Core

Core functions in Katlas library

Setup

Data

We will go through how to load kinase information data and phosphorylation sites data.

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Data

 Data ()

A class for fetching various datasets.

Datasets used in this study can be accessed through Data

Data	Description
get_kinase_info	523 full kinase information
get_pspa_tyr_norm	PSPA Tyr kinase normalized data; with additional 0 position information
get_pspa_st_norm	PSPA ST kinase normalized data; with additional 0 position information
get_pspa_all_norm	PSPA ST and Tyr kinase normalized data; with additional 0 position information
get_pspa_st_pct	PSPA ST kinase scoring on ST phosphoproteome; reference for percentile calculation
get_pspa_tyr_pct	PSPA Tyr kinase scoring on Tyr phosphoproteome; reference for percentile calculation
get_num_dict	dictionary of number of random amino acids for PSPA
get_ks_dataset	kinase-substrate dataset
get_cddm	CDDM PSSM
get_cddm_upper	CDDM PSSM for upper case
get_cddm_others	CDDM PSSM of mutated kinase
get_cddm_others_info	information of mutated kinase
get_combine	combined CDDM and PSPA data
get_aa_info	amino acid information
get_aa_rdkit	chemical properties extracted from amino acids’ smiles through Rdkit
get_aa_morgan	morgan fingerprints of amino acids’ smiles
get_cptac_ensembl_site	CPTAC unique ensembl ID + site
get_cptac_unique_site	CPTAC unique site sequence
get_cptac_gene_site	CPTAC unique gene+site, fewer cases than ensembl ID + site
get_psp_human_site	PhosphoSitePlus, human dataset, gene+site
get_ochoa_site	Ochoa et al. human phosphoproteom dataset
get_combine_site_psp_ochoa	combined Ochoa and PSP low throughput data

To load kinase information data:

kinase = Data.get_kinase_info()
kinase.head()

	kinase	ID_coral	uniprot	ID_HGNC	group	family	subfamily_coral	subfamily	in_ST_paper	in_Tyr_paper	...	cytosol	cytoskeleton	plasma membrane	mitochondrion	Golgi apparatus	endoplasmic reticulum	vesicle	centrosome	aggresome	main_location
0	AAK1	AAK1	Q2M2I8	AAK1	Other	NAK	None	NAK	1	0	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	None
1	ABL1	ABL1	P00519	ABL1	TK	Abl	None	Abl	0	1	...	6.0	NaN	4.0	NaN	NaN	NaN	NaN	NaN	NaN	cytosol
2	ABL2	ABL2	P42684	ABL2	TK	Abl	None	Abl	0	1	...	4.0	6.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	cytoskeleton
3	TNK2	ACK	Q07912	TNK2	TK	Ack	None	Ack	0	1	...	NaN	NaN	NaN	NaN	NaN	NaN	8.0	NaN	2.0	vesicle
4	ACVR2A	ACTR2	P27037	ACVR2A	TKL	STKR	STKR2	STKR2	1	0	...	5.0	NaN	NaN	NaN	NaN	5.0	NaN	NaN	NaN	cytosol

5 rows × 30 columns

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CPTAC

 CPTAC ()

A class for fetching CPTAC phosphoproteomics data.

To check available cancer types, use CPTAC.list_cancer()

CPTAC.list_cancer()

['HNSCC', 'GBM', 'COAD', 'CCRCC', 'LSCC', 'BRCA', 'UCEC', 'LUAD', 'PDAC', 'OV']

To load CPTAC phosphorylation site information, use CPTAC.get_id(). Fold change of various conditions can be acquired through LinkedOmics or LinkedOmicsKB. Use is_KB to indicate whether the phosphorylation site information is for LinkedOmics or LinkedOmicsKB.

# Example of getting phosphorylation site information
tumor = CPTAC.get_id('CCRCC',is_KB=True)
normal = CPTAC.get_id('CCRCC',is_KB=True, is_Tumor=False)
tumor.head()

the CCRCC dataset length is: 54238
after id mapping, the length is 213737
0 sites does not have a mapped gene name
after removing duplicates of protein_site, the length is 212814
the CCRCC dataset length is: 53152
after id mapping, the length is 209188
0 sites does not have a mapped gene name
after removing duplicates of protein_site, the length is 208298

	gene	site	site_seq	protein	gene_name	gene_site	protein_site
0	ENSG00000003056.8	S267	DDQLGEESEERDDHL	ENSP00000000412.3	M6PR	M6PR_S267	ENSP00000000412_S267
1	ENSG00000003056.8	S267	DDQLGEESEERDDHL	ENSP00000440488.2	M6PR	M6PR_S267	ENSP00000440488_S267
2	ENSG00000048028.11	S1053	PPTIRPNSPYDLCSR	ENSP00000003302.4	USP28	USP28_S1053	ENSP00000003302_S1053
3	ENSG00000048028.11	S1053	PPTIRPNSPYDLCSR	ENSP00000445743.1	USP28	USP28_S1053	ENSP00000445743_S1053
4	ENSG00000048028.11	S1053	PPTIRPNSPYDLCSR	ENSP00000442431.1	USP28	USP28_S1053	ENSP00000442431_S1053

Substrate scoring

Utils

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convert_string

 convert_string (input_string:str)

*Convert amino acids of lower case other than s,t,y to capital; convert rare amino acids to _*

In many phosphorylation datsets, there are amino acids in the site sequence that are in lower case but does not belong to s/t/y. Also, there are uncommon amino acids such as U or O that appear in the sequence. Therefore, it is essential to convert the sequence string for kinase ranking.

