Comparing apo and holo CDR loop clustering

Introduction

In this notebook, we set out to assess whether the CDR loops of TCRs stay in there canonical forms between apo and holo states or if they disrupt them.

[1]:

import itertools

import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns

[2]:

df = pd.read_csv('../data/processed/stcrdab_clusters.csv')
df

[2]:

	name	cluster	chain_type	cdr	sequence	cluster_type
0	7zt2_DE	12	alpha_chain	1	TSGFNG	pseudo
1	7zt3_DE	12	alpha_chain	1	TSGFNG	pseudo
2	7zt4_DE	12	alpha_chain	1	TSGFNG	pseudo
3	7zt5_DE	12	alpha_chain	1	TSGFNG	pseudo
4	7zt7_DE	12	alpha_chain	1	TSGFNG	pseudo
...	...	...	...	...	...	...
4807	6miv_CD	22	beta_chain	3	ASGDEGYTQY	canonical
4808	3rtq_CD	22	beta_chain	3	ASGDEGYTQY	canonical
4809	3dxa_NO	noise	beta_chain	3	ASRYRDDSYNEQF	NaN
4810	1d9k_AB	noise	beta_chain	3	ASGGQGRAEQF	NaN
4811	4gg6_GH	noise	beta_chain	3	ASSVAVSAGTYEQY	NaN

4812 rows × 6 columns

Compare apo and holo forms of loops

[3]:

apo_holo_summary_df = pd.read_csv('../data/processed/apo-holo-tcr-pmhc-class-I/apo_holo_summary.csv')
apo_holo_summary_df

[3]:

	file_name	pdb_id	structure_type	state	alpha_chain	beta_chain	antigen_chain	mhc_chain1	mhc_chain2	cdr_sequences_collated	peptide_sequence	mhc_slug
0	1ao7_D-E-C-A-B_tcr_pmhc.pdb	1ao7	tcr_pmhc	holo	D	E	C	A	B	DRGSQS-IYSNGD-AVTTDSWGKLQ-MNHEY-SVGAGI-ASRPGLA...	LLFGYPVYV	hla_a_02_01
1	1bd2_D-E-C-A-B_tcr_pmhc.pdb	1bd2	tcr_pmhc	holo	D	E	C	A	B	NSMFDY-ISSIKDK-AAMEGAQKLV-MNHEY-SVGAGI-ASSYPGG...	LLFGYPVYV	hla_a_02_01
2	1bii_A-B-P_pmhc.pdb	1bii	pmhc	apo	NaN	NaN	P	A	B	NaN	RGPGRAFVTI	h2_dd
3	1ddh_A-B-P_pmhc.pdb	1ddh	pmhc	apo	NaN	NaN	P	A	B	NaN	RGPGRAFVTI	h2_dd
4	1duz_A-B-C_pmhc.pdb	1duz	pmhc	apo	NaN	NaN	C	A	B	NaN	LLFGYPVYV	hla_a_02_01
...	...	...	...	...	...	...	...	...	...	...	...	...
353	8gon_D-E-C-A-B_tcr_pmhc.pdb	8gon	tcr_pmhc	holo	D	E	C	A	B	TSESDYY-QEAYKQQN-ASSGNTPLV-SGHNS-FNNNVP-ASTWGR...	NaN	NaN
354	8gop_A-B_tcr.pdb	8gop	tcr	apo	A	B	NaN	NaN	NaN	TSESDYY-QEAYKQQN-ASSGNTPLV-SGHNS-FNNNVP-ASTWGR...	NaN	NaN
355	8gvb_A-B-P-H-L_tcr_pmhc.pdb	8gvb	tcr_pmhc	holo	A	B	P	H	L	YGATPY-YFSGDTLV-AVGFTGGGNKLT-SEHNR-FQNEAQ-ASSD...	RYPLTFGW	hla_a_24_02
356	8gvg_A-B-P-H-L_tcr_pmhc.pdb	8gvg	tcr_pmhc	holo	A	B	P	H	L	YGATPY-YFSGDTLV-AVGFTGGGNKLT-SEHNR-FQNEAQ-ASSD...	RFPLTFGW	hla_a_24_02
357	8gvi_A-B-P-H-L_tcr_pmhc.pdb	8gvi	tcr_pmhc	holo	A	B	P	H	L	YGATPY-YFSGDTLV-AVVFTGGGNKLT-SEHNR-FQNEAQ-ASSL...	RYPLTFGW	hla_a_24_02

