pMHC movement based on peptide anchoring

Introduction

In this notebook, apo-holo comparisons between different anchoring patterns of MHC alleles plotted next to each other. The anchoring patterns were determined by finding the peptide motifs of each MHC allele from the MHCMotifAtlas and using these strong motifs as anchors for the peptide.

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from python_pdb.formats.residue import THREE_TO_ONE_CODE

mhc_anchor_position_df = pd.read_csv('../data/external/mhc_motif_atlas.csv')
mhc_anchor_position_df = mhc_anchor_position_df.query("grade in ('high', 'dominant')")
mhc_anchor_position_df = mhc_anchor_position_df.sort_values('allele_slug')
mhc_anchor_position_df = mhc_anchor_position_df[['allele_slug', 'position', 'amino_acid', 'peptide_length']]
mhc_anchor_position_df = mhc_anchor_position_df.reset_index(drop=True)

mhc_anchor_position_df

	allele_slug	position	amino_acid	peptide_length
0	hla_a_01_01	2	T	9
1	hla_a_01_01	3	D	9
2	hla_a_01_01	9	Y	9
3	hla_a_02_01	2	L	9
4	hla_a_02_01	9	L	9
...	...	...	...	...
319	hla_g_01_03	1	K	9
320	hla_g_01_04	1	R	9
321	hla_g_01_04	3	P	9
322	hla_g_01_04	1	K	9
323	hla_g_01_04	9	L	9

324 rows × 4 columns

Load apo-holo comparisons

apo_holo_comparison = pd.read_csv('../data/processed/apo-holo-tcr-pmhc-class-I-comparisons/pmhc_per_res_apo_holo.csv')

peptide_apo_holo_comparison = apo_holo_comparison.query("chain_type == 'antigen_chain'").copy()
peptide_apo_holo_comparison

	complex_id	structure_x_name	structure_y_name	chain_type	residue_name	residue_seq_id	residue_insert_code	rmsd	ca_distance	chi_angle_change	com_distance
181	7ow6_D-E-C-A-B_tcr_pmhc	7ow4_A-B-C_pmhc.pdb	7ow4_D-E-F_pmhc.pdb	antigen_chain	VAL	1	NaN	0.354933	0.330887	0.200330	0.316020
182	7ow6_D-E-C-A-B_tcr_pmhc	7ow4_A-B-C_pmhc.pdb	7ow4_D-E-F_pmhc.pdb	antigen_chain	VAL	2	NaN	0.262440	0.073709	0.030793	0.085332
183	7ow6_D-E-C-A-B_tcr_pmhc	7ow4_A-B-C_pmhc.pdb	7ow4_D-E-F_pmhc.pdb	antigen_chain	VAL	3	NaN	0.256739	0.248310	-0.007898	0.215858
184	7ow6_D-E-C-A-B_tcr_pmhc	7ow4_A-B-C_pmhc.pdb	7ow4_D-E-F_pmhc.pdb	antigen_chain	GLY	4	NaN	0.568505	0.622876	NaN	0.318856
185	7ow6_D-E-C-A-B_tcr_pmhc	7ow4_A-B-C_pmhc.pdb	7ow4_D-E-F_pmhc.pdb	antigen_chain	GLY	7	NaN	0.346240	0.192317	NaN	0.324260
...	...	...	...	...	...	...	...	...	...	...	...
126149	7rtr_D-E-C-A-B_tcr_pmhc	7rtd_A-B-C_pmhc.pdb	7rtr_D-E-C-A-B_tcr_pmhc.pdb	antigen_chain	ARG	5	NaN	1.064663	0.157924	0.364432	0.705033
126150	7rtr_D-E-C-A-B_tcr_pmhc	7rtd_A-B-C_pmhc.pdb	7rtr_D-E-C-A-B_tcr_pmhc.pdb	antigen_chain	THR	6	NaN	0.421897	0.345439	0.097701	0.344074
126151	7rtr_D-E-C-A-B_tcr_pmhc	7rtd_A-B-C_pmhc.pdb	7rtr_D-E-C-A-B_tcr_pmhc.pdb	antigen_chain	PHE	7	NaN	1.225982	0.317819	-0.347835	0.883236
126152	7rtr_D-E-C-A-B_tcr_pmhc	7rtd_A-B-C_pmhc.pdb	7rtr_D-E-C-A-B_tcr_pmhc.pdb	antigen_chain	LEU	8	NaN	1.323615	0.310356	0.612672	0.270049
126153	7rtr_D-E-C-A-B_tcr_pmhc	7rtd_A-B-C_pmhc.pdb	7rtr_D-E-C-A-B_tcr_pmhc.pdb	antigen_chain	LEU	9	NaN	1.599917	0.192969	0.885323	0.175807