# example
convert_string('AAkUuPRFstTH')

'AAK__PRFstTH'

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STY2sty

 STY2sty (input_string:str)

Replace ‘STY’ with ‘sty’

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checker

 checker (input_string)

Check if the input string contains non-s/t/y at the middle position

checker('AAkUuPSFstTH') # if the center amino acid does not belong to sty/STY, will raise an error

STY2sty('AAkUuPSFSTtH') # convert all capital STY to sty in a string

'AAkUuPsFsttH'

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cut_seq

 cut_seq (input_string:str, min_position:int, max_position:int)

Extract sequence based on a range relative to its center position

	Type	Details
input_string	str	site sequence
min_position	int	minimum position relative to its center
max_position	int	maximum position relative to its center

cut_seq('AAkUuPSFSTtH',-5,4)

'AkUuPSFSTt'

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get_dict

 get_dict (input_string:str)

Get a dictionary of input string; no need for the star in the middle; make sure it is 15 or 10 length

	Type	Details
input_string	str	phosphorylation site sequence

cols = get_dict("PSVEPPLsQETFSDL")
cols

['-7P',
 '-6S',
 '-5V',
 '-4E',
 '-3P',
 '-2P',
 '-1L',
 '0s',
 '1Q',
 '2E',
 '3T',
 '4F',
 '5S',
 '6D',
 '7L']

Function1 - multiply

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multiply_func

 multiply_func (values, factor=17)

Multiply the possibilities of the amino acids at each position in a phosphorylation site

	Type	Default	Details
values			list of values, possibilities of amino acids at certain positions
factor	int	17	scale factor

image.png

The function implement formula from Johnson et al. Nature: An atlas of substrate specificities for the human serine/threonine kinome, Supplementary Note2 (page 160)