358 rows × 12 columns

[4]:

df[['pdb_id', 'chains']] = df['name'].str.split('_').apply(pd.Series)
df[['alpha_chain', 'beta_chain']] = df['chains'].apply(list).apply(pd.Series)

[5]:

apo_holo_clusters = df.merge(apo_holo_summary_df,
                             how='inner',
                             on=['pdb_id', 'alpha_chain', 'beta_chain'])
apo_holo_clusters

[5]:

	name	cluster	chain_type	cdr	sequence	cluster_type	pdb_id	chains	alpha_chain	beta_chain	file_name	structure_type	state	antigen_chain	mhc_chain1	mhc_chain2	cdr_sequences_collated	peptide_sequence	mhc_slug
0	8gom_DE	4	alpha_chain	1	TSESDYY	canonical	8gom	DE	D	E	8gom_D-E-C-A-B_tcr_pmhc.pdb	tcr_pmhc	holo	C	A	B	TSESDYY-QEAYKQQN-ASSGNTPLV-SGHNS-FNNNVP-ASTWGR...	NaN	NaN
1	8gom_DE	4	beta_chain	1	SGHNS	canonical	8gom	DE	D	E	8gom_D-E-C-A-B_tcr_pmhc.pdb	tcr_pmhc	holo	C	A	B	TSESDYY-QEAYKQQN-ASSGNTPLV-SGHNS-FNNNVP-ASTWGR...	NaN	NaN
2	8gom_DE	3	alpha_chain	2	QEAYKQQN	canonical	8gom	DE	D	E	8gom_D-E-C-A-B_tcr_pmhc.pdb	tcr_pmhc	holo	C	A	B	TSESDYY-QEAYKQQN-ASSGNTPLV-SGHNS-FNNNVP-ASTWGR...	NaN	NaN
3	8gom_DE	2	beta_chain	2	FNNNVP	canonical	8gom	DE	D	E	8gom_D-E-C-A-B_tcr_pmhc.pdb	tcr_pmhc	holo	C	A	B	TSESDYY-QEAYKQQN-ASSGNTPLV-SGHNS-FNNNVP-ASTWGR...	NaN	NaN
4	8gom_DE	noise	alpha_chain	3	ASSGNTPLV	NaN	8gom	DE	D	E	8gom_D-E-C-A-B_tcr_pmhc.pdb	tcr_pmhc	holo	C	A	B	TSESDYY-QEAYKQQN-ASSGNTPLV-SGHNS-FNNNVP-ASTWGR...	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1189	3dxa_NO	4	beta_chain	1	SEHNR	canonical	3dxa	NO	N	O	3dxa_N-O-M-K-L_tcr_pmhc.pdb	tcr_pmhc	holo	M	K	L	TISGNEY-GLKNN-IVWGGYQKVT-SEHNR-FQNEAQ-ASRYRDDS...	NaN	NaN
1190	3dxa_NO	noise	alpha_chain	2	GLKNN	NaN	3dxa	NO	N	O	3dxa_N-O-M-K-L_tcr_pmhc.pdb	tcr_pmhc	holo	M	K	L	TISGNEY-GLKNN-IVWGGYQKVT-SEHNR-FQNEAQ-ASRYRDDS...	NaN	NaN
1191	3dxa_NO	2	beta_chain	2	FQNEAQ	canonical	3dxa	NO	N	O	3dxa_N-O-M-K-L_tcr_pmhc.pdb	tcr_pmhc	holo	M	K	L	TISGNEY-GLKNN-IVWGGYQKVT-SEHNR-FQNEAQ-ASRYRDDS...	NaN	NaN
1192	3dxa_NO	6	alpha_chain	3	IVWGGYQKVT	canonical	3dxa	NO	N	O	3dxa_N-O-M-K-L_tcr_pmhc.pdb	tcr_pmhc	holo	M	K	L	TISGNEY-GLKNN-IVWGGYQKVT-SEHNR-FQNEAQ-ASRYRDDS...	NaN	NaN
1193	3dxa_NO	noise	beta_chain	3	ASRYRDDSYNEQF	NaN	3dxa	NO	N	O	3dxa_N-O-M-K-L_tcr_pmhc.pdb	tcr_pmhc	holo	M	K	L	TISGNEY-GLKNN-IVWGGYQKVT-SEHNR-FQNEAQ-ASRYRDDS...	NaN	NaN