5838 rows × 11 columns

peptide_apo_holo_comparison['peptide_length'] = \
    peptide_apo_holo_comparison.groupby(['complex_id',
                                         'structure_x_name',
                                         'structure_y_name']).transform('size')

peptide_apo_holo_comparison['amino_acid'] = peptide_apo_holo_comparison['residue_name'].map(THREE_TO_ONE_CODE)

Load summary data

summary_df = pd.read_csv('../data/processed/apo-holo-tcr-pmhc-class-I/apo_holo_summary.csv')
summary_df['group_name'] = summary_df['file_name'].str.replace('.pdb', '')

summary_df

/var/scratch/bmcmaste/1915160/ipykernel_3863244/1956598565.py:2: FutureWarning: The default value of regex will change from True to False in a future version.
  summary_df['group_name'] = summary_df['file_name'].str.replace('.pdb', '')

	file_name	pdb_id	structure_type	state	alpha_chain	beta_chain	antigen_chain	mhc_chain1	mhc_chain2	cdr_sequences_collated	peptide_sequence	mhc_slug	group_name
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	1ao7_D-E-C-A-B_tcr_pmhc
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	1bd2_D-E-C-A-B_tcr_pmhc
2	1bii_A-B-P_pmhc.pdb	1bii	pmhc	apo	NaN	NaN	P	A	B	NaN	RGPGRAFVTI	h2_dd	1bii_A-B-P_pmhc
3	1ddh_A-B-P_pmhc.pdb	1ddh	pmhc	apo	NaN	NaN	P	A	B	NaN	RGPGRAFVTI	h2_dd	1ddh_A-B-P_pmhc
4	1duz_A-B-C_pmhc.pdb	1duz	pmhc	apo	NaN	NaN	C	A	B	NaN	LLFGYPVYV	hla_a_02_01	1duz_A-B-C_pmhc
...	...	...	...	...	...	...	...	...	...	...	...	...	...
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	8gon_D-E-C-A-B_tcr_pmhc
354	8gop_A-B_tcr.pdb	8gop	tcr	apo	A	B	NaN	NaN	NaN	TSESDYY-QEAYKQQN-ASSGNTPLV-SGHNS-FNNNVP-ASTWGR...	NaN	NaN	8gop_A-B_tcr
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	8gvb_A-B-P-H-L_tcr_pmhc
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	8gvg_A-B-P-H-L_tcr_pmhc
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	8gvi_A-B-P-H-L_tcr_pmhc

358 rows × 13 columns

Annotate apo-holo data with allele information

peptide_apo_holo_comparison = peptide_apo_holo_comparison.merge(summary_df, how='left', left_on='complex_id', right_on='group_name')
peptide_apo_holo_comparison

