Site promiscuity

from katlas.core import *
import pandas as pd
import seaborn as sns
from matplotlib import pyplot as plt
from katlas.plot import *
import matplotlib.ticker as mticker

df=Data.get_ks_dataset()

df.head()

	kin_sub_site	kinase_uniprot	substrate_uniprot	site	source	substrate_genes	substrate_phosphoseq	position	site_seq	sub_site	substrate_sequence	kinase_on_tree	kinase_genes	kinase_group	kinase_family	kinase_pspa_big	kinase_pspa_small	kinase_coral_ID	num_kin
0	O00141_A4FU28_S140	O00141	A4FU28	S140	Sugiyama	CTAGE9	MEEPGATPQPYLGLVLEELGRVVAALPESMRPDENPYGFPSELVVC...	140	AAAEEARSLEATCEKLSRsNsELEDEILCLEKDLKEEKSKH	A4FU28_S140	MEEPGATPQPYLGLVLEELGRVVAALPESMRPDENPYGFPSELVVC...	1	SGK1 SGK	AGC	SGK	Basophilic	Akt/rock	SGK1	22
1	O00141_O00141_S252	O00141	O00141	S252	Sugiyama	SGK1 SGK	MTVKTEAAKGTLTYSRMRGMVAILIAFMKQRRMGLNDFIQKIANNS...	252	SQGHIVLTDFGLCKENIEHNsTtstFCGtPEyLAPEVLHKQ	O00141_S252	MTVKTEAAKGTLTYSRMRGMVAILIAFMKQRRMGLNDFIQKIANNS...	1	SGK1 SGK	AGC	SGK	Basophilic	Akt/rock	SGK1	1
2	O00141_O00141_S255	O00141	O00141	S255	Sugiyama	SGK1 SGK	MTVKTEAAKGTLTYSRMRGMVAILIAFMKQRRMGLNDFIQKIANNS...	255	HIVLTDFGLCKENIEHNsTtstFCGtPEyLAPEVLHKQPYD	O00141_S255	MTVKTEAAKGTLTYSRMRGMVAILIAFMKQRRMGLNDFIQKIANNS...	1	SGK1 SGK	AGC	SGK	Basophilic	Akt/rock	SGK1	1
3	O00141_O00141_S397	O00141	O00141	S397	Sugiyama	SGK1 SGK	MTVKTEAAKGTLTYSRMRGMVAILIAFMKQRRMGLNDFIQKIANNS...	397	sGPNDLRHFDPEFTEEPVPNsIGKsPDsVLVTAsVKEAAEA	O00141_S397	MTVKTEAAKGTLTYSRMRGMVAILIAFMKQRRMGLNDFIQKIANNS...	1	SGK1 SGK	AGC	SGK	Basophilic	Akt/rock	SGK1	1
4	O00141_O00141_S404	O00141	O00141	S404	Sugiyama	SGK1 SGK	MTVKTEAAKGTLTYSRMRGMVAILIAFMKQRRMGLNDFIQKIANNS...	404	HFDPEFTEEPVPNsIGKsPDsVLVTAsVKEAAEAFLGFsYA	O00141_S404	MTVKTEAAKGTLTYSRMRGMVAILIAFMKQRRMGLNDFIQKIANNS...	1	SGK1 SGK	AGC	SGK	Basophilic	Akt/rock	SGK1	1

Total kinase count per site

pivot = pd.crosstab(df['sub_site'], df['substrate_uniprot'])

hist_data = pivot.sum(1)  # total kinase count per sub_site

# Define bins and labels
bins = [0, 1, 10, 100, 300]
labels = ['1', '2~10', '11~100', '101~300']

# Cut into categories
binned = pd.cut(hist_data, bins=bins, labels=labels, right=True, include_lowest=True)

# Count how many kin_sub_sites fall into each bin
binned_counts = binned.value_counts().sort_index()

sites = pd.concat([hist_data,binned],axis=1)

sites.columns=['num_kin','bin']

sites = sites.reset_index()

Plot sites kin distribution

# pip install brokenaxes

import matplotlib.pyplot as plt
from brokenaxes import brokenaxes

import pandas as pd
import numpy as np

set_sns(100)

fig = plt.figure(figsize=(5, 4))
bax = brokenaxes(ylims=((0, 5000), (20_000, 22_000)), hspace=0.2)