Multiply class, consider the dynamics of scale factor

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multiply

 multiply (values, kinase, num_dict={'SYK': 18, 'PTK2': 18, 'ZAP70': 18,
           'ERBB2': 18, 'CSK': 18, 'FGFR4': 18, 'EGFR': 18, 'ERBB4': 18,
           'EPHA8': 18, 'EPHA7': 18, 'EPHA5': 18, 'EPHA2': 18, 'EPHB2':
           18, 'EPHB1': 18, 'EPHB3': 18, 'EPHB4': 18, 'EPHA4': 18,
           'EPHA3': 18, 'EPHA6': 18, 'FRK': 18, 'EPHA1': 18, 'TEC': 18,
           'BTK': 18, 'ITK': 18, 'BMX': 18, 'TXK': 16, 'ABL2': 18, 'ABL1':
           18, 'SRMS': 18, 'PTK2B': 18, 'FER': 18, 'MERTK': 18, 'AXL': 18,
           'FES': 18, 'PTK6': 18, 'YES1': 18, 'FGR': 18, 'SRC': 18, 'FYN':
           18, 'LCK': 18, 'BLK': 18, 'LYN': 18, 'HCK': 18, 'PDGFRB': 18,
           'PDGFRA': 18, 'FLT3': 18, 'TYRO3': 18, 'ROS1': 18, 'TEK': 18,
           'LTK': 18, 'ALK': 18, 'MUSK': 18, 'KIT': 18, 'CSF1R': 18,
           'MET': 18, 'KDR': 18, 'RET': 18, 'MST1R': 16, 'JAK3': 16,
           'FLT1': 16, 'MATK': 18, 'FGFR3': 18, 'FGFR2': 18, 'FGFR1': 18,
           'FLT4': 18, 'INSR': 18, 'IGF1R': 18, 'INSRR': 16, 'NTRK3': 18,
           'NTRK1': 18, 'NTRK2': 18, 'TNK1': 18, 'TNK2': 18, 'DDR2': 18,
           'DDR1': 18, 'TYK2': 18, 'JAK2': 18, 'JAK1': 18, 'TNNI3K_TYR':
           18, 'NEK10_TYR': 16, 'PINK1_TYR': 16, 'MAP2K7_TYR': 16,
           'PKMYT1_TYR': 16, 'TESK1_TYR': 16, 'LIMK1_TYR': 16,
           'LIMK2_TYR': 16, 'WEE1_TYR': 18, 'MAP2K6_TYR': 16,
           'MAP2K4_TYR': 16, 'PDHK1_TYR': 16, 'BMPR2_TYR': 16,
           'PDHK4_TYR': 16, 'PDHK3_TYR': 16, 'AAK1': 17, 'ACVR2A': 17,
           'ACVR2B': 17, 'AKT1': 17, 'AKT2': 17, 'AKT3': 17, 'ALK2': 17,
           'ALK4': 17, 'ALPHAK3': 17, 'AMPKA1': 17, 'AMPKA2': 17,
           'ANKRD3': 17, 'ATM': 17, 'ATR': 17, 'AURA': 17, 'AURB': 17,
           'AURC': 17, 'GRK2': 17, 'GRK3': 17, 'BCKDK': 17, 'BIKE': 17,
           'BMPR1A': 17, 'BMPR1B': 17, 'BMPR2': 17, 'BRAF': 17, 'BRSK1':
           17, 'BRSK2': 17, 'BUB1': 17, 'CAMK1A': 17, 'CAMK1B': 17,
           'CAMK1D': 17, 'CAMK1G': 17, 'CAMK2A': 17, 'CAMK2B': 17,
           'CAMK2D': 17, 'CAMK2G': 17, 'CAMK4': 17, 'CAMKK1': 17,
           'CAMKK2': 17, 'CAMLCK': 17, 'CDK1': 17, 'CDC7': 17, 'CDK10':
           17, 'CDK19': 17, 'CDK2': 17, 'CDK3': 17, 'CDK4': 17, 'CDK5':
           17, 'CDK6': 17, 'CDK7': 17, 'CDK8': 17, 'CDK9': 17, 'CDKL1':
           17, 'CDKL5': 17, 'CHAK1': 17, 'CHAK2': 17, 'CDK13': 17, 'CHK1':
           17, 'CHK2': 17, 'CK1A': 17, 'CK1A2': 17, 'CK1D': 17, 'CK1E':
           17, 'CK1G1': 17, 'CK1G2': 17, 'CK1G3': 17, 'CK2A1': 17,
           'CK2A2': 17, 'CLK1': 17, 'CLK2': 17, 'CLK3': 17, 'CLK4': 17,
           'COT': 17, 'CRIK': 17, 'CDK12': 17, 'DAPK1': 17, 'DAPK2': 17,
           'DAPK3': 17, 'DCAMKL1': 17, 'DCAMKL2': 17, 'DLK': 17, 'DMPK1':
           17, 'DNAPK': 17, 'DRAK1': 17, 'DYRK1A': 17, 'DYRK1B': 17,
           'DYRK2': 17, 'DYRK3': 17, 'DYRK4': 17, 'ERK1': 17, 'ERK2': 17,
           'ERK5': 17, 'ERK7': 17, 'MTOR': 17, 'GAK': 17, 'GCK': 17,
           'GCN2': 17, 'GRK4': 17, 'GRK5': 17, 'GRK6': 17, 'GRK7': 17,
           'GSK3A': 17, 'GSK3B': 17, 'HASPIN': 17, 'HGK': 17, 'HIPK1': 17,
           'HIPK2': 17, 'HIPK3': 17, 'HIPK4': 17, 'HPK1': 17, 'HRI': 17,
           'HUNK': 17, 'ICK': 17, 'IKKA': 17, 'IKKB': 17, 'IKKE': 17,
           'IRAK1': 17, 'IRAK4': 17, 'IRE1': 17, 'IRE2': 17, 'JNK1': 17,
           'JNK2': 17, 'JNK3': 17, 'KHS1': 17, 'KHS2': 17, 'KIS': 17,
           'LATS1': 17, 'LATS2': 17, 'LKB1': 17, 'LOK': 17, 'LRRK2': 17,
           'MAK': 17, 'MEK1': 17, 'MEK2': 17, 'MEK5': 17, 'MEKK1': 17,
           'YSK4': 17, 'MEKK2': 17, 'MEKK3': 17, 'ASK1': 17, 'MEKK6': 17,
           'MAP3K15': 17, 'MAPKAPK2': 17, 'MAPKAPK3': 17, 'MAPKAPK5': 17,
           'MARK1': 17, 'MARK2': 17, 'MARK3': 17, 'MARK4': 17, 'MASTL':
           17, 'MELK': 17, 'MINK': 17, 'MLK1': 17, 'MLK2': 17, 'MLK3': 17,
           'MLK4': 17, 'MNK1': 17, 'MNK2': 17, 'MOK': 17, 'MOS': 17,
           'MPSK1': 17, 'MRCKA': 17, 'MRCKB': 17, 'MSK1': 17, 'MSK2': 17,
           'SRPK3': 17, 'MST1': 17, 'MST2': 17, 'MST3': 17, 'MST4': 17,
           'MYO3A': 17, 'MYO3B': 17, 'NDR1': 17, 'NDR2': 17, 'NEK1': 17,
           'NEK11': 17, 'NEK2': 17, 'NEK3': 17, 'NEK4': 17, 'NEK5': 17,
           'NEK6': 17, 'NEK7': 17, 'NEK8': 17, 'NEK9': 17, 'NIK': 17,
           'NIM1': 17, 'NLK': 17, 'NUAK1': 17, 'NUAK2': 17, 'OSR1': 17,
           'P38A': 17, 'P38B': 17, 'P38D': 17, 'P38G': 17, 'P70S6K': 17,
           'P70S6KB': 17, 'PAK1': 17, 'PAK2': 17, 'PAK3': 17, 'PAK4': 17,
           'PAK5': 17, 'PAK6': 17, 'PASK': 17, 'PBK': 17, 'CDK16': 17,
           'CDK17': 17, 'CDK18': 17, 'PDHK1': 16, 'PDHK4': 16, 'PDK1': 17,
           'PERK': 17, 'CDK14': 17, 'PHKG1': 17, 'PHKG2': 17, 'PIM1': 17,
           'PIM2': 17, 'PIM3': 17, 'PINK1': 17, 'PKACA': 17, 'PKACB': 17,
           'PKACG': 17, 'PKCA': 17, 'PKCB': 17, 'PKCD': 17, 'PKCE': 17,
           'PKCG': 17, 'PKCH': 17, 'PKCI': 17, 'PKCT': 17, 'PKCZ': 17,
           'PRKD1': 17, 'PRKD2': 17, 'PRKD3': 17, 'PKG1': 17, 'PKG2': 17,
           'PKN1': 17, 'PKN2': 17, 'PKN3': 17, 'PKR': 17, 'PLK1': 17,
           'PLK2': 17, 'PLK3': 17, 'PLK4': 17, 'PRKX': 17, 'PRP4': 17,
           'PRPK': 17, 'QIK': 17, 'QSK': 17, 'RAF1': 17, 'GRK1': 17,
           'RIPK1': 17, 'RIPK2': 17, 'RIPK3': 17, 'ROCK1': 17, 'ROCK2':
           17, 'P90RSK': 17, 'RSK2': 17, 'RSK3': 17, 'RSK4': 17, 'SBK':
           17, 'MYLK4': 17, 'SGK1': 17, 'SGK3': 17, 'DSTYK': 17, 'SIK':
           17, 'SKMLCK': 17, 'SLK': 17, 'SMG1': 17, 'SMMLCK': 17, 'SNRK':
           17, 'SRPK1': 17, 'SRPK2': 17, 'SSTK': 17, 'STK33': 17, 'STLK3':
           17, 'TAK1': 17, 'TAO1': 17, 'TAO2': 17, 'TAO3': 17, 'TBK1': 17,
           'TGFBR1': 17, 'TGFBR2': 17, 'TLK1': 17, 'TLK2': 17, 'TNIK': 17,
           'TSSK1': 17, 'TSSK2': 17, 'TTBK1': 17, 'TTBK2': 17, 'TTK': 17,
           'ULK1': 17, 'ULK2': 17, 'VRK1': 17, 'VRK2': 17, 'WNK1': 17,
           'WNK3': 17, 'WNK4': 17, 'YANK2': 17, 'YANK3': 17, 'YSK1': 17,
           'ZAK': 17, 'EEF2K': 17, 'FAM20C': 17})

Multiply values, consider the dynamics of scale factor, which is PSPA random aa number.

multiply(values=[1,2,3,4,5],kinase='PDHK1')

22.906890595608516

Function2 - sum up

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sumup

 sumup (values, kinase=None)

Sum up the possibilities of the amino acids at each position in a phosphorylation site sequence

	Type	Default	Details
values			list of values, possibilities of amino acids at certain positions
kinase	NoneType	None

image.png

Substrate scoring for one input string

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predict_kinase

 predict_kinase (input_string:str, ref:pandas.core.frame.DataFrame,
                 func:Callable, to_lower:bool=False, to_upper:bool=False,
                 verbose=True)