	complex_id	structure_x_name	structure_y_name	chain_type	residue_name	residue_seq_id	residue_insert_code	rmsd	ca_distance	chi_angle_change	...	state	alpha_chain	beta_chain	antigen_chain	mhc_chain1	mhc_chain2	cdr_sequences_collated	peptide_sequence	mhc_slug	group_name
0	7ow6_D-E-C-A-B_tcr_pmhc	7ow4_A-B-C_pmhc.pdb	7ow4_D-E-F_pmhc.pdb	antigen_chain	VAL	1	NaN	0.354933	0.330887	0.200330	...	holo	D	E	C	A	B	TRDTAYY-QPWWGEQN-AMSVPSGDGSYQFT-MNHEY-SVGEGT-A...	VVVGADGVGK	hla_a_11_01	7ow6_D-E-C-A-B_tcr_pmhc
1	7ow6_D-E-C-A-B_tcr_pmhc	7ow4_A-B-C_pmhc.pdb	7ow4_D-E-F_pmhc.pdb	antigen_chain	VAL	2	NaN	0.262440	0.073709	0.030793	...	holo	D	E	C	A	B	TRDTAYY-QPWWGEQN-AMSVPSGDGSYQFT-MNHEY-SVGEGT-A...	VVVGADGVGK	hla_a_11_01	7ow6_D-E-C-A-B_tcr_pmhc
2	7ow6_D-E-C-A-B_tcr_pmhc	7ow4_A-B-C_pmhc.pdb	7ow4_D-E-F_pmhc.pdb	antigen_chain	VAL	3	NaN	0.256739	0.248310	-0.007898	...	holo	D	E	C	A	B	TRDTAYY-QPWWGEQN-AMSVPSGDGSYQFT-MNHEY-SVGEGT-A...	VVVGADGVGK	hla_a_11_01	7ow6_D-E-C-A-B_tcr_pmhc
3	7ow6_D-E-C-A-B_tcr_pmhc	7ow4_A-B-C_pmhc.pdb	7ow4_D-E-F_pmhc.pdb	antigen_chain	GLY	4	NaN	0.568505	0.622876	NaN	...	holo	D	E	C	A	B	TRDTAYY-QPWWGEQN-AMSVPSGDGSYQFT-MNHEY-SVGEGT-A...	VVVGADGVGK	hla_a_11_01	7ow6_D-E-C-A-B_tcr_pmhc
4	7ow6_D-E-C-A-B_tcr_pmhc	7ow4_A-B-C_pmhc.pdb	7ow4_D-E-F_pmhc.pdb	antigen_chain	GLY	7	NaN	0.346240	0.192317	NaN	...	holo	D	E	C	A	B	TRDTAYY-QPWWGEQN-AMSVPSGDGSYQFT-MNHEY-SVGEGT-A...	VVVGADGVGK	hla_a_11_01	7ow6_D-E-C-A-B_tcr_pmhc
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
5833	7rtr_D-E-C-A-B_tcr_pmhc	7rtd_A-B-C_pmhc.pdb	7rtr_D-E-C-A-B_tcr_pmhc.pdb	antigen_chain	ARG	5	NaN	1.064663	0.157924	0.364432	...	holo	D	E	C	A	B	DRGSQS-IYSNGD-AVNRDDKII-SEHNR-FQNEAQ-ASSPDIEQY	YLQPRTFLL	hla_a_02_01	7rtr_D-E-C-A-B_tcr_pmhc
5834	7rtr_D-E-C-A-B_tcr_pmhc	7rtd_A-B-C_pmhc.pdb	7rtr_D-E-C-A-B_tcr_pmhc.pdb	antigen_chain	THR	6	NaN	0.421897	0.345439	0.097701	...	holo	D	E	C	A	B	DRGSQS-IYSNGD-AVNRDDKII-SEHNR-FQNEAQ-ASSPDIEQY	YLQPRTFLL	hla_a_02_01	7rtr_D-E-C-A-B_tcr_pmhc
5835	7rtr_D-E-C-A-B_tcr_pmhc	7rtd_A-B-C_pmhc.pdb	7rtr_D-E-C-A-B_tcr_pmhc.pdb	antigen_chain	PHE	7	NaN	1.225982	0.317819	-0.347835	...	holo	D	E	C	A	B	DRGSQS-IYSNGD-AVNRDDKII-SEHNR-FQNEAQ-ASSPDIEQY	YLQPRTFLL	hla_a_02_01	7rtr_D-E-C-A-B_tcr_pmhc
5836	7rtr_D-E-C-A-B_tcr_pmhc	7rtd_A-B-C_pmhc.pdb	7rtr_D-E-C-A-B_tcr_pmhc.pdb	antigen_chain	LEU	8	NaN	1.323615	0.310356	0.612672	...	holo	D	E	C	A	B	DRGSQS-IYSNGD-AVNRDDKII-SEHNR-FQNEAQ-ASSPDIEQY	YLQPRTFLL	hla_a_02_01	7rtr_D-E-C-A-B_tcr_pmhc
5837	7rtr_D-E-C-A-B_tcr_pmhc	7rtd_A-B-C_pmhc.pdb	7rtr_D-E-C-A-B_tcr_pmhc.pdb	antigen_chain	LEU	9	NaN	1.599917	0.192969	0.885323	...	holo	D	E	C	A	B	DRGSQS-IYSNGD-AVNRDDKII-SEHNR-FQNEAQ-ASSPDIEQY	YLQPRTFLL	hla_a_02_01	7rtr_D-E-C-A-B_tcr_pmhc