1194 rows × 19 columns

[6]:

def collate_clusters(group):
    apo_clusters = sorted(group.query("state == 'apo'")['cluster'].tolist())
    holo_clusters = sorted(group.query("state == 'holo'")['cluster'].tolist())

    return pd.Series({'apo_clusters': apo_clusters, 'holo_clusters': holo_clusters})

apo_holo_clusters_agg = apo_holo_clusters.groupby(['cdr_sequences_collated',
                                                   'chain_type',
                                                   'cdr']).apply(collate_clusters).reset_index()
apo_holo_clusters_agg

[6]:

	cdr_sequences_collated	chain_type	cdr	apo_clusters	holo_clusters
0	ATGYPS-ATKADDK-ALSDPVNDMR-SGHAT-FQNNGV-ASSLRGR...	alpha_chain	1	[13]	[noise, noise]
1	ATGYPS-ATKADDK-ALSDPVNDMR-SGHAT-FQNNGV-ASSLRGR...	alpha_chain	2	[noise]	[noise, noise]
2	ATGYPS-ATKADDK-ALSDPVNDMR-SGHAT-FQNNGV-ASSLRGR...	alpha_chain	3	[noise]	[noise, noise]
3	ATGYPS-ATKADDK-ALSDPVNDMR-SGHAT-FQNNGV-ASSLRGR...	beta_chain	1	[4]	[4, 4]
4	ATGYPS-ATKADDK-ALSDPVNDMR-SGHAT-FQNNGV-ASSLRGR...	beta_chain	2	[2]	[2, 2]
...	...	...	...	...	...
385	YSGSPE-HISR-ALSGFNNAGNMLT-SGHAT-FQNNGV-ASSLGGA...	alpha_chain	2	[noise]	[noise]
386	YSGSPE-HISR-ALSGFNNAGNMLT-SGHAT-FQNNGV-ASSLGGA...	alpha_chain	3	[noise]	[2]
387	YSGSPE-HISR-ALSGFNNAGNMLT-SGHAT-FQNNGV-ASSLGGA...	beta_chain	1	[4]	[4]
388	YSGSPE-HISR-ALSGFNNAGNMLT-SGHAT-FQNNGV-ASSLGGA...	beta_chain	2	[2]	[2]
389	YSGSPE-HISR-ALSGFNNAGNMLT-SGHAT-FQNNGV-ASSLGGA...	beta_chain	3	[noise]	[noise]

390 rows × 5 columns

[7]:

cluster_types = (df[['chain_type', 'cdr', 'cluster', 'cluster_type']].drop_duplicates()
                                                                     .set_index(['chain_type', 'cdr', 'cluster'])
                                                                     ['cluster_type'])