Predict kinase given a phosphorylation site sequence

	Type	Default	Details
input_string	str		site sequence
ref	DataFrame		reference dataframe for scoring
func	Callable		function to calculate score
to_lower	bool	False	convert capital STY to lower case
to_upper	bool	False	convert all letter to uppercase
verbose	bool	True

Here we provide different PSSM settings from either PSPA data or kinase-substrate dataset for kinase prediction:

predict_kinase("GAEEKEyHAEGGA", **param_PSPA).dropna()

considering string: ['-5A', '-4E', '-3E', '-2K', '-1E', '0y', '1H', '2A', '3E', '4G', '5G']

kinase
EGFR     2.881
CSK      2.148
ZAP70    1.997
FGFR4    1.918
MATK     1.778
         ...  
EPHA1   -3.486
TNK1    -4.211
FES     -4.315
TNK2    -4.595
DDR2    -5.467
Length: 93, dtype: float64

for param in [param_PSPA, param_CDDM,param_CDDM_upper]:
    print(predict_kinase("PSVEPPLsQETFSDL",**param).head())

considering string: ['-5V', '-4E', '-3P', '-2P', '-1L', '0s', '1Q', '2E', '3T', '4F', '5S']
kinase
ATM       5.037
SMG1      4.385
DNAPK     3.818
ATR       3.507
FAM20C    3.170
dtype: float64
considering string: ['-7P', '-6S', '-5V', '-4E', '-3P', '-2P', '-1L', '0s', '1Q', '2E', '3T', '4F', '5S', '6D', '7L']
kinase
ATR      3.064
ATM      2.909
DNAPK    2.270
CK2A1    1.873
TSSK1    1.856
dtype: float64
considering string: ['-7P', '-6S', '-5V', '-4E', '-3P', '-2P', '-1L', '0S', '1Q', '2E', '3T', '4F', '5S', '6D', '7L']
kinase
ATR      3.229
ATM      3.038
DNAPK    2.479
CK2A1    2.006
CDK8     1.999
dtype: float64

Substrates scoring for a column of input strings

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predict_kinase_df

 predict_kinase_df (df, seq_col, ref, func, to_lower=False,
                    to_upper=False)

df = Data.get_psp_human_site()
df_sty = df[df['site_seq'].str[7].isin(list('sty'))]

out = predict_kinase_df(df_sty.head(20_000),'site_seq', **param_PSPA)

input dataframe has a length 20000
Preprocessing
Finish preprocessing
Merging reference
Finish merging

100%|██████████| 396/396 [00:17<00:00, 23.17it/s]

CPU times: user 18.8 s, sys: 76.4 ms, total: 18.9 s
Wall time: 19.1 s

out_cddm = predict_kinase_df(df_sty.head(20_000),'site_seq', **param_CDDM)

input dataframe has a length 20000
Preprocessing
Finish preprocessing
Merging reference
Finish merging
CPU times: user 2.26 s, sys: 16 ms, total: 2.27 s
Wall time: 2.27 s

Other examples:

data = Data.get_ochoa_site().head()

for param in [param_PSPA,param_PSPA_st,param_PSPA_y, param_CDDM,param_CDDM_upper]:
    display(predict_kinase_df(data,'site_seq', **param))

input dataframe has a length 5
Preprocessing
Finish preprocessing

100%|██████████| 396/396 [00:01<00:00, 278.67it/s]

	AAK1	ACVR2A	ACVR2B	AKT1	AKT2	AKT3	ALK2	ALK4	ALPHAK3	AMPKA1	...	NTRK3	TXK	TYK2	TYRO3	FLT1	KDR	FLT4	WEE1_TYR	YES1	ZAP70
0	-10.959900	-0.580989	0.328868	-3.890850	-3.590503	-5.312027	0.813786	-0.559382	-0.932736	-2.607226	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1	-6.787549	-0.166112	0.306884	-5.885999	-4.786083	-6.575957	1.561436	-0.865154	-3.399169	-3.260798	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2	-9.030938	1.231686	1.774661	-6.164209	-5.446345	-8.329813	0.777783	-1.355417	-0.928937	-4.998190	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
3	-4.849113	2.271636	2.057240	-2.886034	-2.379830	-3.634907	1.547144	2.735341	-2.825794	-1.696886	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
4	-6.596842	-1.387696	-0.956218	-2.834231	-3.794276	-4.968521	-1.862002	-1.717226	-2.653170	-3.514512	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

5 rows × 396 columns

input dataframe has a length 5
Preprocessing
Finish preprocessing

100%|██████████| 303/303 [00:01<00:00, 282.00it/s]

	AAK1	ACVR2A	ACVR2B	AKT1	AKT2	AKT3	ALK2	ALK4	ALPHAK3	AMPKA1	...	VRK1	VRK2	WNK1	WNK3	WNK4	YANK2	YANK3	YSK1	YSK4	ZAK
0	-10.959900	-0.580989	0.328868	-3.890850	-3.590503	-5.312027	0.813786	-0.559382	-0.932736	-2.607226	...	-4.682274	-2.854275	-1.668666	-1.526785	-2.964575	-2.877200	-1.792244	-6.282651	-1.715336	-3.204281
1	-6.787549	-0.166112	0.306884	-5.885999	-4.786083	-6.575957	1.561436	-0.865154	-3.399169	-3.260798	...	-5.669906	-2.817085	-4.071192	-3.393741	-5.096516	-1.874000	-1.479976	-8.708810	-3.708147	-6.092628
2	-9.030938	1.231686	1.774661	-6.164209	-5.446345	-8.329813	0.777783	-1.355417	-0.928937	-4.998190	...	-5.832088	-3.243278	-4.249323	-2.749652	-5.053019	0.581384	-0.502630	-6.448245	-1.897494	-2.846533
3	-4.849113	2.271636	2.057240	-2.886034	-2.379830	-3.634907	1.547144	2.735341	-2.825794	-1.696886	...	-2.757951	-1.699232	-1.725384	-0.091196	-0.672545	0.313278	-0.207212	-2.315848	-0.053572	-1.117657
4	-6.596842	-1.387696	-0.956218	-2.834231	-3.794276	-4.968521	-1.862002	-1.717226	-2.653170	-3.514512	...	-1.546328	-1.457323	-1.277532	0.510635	-1.045845	-0.314193	-1.023331	-2.482345	-2.227114	-1.592725