# Plot histogram
bax.hist(sites.num_kin, bins=100, edgecolor='black');

# bax.set_xlabel('# Kinases')
# bax.set_ylabel('Frequency') # overlap, does not work very well
plt.title('Histogram of # Kinases per Substrate Site');
save_pdf('fig/promi_hist.pdf')

cnt=sites.bin.value_counts()

def plot_pie(value_counts, # value counts
             hue_order=None, # list of strings
             labeldistance=0.8,
             fontsize=12,
             fontcolor='black',
             palette='tab20' ,
             figsize=(4,3)
            ):
    if hue_order is not None: value_counts = value_counts.reindex(hue_order)
    colors = sns.color_palette(palette, n_colors=len(value_counts))
    value_counts.plot.pie(
        autopct='%1.1f%%',    # Show percentage inside slices
        labeldistance=labeldistance,    # Move labels closer to center
        textprops={'fontsize': fontsize, 'color': fontcolor} ,
        colors=colors,
        figsize=figsize,
    )
    plt.ylabel('')
    plt.title(f'n={value_counts.sum():,}')

plot_pie(cnt,
         fontsize=9,labeldistance=1,palette='Pastel1')
save_pdf('fig/promi_pie.pdf')

def plot_cnt(cnt, xlabel=None,ylabel='Count',figsize=(6, 3)):
    fig, ax = plt.subplots(figsize=figsize)
    cnt.plot.bar(ax=ax)
    # Add text on top of each bar
    for idx, value in enumerate(cnt):
        ax.text(idx, value + 0.5, f"{value:,}", ha='center', va='bottom', fontsize=10)

    ax.spines['top'].set_visible(False)
    ax.spines['right'].set_visible(False)
    ax.set_ylabel(ylabel)
    ax.set_xlabel(xlabel)
    plt.xticks(rotation=0)
    plt.tight_layout()

plot_cnt(cnt,
         xlabel='# Kinases per Substrate Site',
         ylabel='# Substrate Sites',
         figsize=(5,2.4)
        )
save_pdf('fig/promi_bar.pdf')

# sites.num_kin.sort_values(ascending=False).to_csv('test.csv')

Characterize promicuous motif

seqs = Data.get_ks_unique()

seqs

	sub_site	num_kin	bin	sub_genes	site_seq	source_combine	acceptor	O00141_SGK1	O00238_BMPR1B	O00311_CDC7	...	Q9Y2K2_SIK3	Q9Y2U5_MAP3K2	Q9Y3S1_WNK2	Q9Y463_DYRK1B	Q9Y4K4_MAP4K5	Q9Y572_RIPK3	Q9Y5S2_CDC42BPB	Q9Y6E0_STK24	Q9Y6M4_CSNK1G3	Q9Y6R4_MAP3K4
0	A0A2R8Y4L2_S158	1	1	HNRNPA1L3 HNRNPA1P48	TDRGSGKKRGFAFVTFDDHDsVDKIVIQKYHTVNGHNCEVR	Sugiyama	S	0	0	0	...	0	0	0	0	0	0	0	0	0	0
1	A0A2R8Y4L2_S22	3	2~10	HNRNPA1L3 HNRNPA1P48	SKSEsPKEPEQLRKLFIGGLsFEtTDESLRSHFEQWGTLTD	Sugiyama	S	0	0	0	...	0	0	0	0	0	0	0	0	0	0
2	A0A2R8Y4L2_S6	3	2~10	HNRNPA1L3 HNRNPA1P48	_______________MSKSEsPKEPEQLRKLFIGGLsFEtT	Sugiyama	S	0	0	0	...	0	0	0	0	0	0	0	0	0	0
3	A0A2R8Y4L2_S95	65	11~100	HNRNPA1L3 HNRNPA1P48	RPHKVDGRVVEPKRAVSREDsQRPDAHLTVKKIFVGGIKED	Sugiyama	S	0	1	0	...	0	1	0	0	1	0	0	0	0	0
4	A0A2R8Y4L2_T25	3	2~10	HNRNPA1L3 HNRNPA1P48	EsPKEPEQLRKLFIGGLsFEtTDESLRSHFEQWGTLTDCVV	Sugiyama	T	0	0	0	...	0	0	0	0	0	0	0	0	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
29806	Q9Y6Y8_S894	2	2~10	SEC23IP MSTP053	FISSLKSAWQTLNEFARAHtsstQLQEELEKVANQIKEEEE	Sugiyama	S	0	0	0	...	0	0	0	0	0	0	0	0	0	0
29807	Q9Y6Y8_T893	4	2~10	SEC23IP MSTP053	GFISSLKSAWQTLNEFARAHtsstQLQEELEKVANQIKEEE	Sugiyama	T	0	0	0	...	0	0	0	0	0	0	0	0	0	0
29808	Q9Y6Y8_Y935	2	2~10	SEC23IP MSTP053	KQVVEAEKVVEsPDFsKDEDyLGKVGMLNGGRRIDYVLQEK	Sugiyama	Y	0	0	0	...	0	0	0	0	0	0	0	0	0	0
29809	Q9Y6Y9_Y131	1	1	LY96 ESOP1 MD2	ETVNTTISFSFKGIKFSKGKyKCVVEAISGSPEEMLFCLEF	Non-Sugiyama	Y	0	0	0	...	0	0	0	0	0	0	0	0	0	0
29810	Q9Y6Y9_Y22	1	1	LY96 ESOP1 MD2	LPFLFFSTLFSSIFTEAQKQyWVCNSSDASISYTYCDKMQY	Non-Sugiyama	Y	0	0	0	...	0	0	0	0	0	0	0	0	0	0