[8]:

def classify_movement(chain_type: str,
                      cdr: str,
                      apo_clusters: list[str],
                      holo_clusters: list[str],
                      cluster_types: pd.Series) -> pd.Series:
    types = {
        'Noise': 0,
        'Noise to Canonical Cluster': 0,
        'Noise to Pseudo Cluster': 0,
        'Canonical Cluster to Noise': 0,
        'Pseudo Cluster to Noise': 0,
        'Canonical Cluster Shift': 0,
        'Pseudo Cluster Shift': 0,
        'Canonical to Pseudo': 0,
        'Pseudo to Canonical': 0,
        'Canonical Cluster Same': 0,
        'Pseudo Cluster Same': 0,
    }

    for apo_clust, holo_clust in itertools.product(apo_clusters, holo_clusters):
        if apo_clust == 'noise' and holo_clust == 'noise':
            types['Noise'] += 1

        elif apo_clust == 'noise' and holo_clust != 'noise':
            if cluster_types.loc[chain_type, cdr, holo_clust] == 'canonical':
                 types['Noise to Canonical Cluster'] += 1

            else:
                types['Noise to Pseudo Cluster'] += 1

        elif apo_clust != 'noise' and holo_clust == 'noise':
            if cluster_types.loc[chain_type, cdr, apo_clust] == 'canonical':
                 types['Canonical Cluster to Noise'] += 1

            else:
                types['Pseudo Cluster to Noise'] += 1

        elif apo_clust != holo_clust:
            if (cluster_types.loc[chain_type, cdr, apo_clust]
                == cluster_types.loc[chain_type, cdr, holo_clust]
                == 'canonical'):
                 types['Canonical Cluster Shift'] += 1

            elif (cluster_types.loc[chain_type, cdr, apo_clust] == 'canonical'
                  and cluster_types.loc[chain_type, cdr, holo_clust] == 'pseudo'):
                types['Canonical to Pseudo'] += 1

            else:
                types['Pseudo to Canonical'] += 1

        elif apo_clust == holo_clust:
            if cluster_types.loc[chain_type, cdr, apo_clust] == 'canonical':
                types['Canonical Cluster Same'] += 1

            else:
                types['Pseudo Cluster Same'] += 1

    return pd.Series(types)


apo_holo_clusters_agg = apo_holo_clusters_agg.join(apo_holo_clusters_agg.apply(
    lambda row: classify_movement(row.chain_type, row.cdr, row.apo_clusters, row.holo_clusters, cluster_types), axis=1
))

# Wide to long
apo_holo_clusters_agg = apo_holo_clusters_agg.melt(id_vars=['cdr_sequences_collated', 'chain_type', 'cdr'],
                                                 value_vars=['Noise',
                                                             'Noise to Canonical Cluster',
                                                             'Noise to Pseudo Cluster',
                                                             'Canonical Cluster to Noise',
                                                             'Pseudo Cluster to Noise',
                                                             'Canonical Cluster Shift',
                                                             'Pseudo Cluster Shift',
                                                             'Canonical to Pseudo',
                                                             'Pseudo to Canonical',
                                                             'Canonical Cluster Same',
                                                             'Pseudo Cluster Same'],
                                                 var_name='types', value_name='count')

# Normalize for differeing group sizes
apo_holo_clusters_agg = apo_holo_clusters_agg.groupby(
    ['cdr_sequences_collated', 'chain_type', 'cdr', 'types'],
)['count'].median().reset_index()
apo_holo_clusters_agg

[8]:

	cdr_sequences_collated	chain_type	cdr	types	count
0	ATGYPS-ATKADDK-ALSDPVNDMR-SGHAT-FQNNGV-ASSLRGR...	alpha_chain	1	Canonical Cluster Same	0.0
1	ATGYPS-ATKADDK-ALSDPVNDMR-SGHAT-FQNNGV-ASSLRGR...	alpha_chain	1	Canonical Cluster Shift	0.0
2	ATGYPS-ATKADDK-ALSDPVNDMR-SGHAT-FQNNGV-ASSLRGR...	alpha_chain	1	Canonical Cluster to Noise	2.0
3	ATGYPS-ATKADDK-ALSDPVNDMR-SGHAT-FQNNGV-ASSLRGR...	alpha_chain	1	Canonical to Pseudo	0.0
4	ATGYPS-ATKADDK-ALSDPVNDMR-SGHAT-FQNNGV-ASSLRGR...	alpha_chain	1	Noise	0.0
...	...	...	...	...	...
4285	YSGSPE-HISR-ALSGFNNAGNMLT-SGHAT-FQNNGV-ASSLGGA...	beta_chain	3	Noise to Pseudo Cluster	0.0
4286	YSGSPE-HISR-ALSGFNNAGNMLT-SGHAT-FQNNGV-ASSLGGA...	beta_chain	3	Pseudo Cluster Same	0.0
4287	YSGSPE-HISR-ALSGFNNAGNMLT-SGHAT-FQNNGV-ASSLGGA...	beta_chain	3	Pseudo Cluster Shift	0.0
4288	YSGSPE-HISR-ALSGFNNAGNMLT-SGHAT-FQNNGV-ASSLGGA...	beta_chain	3	Pseudo Cluster to Noise	0.0
4289	YSGSPE-HISR-ALSGFNNAGNMLT-SGHAT-FQNNGV-ASSLGGA...	beta_chain	3	Pseudo to Canonical	0.0

4290 rows × 5 columns

[9]:

apo_holo_clusters_agg

[9]:

	cdr_sequences_collated	chain_type	cdr	types	count
0	ATGYPS-ATKADDK-ALSDPVNDMR-SGHAT-FQNNGV-ASSLRGR...	alpha_chain	1	Canonical Cluster Same	0.0
1	ATGYPS-ATKADDK-ALSDPVNDMR-SGHAT-FQNNGV-ASSLRGR...	alpha_chain	1	Canonical Cluster Shift	0.0
2	ATGYPS-ATKADDK-ALSDPVNDMR-SGHAT-FQNNGV-ASSLRGR...	alpha_chain	1	Canonical Cluster to Noise	2.0
3	ATGYPS-ATKADDK-ALSDPVNDMR-SGHAT-FQNNGV-ASSLRGR...	alpha_chain	1	Canonical to Pseudo	0.0
4	ATGYPS-ATKADDK-ALSDPVNDMR-SGHAT-FQNNGV-ASSLRGR...	alpha_chain	1	Noise	0.0
...	...	...	...	...	...
4285	YSGSPE-HISR-ALSGFNNAGNMLT-SGHAT-FQNNGV-ASSLGGA...	beta_chain	3	Noise to Pseudo Cluster	0.0
4286	YSGSPE-HISR-ALSGFNNAGNMLT-SGHAT-FQNNGV-ASSLGGA...	beta_chain	3	Pseudo Cluster Same	0.0
4287	YSGSPE-HISR-ALSGFNNAGNMLT-SGHAT-FQNNGV-ASSLGGA...	beta_chain	3	Pseudo Cluster Shift	0.0
4288	YSGSPE-HISR-ALSGFNNAGNMLT-SGHAT-FQNNGV-ASSLGGA...	beta_chain	3	Pseudo Cluster to Noise	0.0
4289	YSGSPE-HISR-ALSGFNNAGNMLT-SGHAT-FQNNGV-ASSLGGA...	beta_chain	3	Pseudo to Canonical	0.0

4290 rows × 5 columns

[10]:

g = sns.catplot(apo_holo_clusters_agg.sort_values(['chain_type', 'cdr', 'types']),
                row='chain_type', col='cdr',
                x='types', y='count',
                kind='bar')

for ax in g.axes.flat:
    labels = ax.get_xticklabels()
    plt.setp(labels, rotation=90)

../_images/source_Comparing_of_apo_and_holo_CDR_loop_clustering_12_0.png

[11]:

cluster_shift_counts = apo_holo_clusters_agg.groupby(['chain_type', 'cdr', 'types'])['count'].sum().astype(int)
cluster_shift_counts[cluster_shift_counts > 0]