5 rows × 303 columns

input dataframe has a length 5
Preprocessing
Finish preprocessing

100%|██████████| 93/93 [00:00<00:00, 282.04it/s]

	ABL1	TNK2	ALK	ABL2	AXL	BLK	BMPR2_TYR	PTK6	BTK	CSF1R	...	NTRK3	TXK	TYK2	TYRO3	FLT1	KDR	FLT4	WEE1_TYR	YES1	ZAP70
0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
3	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
4	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

5 rows × 93 columns

input dataframe has a length 5
Preprocessing
Finish preprocessing

	SRC	EPHA3	FES	NTRK3	ALK	EPHA8	ABL1	FLT3	EPHB2	FYN	...	MEK5	PKN2	MAP2K7	MRCKB	HIPK3	CDK8	BUB1	MEKK3	MAP2K3	GRK1
0	0.929569	1.023548	0.989176	0.993497	0.949303	1.035864	0.911981	0.931400	0.980941	0.999884	...	1.280109	1.579914	1.585706	1.521548	1.331723	1.705593	1.281669	1.387180	1.419866	1.808546
1	0.831463	0.876808	0.861939	0.868801	0.794678	0.858953	0.772543	0.754167	0.822913	0.863020	...	1.072389	1.323723	1.319648	1.136658	1.507195	1.556711	1.224350	1.111727	1.115820	1.707075
2	0.793592	0.837832	0.783147	0.809343	0.808309	0.834936	0.777472	0.777312	0.788872	0.816794	...	1.234520	1.758183	1.355396	0.983398	1.290056	1.422390	1.260192	1.113435	1.221784	1.724631
3	0.622633	0.652045	0.602943	0.666497	0.638019	0.616664	0.661643	0.618955	0.607654	0.626510	...	0.915427	1.447707	1.443802	1.233698	1.290700	1.162054	1.060876	0.852917	1.219678	1.351096
4	0.653840	0.619648	0.617121	0.645628	0.635812	0.618403	0.635859	0.625696	0.605016	0.683491	...	0.835344	1.212995	1.333055	1.103199	1.280820	1.219769	1.190424	0.901394	0.952642	1.504753

5 rows × 289 columns

input dataframe has a length 5
Preprocessing
Finish preprocessing

	SRC	EPHA3	FES	NTRK3	ALK	EPHA8	ABL1	FLT3	EPHB2	FYN	...	MEK5	PKN2	MAP2K7	MRCKB	HIPK3	CDK8	BUB1	MEKK3	MAP2K3	GRK1
0	0.991760	1.093712	1.051750	1.067134	1.013682	1.097519	0.966379	0.982464	1.054986	1.055910	...	1.314859	1.635470	1.652251	1.622672	1.362973	1.797155	1.305198	1.423618	1.504941	1.872020
1	0.910262	0.953743	0.942327	0.950601	0.872694	0.932586	0.846899	0.826662	0.915020	0.942713	...	1.175454	1.402006	1.430392	1.215826	1.569373	1.716455	1.270999	1.195081	1.223082	1.793290
2	0.849866	0.899910	0.848895	0.879652	0.874959	0.899414	0.839200	0.836523	0.858040	0.867269	...	1.408003	1.813739	1.454786	1.084522	1.352556	1.524663	1.377839	1.173830	1.305691	1.811849
3	0.803826	0.836527	0.800759	0.894570	0.839905	0.781001	0.847847	0.807040	0.805877	0.801402	...	1.110307	1.703637	1.795092	1.469653	1.549936	1.491344	1.446922	1.055452	1.534895	1.741090
4	0.822793	0.796532	0.792343	0.839882	0.810122	0.781420	0.805251	0.795022	0.790380	0.864538	...	1.062617	1.357689	1.485945	1.249266	1.456078	1.422782	1.376471	1.089629	1.121309	1.697524

5 rows × 289 columns

Percentile scoring for one input string

---

source

get_pct

 get_pct (site, ref, func, pct_ref)

Replicate the precentile results from The Kinase Library.

st_pct = Data.get_pspa_st_pct()
y_pct = Data.get_pspa_tyr_pct()

a = get_pct('PSVEPPLyQETFSDL',**param_PSPA_y, pct_ref=y_pct)

considering string: ['-5V', '-4E', '-3P', '-2P', '-1L', '0Y', '1Q', '2E', '3T', '4F', '5S']

a.sort_values('percentile',ascending=False)

	log2(score)	percentile
ABL2	3.137	96.568694
BMX	2.816	96.117567
BTK	1.956	95.693780
CSK	2.303	95.174299
MERTK	2.509	93.588517
...	...	...
FLT1	-1.919	25.358852
PINK1_TYR	-1.227	21.927546
MUSK	-3.031	21.298701
TNNI3K_TYR	-3.549	11.004785
PKMYT1_TYR	-1.739	4.798360

93 rows × 2 columns

get_pct('PSVEPPLsQETFSDL',**param_PSPA_st, pct_ref=st_pct)

considering string: ['-5V', '-4E', '-3P', '-2P', '-1L', '0S', '1Q', '2E', '3T', '4F']

	log2(score)	percentile
ATM	5.037	99.822351
SMG1	4.385	99.831819
DNAPK	3.818	99.205315
ATR	3.507	99.680344
FAM20C	3.170	95.370556
...	...	...
PKN1	-7.275	14.070436
P70S6K	-7.295	4.089816
AKT3	-7.375	11.432995
PKCI	-7.742	8.129511
NEK3	-8.254	4.637240

303 rows × 2 columns

Percentile scoring for a column of input strings

---

source

get_pct_df

 get_pct_df (score_df, pct_ref)

Replicate the precentile results from The Kinase Library.

