMD and its juxtamembrane flanks) that CPP profiles. CPP then creates all Part × Split × Scale features over a default scale set — no scale selection needed — contrasts the two groups, and returns the ranked, non-redundant feature table df_feat.

sf = aa.SequenceFeature()
df_parts = sf.get_df_parts(df_seq=df_seq)
cpp = aa.CPP(df_parts=df_parts)
df_feat = cpp.run(labels=labels)
aa.display_df(df=df_feat, n_rows=10, show_shape=True)

DataFrame shape: (100, 13)
  feature category subcategory scale_name scale_description abs_auc abs_mean_dif mean_dif std_test std_ref p_val_mann_whitney p_val_fdr_bh positions
1 TMD_C_JMD_C-Seg...2,3)-QIAN880106 Conformation α-helix α-helix (middle) Weights for alp...ejnowski, 1988) 0.387000 0.121000 0.121000 0.069000 0.085000 0.000000 0.000001 27,28,29,30,31,32,33
2 TMD_C_JMD_C-Seg...5,7)-FAUJ880104 Shape Side chain length Steric parameter STERIMOL length...e et al., 1988) 0.382000 0.264000 0.264000 0.156000 0.156000 0.000000 0.000001 32,33,34
3 TMD_C_JMD_C-Pat...,12)-ROBB760109 Conformation β-turn (N-term) β-turn (1st residue) Information mea...n-Suzuki, 1976) 0.377000 0.127000 -0.127000 0.062000 0.088000 0.000000 0.000001 21,25,28,32
4 TMD_C_JMD_C-Seg...4,5)-ZIMJ680104 Energy Isoelectric point Isoelectric point Isoelectric poi...n et al., 1968) 0.373000 0.220000 0.220000 0.124000 0.137000 0.000000 0.000001 33,34,35,36
5 TMD_C_JMD_C-Seg...5,7)-ONEK900101 Others Unclassified (Others) ΔG values in peptides Delta G values ...-DeGrado, 1990) 0.373000 0.115000 0.115000 0.066000 0.113000 0.000000 0.000001 32,33,34
6 TMD_C_JMD_C-Seg...4,5)-WOLS870103 Others PC 4 Principal Component 3 (Wold) Principal prope...d et al., 1987) 0.370000 0.218000 -0.218000 0.123000 0.169000 0.000000 0.000001 33,34,35,36
7 TMD_C_JMD_C-Seg...2,3)-WOLS870103 Others PC 4 Principal Component 3 (Wold) Principal prope...d et al., 1987) 0.365000 0.154000 -0.154000 0.096000 0.123000 0.000000 0.000001 27,28,29,30,31,32,33
8 TMD_C_JMD_C-Seg...4,5)-FINA910103 Conformation α-helix (C-cap) α-helix (C-terminal, inside) Helix terminati...n et al., 1991) 0.362000 0.264000 0.264000 0.157000 0.175000 0.000000 0.000001 33,34,35,36
9 TMD_C_JMD_C-Pat...,15)-QIAN880107 Conformation α-helix α-helix (middle) Weights for alp...ejnowski, 1988) 0.359000 0.158000 0.158000 0.081000 0.122000 0.000000 0.000001 25,28,32,35
10 TMD_C_JMD_C-Pat...,15)-MUNV940101 Energy Free energy (folding) Free energy (α-helix) Free energy in ...-Serrano, 1994) 0.358000 0.097000 -0.097000 0.050000 0.082000 0.000000 0.000001 24,28,32,35

3. Score feature importance

A TreeModel scores how much each feature helps tell the two groups apart and writes that back as the feat_importance column that the plots rank by.

X = sf.feature_matrix(df_feat, df_parts)
tm = aa.TreeModel()
tm.fit(X, labels=labels)
df_feat = tm.add_feat_importance(df_feat=df_feat)

4. Read out the signature

ranking() lists the top features — each an interpretable, residue-grounded Part × Split × Scale combination — which together form the physicochemical signature of the substrates.

cpp_plot = aa.CPPPlot()
aa.plot_settings(short_ticks=True, weight_bold=False)
cpp_plot.ranking(df_feat=df_feat, n_top=10)
plt.tight_layout()
plt.show()
../_images/tutorial0_minimal_1_output_10_0.png

feature_map() then charts the whole signature in a single figure — every selected Part × Split × Scale feature placed by scale subcategory (y-axis) and residue position (x-axis), colored by the group mean difference and marked by feature importance. It is the most complete single-figure read-out of a CPP analysis.

aa.plot_settings(font_scale=0.65, weight_bold=False)
cpp_plot.feature_map(df_feat=df_feat)
plt.show()
../_images/tutorial0_minimal_2_output_12_0.png

That is the whole loop: data → CPP → signature. To pick the right setup for your task — residue, domain, or protein level — see the Prediction tasks page, and the Protocols for end-to-end workflows.