29811 rows × 462 columns

seq_map=df.drop_duplicates('sub_site').set_index('sub_site').site_seq

gene_map = df.drop_duplicates('sub_site').set_index('sub_site').substrate_genes

sites['sub_genes'] = sites.sub_site.map(gene_map)
sites['site_seq'] = sites.sub_site.map(seq_map)

for b in sites.bin.value_counts().index:
    sites_b=sites[sites.bin==b].copy()
    pssm_df= get_prob(sites_b,'site_seq')
    plot_logo(pssm_df,b,figsize=(13,2))
    kin_str='kinase' if b=='1' else 'kinases'
    plt.title(f"Sites with {b} {kin_str} (n={len(sites_b)})")
    plt.show()
    plt.close()

# a = sites[(sites.bin=='101~300')|(sites.bin=='11~100')].copy()

# s_sites = a[a.sub_site.str.split('_').str[1].str[0]=='S']

# y_sites = a[a.sub_site.str.split('_').str[1].str[0]=='Y']

# pssm_df_s = get_prob(s_sites,'site_seq')
# pssm_df_y = get_prob(y_sites,'site_seq')

Genes in promicuous site

sites['gene'] = sites.sub_genes.str.split(' ').str[0]

sites_b=sites[sites.bin=='101~300'].copy()

data = pd.read_csv('raw/genes_grouped.csv')

data.Gene.isin(genes).sum()

# remove (small) and (large) for rebosomal protein
data.Group = data.Group.str.split('(').str[0]

group_map = data.set_index('Gene')['Group'].to_dict()

sites_b['gene_group'] = sites_b.gene.map(group_map)

sites_b.gene_group.value_counts()

gene_group
Glycolysis                 16
Actin cytoskeleton         15
Heat shock protein         12
Ribosomal protein          12
RNA‑binding protein         6
POTE family                 3
NME family                  2
Signaling/regulatory        2
Tubulin cytoskeleton        2
Cytoskeleton‑associated     1
Regulatory/other            1
Name: count, dtype: int64

Stacked by source

def convert_source(x):
    if x == "Sugiyama":
        return x
    elif 'Sugiyama' in x and '|' in x:
        return 'Both'
    elif 'Sugiyama' not in x:
        return 'Non-Sugiyama'

# df=Data.get_ks_dataset()

df['source2'] = df.source.apply(convert_source)

out = df.groupby('sub_site')['source2'].unique()

sites['source'] = sites.sub_site.map(out)

def combine_source(sources):
    sources = set(sources)  # remove duplicates
    if sources == {'Sugiyama'}:
        return 'Sugiyama'
    elif sources == {'Non-Sugiyama'}:
        return 'Non-Sugiyama'
    else:
        return 'Both'

sites['source_num'] = sites.source.str.len()

sites['source_combine'] = sites.source.apply(combine_source)

def get_pct(df,bin_col, hue_col):
    count_df = df.groupby([bin_col, hue_col], observed=False).size().unstack(fill_value=0)
    pct_df = count_df.div(count_df.sum(axis=1), axis=0) * 100
    return pct_df

pct_df = get_pct(sites,'bin','source_combine')

pct_df

source_combine	Both	Non-Sugiyama	Sugiyama
bin
1	1.102740	47.445205	51.452055
2~10	7.470243	30.582697	61.947060
11~100	7.137336	1.056429	91.806236
101~300	12.500000	0.000000	87.500000