	Details
score_df	output from predict_kinase_df
pct_ref	a reference df for percentile calculation

score_df = predict_kinase_df(data,'site_seq', **param_PSPA_st)

input dataframe has a length 5
Preprocessing
Finish preprocessing

100%|██████████| 303/303 [00:01<00:00, 262.53it/s]

# substrate score first
score_df = predict_kinase_df(data,'site_seq', **param_PSPA_st)

# get percentile reference
pct_ref = Data.get_pspa_st_pct()

input dataframe has a length 5
Preprocessing
Finish preprocessing

100%|██████████| 303/303 [00:01<00:00, 269.52it/s]

pct = get_pct_df(score_df,pct_ref)
pct

100%|██████████| 303/303 [00:02<00:00, 142.57it/s]

kinase	AAK1	ACVR2A	ACVR2B	AKT1	AKT2	AKT3	ALK2	ALK4	ALPHAK3	AMPKA1	...	VRK1	VRK2	WNK1	WNK3	WNK4	YANK2	YANK3	YSK1	YSK4	ZAK
0	0.458	74.671	84.053	40.272	33.211	33.567	88.644	72.171	87.964	39.757	...	29.815	7.807	51.817	61.251	39.677	32.182	31.327	15.722	64.522	37.746
1	22.403	79.735	83.860	14.659	14.462	18.451	92.567	62.538	55.851	28.697	...	12.757	8.305	15.029	25.499	11.296	53.721	39.987	2.279	14.715	0.783
2	4.115	90.708	93.359	12.239	8.160	5.969	88.406	43.246	88.003	8.176	...	10.769	3.742	13.282	36.930	11.678	93.213	66.309	14.134	60.500	46.700
3	52.441	95.324	94.544	55.475	55.510	56.197	92.514	97.997	65.543	55.549	...	70.543	39.667	50.790	84.612	77.572	90.827	72.860	75.441	90.201	81.427
4	24.954	60.679	69.408	56.285	29.554	38.174	54.628	28.577	68.272	24.793	...	87.502	49.994	58.722	91.131	72.087	83.217	52.870	73.150	52.402	73.957

5 rows × 303 columns

Data collection/processing

---

source

get_unique_site

 get_unique_site (df:pandas.core.frame.DataFrame=None,
                  seq_col:str='site_seq', id_col:str='gene_site')

Remove duplicates among phosphorylation sites; return df with new columns of acceptor and number of duplicates

	Type	Default	Details
df	DataFrame	None	dataframe that contains phosphorylation sites
seq_col	str	site_seq	column name of site sequence
id_col	str	gene_site	column name of site id

As there are lots of duplicates of the phosphorylation site sequence in the dataset, it could be helpful to remove the duplicated sequences.

Implement get_unique_site to get unique phosphorylation sites. Need to inform columns of sequence and id.

df = Data.get_ochoa_site()
unique = get_unique_site(df,seq_col='site_seq',id_col='gene_site')
unique.sort_values('num_site',ascending=False).head()

	site_seq	gene_site	num_site	acceptor
59397	PDYRQNVYIPGSNAT	PCDGC_Y896|PCDGK_Y898|PCDGG_Y887|PCDGM_Y908|PC...	21	Y
96321	TMGLSARYGPQFTLQ	PCDGC_Y879|PCDGK_Y881|PCDGG_Y870|PCDGM_Y891|PC...	21	Y
96147	TLQHVPDYRQNVYIP	PCDGC_Y891|PCDGK_Y893|PCDGG_Y882|PCDGM_Y903|PC...	21	Y
11223	DKFIIPGSPAIISIR	PCDC1_S906|PCDA7_S880|PCDA6_S893|PCDAC_S884|PC...	14	S
18666	ELAKHAVSEGTKAVT	H2B1K_S113|H2BFS_S113|H2B1D_S113|H2B1C_S113|H2...	12	S

---

source

extract_site_seq

 extract_site_seq (df:pandas.core.frame.DataFrame, seq_col:str,
                   position_col:str)

Extract -7 to +7 site sequence from protein sequence

	Type	Details
df	DataFrame	dataframe that contains protein sequence
seq_col	str	column name of protein sequence
position_col	str	column name of position 0

As some datasets only contains protein information and position of phosphorylation sites, but not phosphorylation site sequence, we can retreive protein sequence and use this function to get -7 to +7 phosphorylation site sequence (as numpy array).

Remember to validate the phospho-acceptor at position 0 before extract the site sequence, as there could be mismatch due to the protein sequence database updates.

df = Data.get_ochoa_site().head()
df

	uniprot	position	residue	is_disopred	disopred_score	log10_hotspot_pval_min	isHotspot	uniprot_position	functional_score	current_uniprot	name	gene	Sequence	is_valid	site_seq	gene_site
0	A0A075B6Q4	24	S	True	0.91	6.839384	True	A0A075B6Q4_24	0.149257	A0A075B6Q4	A0A075B6Q4_HUMAN	None	MDIQKSENEDDSEWEDVDDEKGDSNDDYDSAGLLSDEDCMSVPGKT...	True	VDDEKGDSNDDYDSA	A0A075B6Q4_S24
1	A0A075B6Q4	35	S	True	0.87	9.192622	False	A0A075B6Q4_35	0.136966	A0A075B6Q4	A0A075B6Q4_HUMAN	None	MDIQKSENEDDSEWEDVDDEKGDSNDDYDSAGLLSDEDCMSVPGKT...	True	YDSAGLLSDEDCMSV	A0A075B6Q4_S35
2	A0A075B6Q4	57	S	False	0.28	0.818834	False	A0A075B6Q4_57	0.125364	A0A075B6Q4	A0A075B6Q4_HUMAN	None	MDIQKSENEDDSEWEDVDDEKGDSNDDYDSAGLLSDEDCMSVPGKT...	True	IADHLFWSEETKSRF	A0A075B6Q4_S57
3	A0A075B6Q4	68	S	False	0.03	0.375986	False	A0A075B6Q4_68	0.119811	A0A075B6Q4	A0A075B6Q4_HUMAN	None	MDIQKSENEDDSEWEDVDDEKGDSNDDYDSAGLLSDEDCMSVPGKT...	True	KSRFTEYSMTSSVMR	A0A075B6Q4_S68
4	A0A075B6Q4	71	S	False	0.05	0.000000	False	A0A075B6Q4_71	0.095193	A0A075B6Q4	A0A075B6Q4_HUMAN	None	MDIQKSENEDDSEWEDVDDEKGDSNDDYDSAGLLSDEDCMSVPGKT...	True	FTEYSMTSSVMRRNE	A0A075B6Q4_S71

extract_site_seq(df,seq_col='Sequence',position_col='position')