[11]:

chain_type   cdr  types
alpha_chain  1    Canonical Cluster Same        112
                  Canonical Cluster to Noise      9
                  Canonical to Pseudo             7
                  Noise                           8
                  Noise to Canonical Cluster     11
                  Noise to Pseudo Cluster        13
                  Pseudo Cluster Same            10
                  Pseudo to Canonical             8
             2    Canonical Cluster Same         95
                  Canonical Cluster to Noise      5
                  Noise                          15
                  Noise to Canonical Cluster      9
                  Pseudo Cluster Same            28
                  Pseudo Cluster to Noise        26
             3    Canonical Cluster Same         39
                  Canonical Cluster to Noise     12
                  Noise                          24
                  Noise to Canonical Cluster     16
                  Noise to Pseudo Cluster        40
                  Pseudo Cluster Same            46
                  Pseudo Cluster to Noise         1
beta_chain   1    Canonical Cluster Same        142
                  Canonical Cluster Shift        29
                  Noise to Canonical Cluster      2
                  Pseudo Cluster Same             5
             2    Canonical Cluster Same         71
                  Canonical Cluster Shift         2
                  Canonical Cluster to Noise      8
                  Noise                          84
                  Noise to Canonical Cluster      2
                  Noise to Pseudo Cluster         6
                  Pseudo Cluster to Noise         5
             3    Canonical Cluster Same          5
                  Noise                          48
                  Noise to Canonical Cluster     11
                  Noise to Pseudo Cluster        32
                  Pseudo Cluster Same            79
                  Pseudo Cluster to Noise         3
Name: count, dtype: int64

[12]:

export_cluster_shift_counts = cluster_shift_counts.to_frame().reset_index()

export_cluster_shift_counts = export_cluster_shift_counts.rename({'types': 'Movement Type'}, axis='columns')

export_cluster_shift_counts['CDR Type'] = export_cluster_shift_counts.apply(
    lambda row: f"CDR{row.cdr}\\text{row.chain_type.split('_')[0]}{{}}",
    axis='columns',
)

export_cluster_shift_counts = export_cluster_shift_counts.pivot(index='Movement Type',
                                                                columns='CDR Type',
                                                                values='count')
export_cluster_shift_counts = export_cluster_shift_counts.sort_values('Movement Type')

export_cluster_shift_counts = export_cluster_shift_counts.replace(0, '-')
export_cluster_shift_counts = export_cluster_shift_counts.sort_index()
export_cluster_shift_counts.columns.name = None
export_cluster_shift_counts = export_cluster_shift_counts.reset_index()

export_cluster_shift_counts

/var/scratch/bmcmaste/1908294/ipykernel_260472/2849749015.py:21: FutureWarning: In future versions `DataFrame.to_latex` is expected to utilise the base implementation of `Styler.to_latex` for formatting and rendering. The arguments signature may therefore change. It is recommended instead to use `DataFrame.style.to_latex` which also contains additional functionality.
  fh.write(export_cluster_shift_counts.to_latex(escape=False, index=False))

[12]:

	Movement Type	CDR1\textalpha{}	CDR1\textbeta{}	CDR2\textalpha{}	CDR2\textbeta{}	CDR3\textalpha{}	CDR3\textbeta{}
0	Canonical Cluster Same	112	142	95	71	39	5
1	Canonical Cluster Shift	-	29	-	2	-	-
2	Canonical Cluster to Noise	9	-	5	8	12	-
3	Canonical to Pseudo	7	-	-	-	-	-
4	Noise	8	-	15	84	24	48
5	Noise to Canonical Cluster	11	2	9	2	16	11
6	Noise to Pseudo Cluster	13	-	-	6	40	32
7	Pseudo Cluster Same	10	5	28	-	46	79
8	Pseudo Cluster Shift	-	-	-	-	-	-
9	Pseudo Cluster to Noise	-	-	26	5	1	3
10	Pseudo to Canonical	8	-	-	-	-	-

Conclusion

The analysis here shows that for the most part, the CDR-1s and CDR-2s stay in their canonical forms between apo and holo states where as the CDR-3 loops have a variety of modes. As expected, it is more difficult to cluster the CDR-3 loops as they are more structurally diverse.