5838 rows × 26 columns

Combine anchoring information with apo-holo comparisons

mhc_anchor_position_df['anchor'] = True

peptide_apo_holo_comparison = peptide_apo_holo_comparison.merge(
    mhc_anchor_position_df,
    how='left',
    left_on=['mhc_slug', 'residue_seq_id', 'amino_acid', 'peptide_length'],
    right_on=['allele_slug', 'position', 'amino_acid', 'peptide_length'],
)

def collate_anchors(group: pd.DataFrame) -> list[int]:
    return sorted(group[group['anchor'] == True]['residue_seq_id'].unique().tolist())

anchoring_strategies = peptide_apo_holo_comparison.groupby(['structure_x_name', 'structure_y_name']).apply(collate_anchors)
anchoring_strategies.name = 'anchoring_strategy'
anchoring_strategies = anchoring_strategies.reset_index()

peptide_apo_holo_comparison = peptide_apo_holo_comparison.merge(anchoring_strategies, how='left', on=['structure_x_name', 'structure_y_name'])

def markup_anchor(anchors: list[int]) -> str:
    anchors = [f'p{anchor}' for anchor in anchors]
    return '-'.join(anchors)

peptide_apo_holo_comparison['anchoring_strategy_str'] = peptide_apo_holo_comparison['anchoring_strategy'].map(markup_anchor)

def find_dominant_anchor(group: pd.DataFrame) -> str:
    anchor_types = group['anchoring_strategy_str'].unique()
    lengths = np.array([len(anchor) for anchor in anchor_types])
    index = np.argmax(lengths)

    return anchor_types[index]

dominant_anchors = peptide_apo_holo_comparison.groupby('mhc_slug').apply(find_dominant_anchor)
dominant_anchors.name = 'dominant_anchor'
dominant_anchors = dominant_anchors.reset_index()

peptide_apo_holo_comparison = peptide_apo_holo_comparison.merge(dominant_anchors, how='left', on='mhc_slug')

The below correction is applied as it is assumed that those peptide marked as solely ‘p2’ or ‘p9’ anchors are most likely still anchored by something at either end that is not necessarily the high or dominant motif.

peptide_apo_holo_comparison['dominant_anchor'] = peptide_apo_holo_comparison['dominant_anchor'].map(
    lambda strategy: 'p2-p9' if strategy in ('p2', 'p9') else strategy
)

peptide_apo_holo_comparison_with_anchor = peptide_apo_holo_comparison.query("anchoring_strategy_str != ''")

Visualising the results

g = sns.catplot(peptide_apo_holo_comparison_with_anchor,
                col='dominant_anchor',
                x='residue_seq_id', y='rmsd',
                sharex=False,
                color='salmon',
                kind='bar')

def annotate(data, **kws):
    groups = data.groupby(['mhc_slug', 'peptide_sequence'])
    ax = plt.gca()
    ax.text(0.45, 0.9, f'n={len(groups)}', fontsize=16, transform=ax.transAxes)

g.map_dataframe(annotate)

max_rmsd = peptide_apo_holo_comparison_with_anchor['rmsd'].max()

for mhc_slug, group in peptide_apo_holo_comparison_with_anchor.groupby('mhc_slug'):
    print(mhc_slug)
    print(group['dominant_anchor'].unique()[0])
    plot = sns.barplot(group, x='residue_seq_id', y='rmsd', color='salmon')
    plot.set_ylim((0, np.ceil(max_rmsd)))
    plt.show()

hla_a_02_01
p2-p9

hla_a_02_06
p2-p9

hla_a_03_01
p2-p9

hla_a_24_02
p2-p9

hla_b_08_01
p2-p5-p9

hla_b_35_01
p2-p9

hla_b_37_01
p2-p5-p9

hla_b_42_01
p2-p9

hla_b_44_05
p2-p9

hla_b_53_01
p2-p9

hla_b_81_01
p2-p9

hla_c_08_02
p2-p3

hla_e_01_03
p2-p9

sns.barplot(peptide_apo_holo_comparison_with_anchor,
            hue='dominant_anchor',
            x='residue_seq_id', y='rmsd')

<AxesSubplot: xlabel='residue_seq_id', ylabel='rmsd'>

representative_alleles = (peptide_apo_holo_comparison_with_anchor.groupby('dominant_anchor')[['dominant_anchor', 'mhc_slug']]
                                                                 .sample(1, random_state=1)
                                                                 .reset_index(drop=True))
representative_alleles

	dominant_anchor	mhc_slug
0	p2-p3	hla_c_08_02
1	p2-p5-p9	hla_b_08_01
2	p2-p9	hla_a_02_01

sns.barplot(peptide_apo_holo_comparison_with_anchor.query("mhc_slug.isin(@representative_alleles['mhc_slug'])"),
            hue='mhc_slug',
            x='residue_seq_id', y='rmsd')

<AxesSubplot: xlabel='residue_seq_id', ylabel='rmsd'>

Conclusion

From the visualisations it is clear that the profile of peptide conformational changes depends on the mhc allele and how the peptides are anchored by the allele.