100%|██████████| 5/5 [00:00<00:00, 3412.22it/s]

array(['VDDEKGDSNDDYDSA', 'YDSAGLLSDEDCMSV', 'IADHLFWSEETKSRF',
       'KSRFTEYSMTSSVMR', 'FTEYSMTSSVMRRNE'], dtype='<U15')

Get amino acids frequency at each position

---

source

get_freq

 get_freq (df_k:pandas.core.frame.DataFrame, aa_order=['P', 'G', 'A', 'C',
           'S', 'T', 'V', 'I', 'L', 'M', 'F', 'Y', 'W', 'H', 'K', 'R',
           'Q', 'N', 'D', 'E', 's', 't', 'y'], aa_order_paper=['P', 'G',
           'A', 'C', 'S', 'T', 'V', 'I', 'L', 'M', 'F', 'Y', 'W', 'H',
           'K', 'R', 'Q', 'N', 'D', 'E', 's', 't', 'y'], position=[-7, -6,
           -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7],
           position_paper=[-5, -4, -3, -2, -1, 1, 2, 3, 4])

Get frequency matrix given a dataframe of phosphorylation sites for a single kinase

	Type	Default	Details
df_k	DataFrame		a dataframe for a single kinase that contains phosphorylation sequence splitted by their position
aa_order	list	[‘P’, ‘G’, ‘A’, ‘C’, ‘S’, ‘T’, ‘V’, ‘I’, ‘L’, ‘M’, ‘F’, ‘Y’, ‘W’, ‘H’, ‘K’, ‘R’, ‘Q’, ‘N’, ‘D’, ‘E’, ‘s’, ‘t’, ‘y’]	amino acid to include in the full matrix
aa_order_paper	list	[‘P’, ‘G’, ‘A’, ‘C’, ‘S’, ‘T’, ‘V’, ‘I’, ‘L’, ‘M’, ‘F’, ‘Y’, ‘W’, ‘H’, ‘K’, ‘R’, ‘Q’, ‘N’, ‘D’, ‘E’, ‘s’, ‘t’, ‘y’]	amino acid to include in the partial matrix
position	list	[-7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7]	position to include in the full matrix
position_paper	list	[-5, -4, -3, -2, -1, 1, 2, 3, 4]	position to include in the partial matrix

# Get data of a certain kinase
df = Data.get_ks_dataset()
df_k = df.query('kinase=="DYRK2"')
df_k.head() # note that the dataframe contains columns -7 to 7

	Kinase	substrate	kinase_uniprot	kinase_paper	source	-7	-6	-5	-4	-3	...	0	1	2	3	4	5	6	7	kinase	on_tree
0	DYRK2	AEGLRPAsPLGLTQE	Q92630	DYRK2	pplus	A	E	G	L	R	...	s	P	L	G	L	T	Q	E	DYRK2	1
1	DYRK2	GGGAGPVsPQHHELT	Q92630	DYRK2	pplus	G	G	G	A	G	...	s	P	Q	H	H	E	L	T	DYRK2	1
2	DYRK2	LRGNVVPsPLPtRRt	Q92630	DYRK2	pplus	L	R	G	N	V	...	s	P	L	P	t	R	R	t	DYRK2	1
3	DYRK2	GPMRRSKsPADSANG	Q92630	DYRK2	pplus	G	P	M	R	R	...	s	P	A	D	S	A	N	G	DYRK2	1
4	DYRK2	PERsQEEsPPGSTKR	Q92630	DYRK2	pplus	P	E	R	s	Q	...	s	P	P	G	S	T	K	R	DYRK2	1

5 rows × 22 columns

# get frequency matrix
paper_format, full = get_freq(df_k)
paper_format.head()

Position	-5	-4	-3	-2	-1	1	2	3	4
aa
P	0.060639	0.066152	0.074972	0.110254	0.110254	0.386313	0.057459	0.135105	0.062361
G	0.076075	0.074972	0.126792	0.061742	0.087100	0.046358	0.068508	0.101883	0.067929
A	0.091510	0.083793	0.061742	0.142227	0.100331	0.089404	0.108287	0.071982	0.080178
C	0.011025	0.006615	0.011025	0.030871	0.017641	0.012141	0.023204	0.018826	0.006682
S	0.036384	0.049614	0.024256	0.036384	0.023153	0.027594	0.028729	0.035437	0.038976

Statistical analysis

---

source

get_pvalue

 get_pvalue (df, columns1, columns2, test_method='mann_whitney',
             FC_method='median')

Performs statistical tests and calculates difference between the median or mean of two groups of columns.

	Type	Default	Details
df
columns1			list of column names for group1
columns2			list of column names for group2
test_method	str	mann_whitney	‘student_t’, ‘mann_whitney’, ‘wilcoxon’
FC_method	str	median	or mean

---

source

get_metaP

 get_metaP (p_values)

Use Fisher’s method to calculate a combined p value given a list of p values; this function also allows negative p values (negative correlation)

p_values = [0.001,-0.5,0.002]

get_metaP(p_values)

0.0003626876953231754

PSPA normalization

---

source

raw2norm

 raw2norm (df:pandas.core.frame.DataFrame, PDHK:bool=False)

Normalize single ST kinase data

	Type	Default	Details
df	DataFrame		single kinase’s df has position as index, and single amino acid as columns
PDHK	bool	False	whether this kinase belongs to PDHK family

This function implement the normalization method from Johnson et al. Nature: An atlas of substrate specificities for the human serine/threonine kinome

Specifically, > - matrices were column-normalized at all positions by the sum of the 17 randomized amino acids (excluding serine, threonine and cysteine), to yield PSSMs. >- PDHK1 and PDHK4 were normalized to the 16 randomized amino acids (excluding serine, threonine, cysteine and additionally tyrosine) >- The cysteine row was scaled by its median to be 1/17 (1/16 for PDHK1 and PDHK4). >- The serine and threonine values in each position were set to be the median of that position. >- The S0/T0 ratio was determined by summing the values of S and T rows in the matrix (SS and ST, respectively), accounting for the different S vs. T composition of the central (1:1) and peripheral (only S or only T) positions (Sctrl and Tctrl, respectively), and then normalizing to the higher value among the two (S0 and T0, respectively, Supplementary Note 1)

This function is usually implemented with the below function, with normalize being a bool argument.