# pct_df.plot.bar()
# plt.legend(title='Source')

def get_plt_color(palette, # dict, list, or set name (tab10)
                  columns, # columns in the df for plot
                 ):
    "Given a dict, list or set name, return the list of names; if dict, need to provide column names of the df."
    if isinstance(palette, dict):
        # Match colors to column order in pct_df
        colors = [palette.get(col, '#cccccc') for col in columns]  # fallback color if missing
    elif isinstance(palette, str):
        colors = sns.color_palette(palette, n_colors=len(columns))
    elif isinstance(palette, list):
        colors = palette
    return colors

get_plt_color('Set2',['a','b'])

import matplotlib.pyplot as plt

def plot_composition(df, bin_col, hue_col,palette='tab20',legend_title=None,rotate=45,xlabel=None,ylabel='Percentage',figsize=(5,3)):
    pct_df = get_pct(df,bin_col,hue_col)

    colors = get_plt_color(palette,pct_df.columns)
    
    pct_df.plot(kind='bar', figsize=figsize,stacked=True,color=colors)
    
    plt.ylabel(ylabel)
    plt.xlabel(xlabel)
    plt.xticks(rotation=rotate)
    if legend_title is None: legend_title = hue_col 
    plt.legend(title=legend_title, bbox_to_anchor=(1.05, 1), loc='upper left')

plot_composition(sites,
                 'bin',
                 'source_combine',
                 palette='Set2',
                 legend_title='Source',
                 figsize=(3,3)
                )
save_pdf('fig/promi_bar_source_percentage.pdf')

Stacked plot by site type

sites['acceptor'] = sites.sub_site.str.split('_').str[1].str[0]

plot_composition(sites,'bin',
                 'acceptor',
                 palette=sty_color,
                 figsize=(3,3),
                 legend_title='Acceptor'
                )

save_pdf('fig/promi_bar_site_percentage.pdf')

pct_df=get_pct(sites,'bin','acceptor')

pct_df

acceptor	S	T	Y
bin
1	57.760274	24.431507	17.808219
2~10	55.809202	22.712738	21.478060
11~100	31.512497	16.361762	52.125741
101~300	58.333333	4.166667	37.500000

fig, axes = plt.subplots(3, 1, figsize=(5, 4.6), sharex=True)
for i, (ax, acc) in enumerate(zip(axes, ['S', 'T', 'Y'])):
    pct_df[acc].plot(kind='bar', ax=ax, color=sty_color[acc])
    ax.set_title(f'{acc} Sites')
    ax.grid(axis='y')
    ax.tick_params(axis='x', labelrotation=0)
    
    # Set ylabel only on the middle plot
    if i == 1:
        ax.set_ylabel('% of Total Substrate Sites Per Bin')
    else:
        ax.set_ylabel('')  # remove label from top/bottom

axes[-1].set_xlabel('Number of Kinases Per Substrate Site')
plt.subplots_adjust(hspace=0.3)
save_pdf('fig/promi_bar_site_percentage_split.pdf')

Save & Add num_kin to the ks_dataset

sites = sites.drop(columns='source')

Let’s add kinase binding info as one-hot

df['kinase_uniprot_gene']=df['kinase_uniprot']+'_'+df['kinase_genes'].str.split(' ').str[0]

pivot = pd.crosstab(df['sub_site'],df['kinase_uniprot_gene'])

pivot = pivot.reset_index()

sites = sites.merge(pivot)

sites.head()

	sub_site	num_kin	bin	sub_genes	site_seq	source_combine	acceptor	O00238_BMPR1B	...	Q9Y2U5_MAP3K2	Q9Y4K4_MAP4K5
0	A0A2R8Y4L2_S158	1	1	HNRNPA1L3 HNRNPA1P48	TDRGSGKKRGFAFVTFDDHDsVDKIVIQKYHTVNGHNCEVR	Sugiyama	S	0	...	0	0
1	A0A2R8Y4L2_S22	3	2~10	HNRNPA1L3 HNRNPA1P48	SKSEsPKEPEQLRKLFIGGLsFEtTDESLRSHFEQWGTLTD	Sugiyama	S	0	...	0	0
2	A0A2R8Y4L2_S6	3	2~10	HNRNPA1L3 HNRNPA1P48	_______________MSKSEsPKEPEQLRKLFIGGLsFEtT	Sugiyama	S	0	...	0	0
3	A0A2R8Y4L2_S95	65	11~100	HNRNPA1L3 HNRNPA1P48	RPHKVDGRVVEPKRAVSREDsQRPDAHLTVKKIFVGGIKED	Sugiyama	S	1	...	1	1
4	A0A2R8Y4L2_T25	3	2~10	HNRNPA1L3 HNRNPA1P48	EsPKEPEQLRKLFIGGLsFEtTDESLRSHFEQWGTLTDCVV	Sugiyama	T	0	...	0	0

5 rows × 462 columns

# sites.to_parquet('out/unique_ks_sites.parquet')

Sites data is available upon ‘Data.get_ks_unique’

The num_kin info is added to ks_dataset in Data