---

source

get_one_kinase

 get_one_kinase (df:pandas.core.frame.DataFrame, kinase:str,
                 normalize:bool=False, drop_s:bool=True)

Obtain a specific kinase data from stacked dataframe

	Type	Default	Details
df	DataFrame		stacked dataframe (paper’s raw data)
kinase	str		a specific kinase
normalize	bool	False	normalize according to the paper; special for PDHK1/4
drop_s	bool	True	drop s as s is a duplicates of t in PSPA

Retreive a single kinase data from PSPA data that has an format of kinase as index and position+amino acid as column.

To load raw and normalized PSPA data:

import pandas as pd

raw = pd.read_csv('https://github.com/sky1ove/katlas_raw/raw/refs/heads/main/nbs/raw/pspa_st_raw.csv').set_index('kinase')
norm = pd.read_csv('https://github.com/sky1ove/katlas_raw/raw/refs/heads/main/nbs/raw/pspa_st_norm.csv').set_index('kinase')
scale = pd.read_csv('https://github.com/sky1ove/katlas_raw/raw/refs/heads/main/nbs/raw/pspa_st_scale.csv').set_index('kinase')

raw

	-5P	-5G	-5A	-5C	-5S	-5T	-5V	-5I	-5L	-5M	...	4H	4K	4R	4Q	4N	4D	4E	4s	4t	4y
kinase
AAK1	7.614134e+06	2.590563e+06	3.001315e+06	4.696631e+06	4.944312e+06	8.315838e+06	1.005654e+07	1.643306e+07	1.049974e+07	9.133578e+06	...	6.020663e+06	8.938081e+06	9.983402e+06	6.833482e+06	6.364453e+06	4.189046e+06	4.921596e+06	2.705054e+06	2.705054e+06	2.909280e+06
ACVR2A	4.991039e+06	5.783856e+06	7.015771e+06	8.367603e+06	7.072052e+06	7.601400e+06	7.188292e+06	7.513916e+06	7.159895e+06	6.266123e+06	...	6.039473e+06	5.556301e+06	5.178735e+06	6.490098e+06	5.862481e+06	6.742906e+06	6.750653e+06	7.414220e+06	7.414220e+06	6.209577e+06
ACVR2B	2.648033e+07	2.568969e+07	2.813730e+07	4.517591e+07	3.287672e+07	3.351696e+07	2.701119e+07	2.199626e+07	2.341299e+07	2.567058e+07	...	2.798420e+07	2.249692e+07	2.423690e+07	2.913286e+07	2.652739e+07	3.638873e+07	3.472932e+07	3.790608e+07	3.790608e+07	3.176142e+07
AKT1	1.839951e+07	1.810468e+07	1.683184e+07	1.724774e+07	2.264728e+07	1.780129e+07	1.303757e+07	1.327190e+07	1.415649e+07	1.540976e+07	...	2.951154e+07	5.094266e+07	4.815292e+07	3.269388e+07	2.889660e+07	1.970135e+07	1.388746e+07	1.748307e+07	1.748307e+07	1.169683e+07
AKT2	5.439238e+06	5.569477e+06	5.805463e+06	6.301076e+06	5.004932e+06	4.812023e+06	3.906822e+06	3.776845e+06	4.450345e+06	4.629320e+06	...	6.812202e+06	1.159068e+07	9.932526e+06	6.544477e+06	6.252361e+06	3.629092e+06	3.510048e+06	5.499662e+06	5.499662e+06	4.188621e+06
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
YANK2	3.458024e+06	4.161241e+06	3.791822e+06	4.204140e+06	5.512662e+06	6.506984e+06	2.579471e+06	2.797506e+06	2.734876e+06	2.793789e+06	...	4.210220e+06	6.679453e+06	6.769908e+06	3.128978e+06	3.000055e+06	1.953885e+06	2.785169e+06	6.746347e+06	6.746347e+06	3.883819e+07
YANK3	6.199852e+06	7.696729e+06	6.418478e+06	7.520856e+06	8.734398e+06	9.017247e+06	4.982414e+06	5.323761e+06	5.566637e+06	5.384766e+06	...	5.905824e+06	6.872153e+06	7.202598e+06	6.212529e+06	5.734786e+06	5.006788e+06	8.467383e+06	1.183013e+07	1.183013e+07	5.676307e+07
YSK1	2.011151e+08	2.429346e+08	2.492902e+08	2.329794e+08	2.793240e+08	5.318319e+08	1.699564e+08	1.605611e+08	1.520427e+08	1.801505e+08	...	2.603125e+08	4.353054e+08	4.744941e+08	1.853705e+08	1.990460e+08	1.114695e+08	9.829561e+07	9.425377e+07	9.425377e+07	8.056884e+07
YSK4	8.476214e+07	1.041055e+08	1.064002e+08	1.206393e+08	1.541274e+08	1.674547e+08	7.388398e+07	5.712895e+07	6.185101e+07	7.312922e+07	...	1.133369e+08	1.403252e+08	1.065588e+08	1.276831e+08	1.069086e+08	1.009037e+08	8.336549e+07	1.092130e+08	1.092130e+08	6.710453e+07
ZAK	1.775228e+08	1.882077e+08	1.937538e+08	1.992441e+08	2.961481e+08	3.950234e+08	1.334480e+08	1.267392e+08	1.402677e+08	1.420544e+08	...	1.855718e+08	3.330092e+08	2.793030e+08	1.693537e+08	1.535532e+08	9.440832e+07	1.021720e+08	1.076703e+08	1.076703e+08	1.127176e+08

303 rows × 207 columns

one_kinase = get_one_kinase(raw,'PDHK1')
one_kinase.head()

aa	A	C	D	E	F	G	H	I	K	L	...	P	Q	R	S	T	V	W	Y	t	y
position
-5	8742435.33	10414182.29	8663835.37	8096013.86	11402696.32	10253402.14	10105837.98	8683931.90	7578162.13	9660152.81	...	6637930.10	6242275.07	8735083.42	17325761.72	10840094.13	8430649.60	14729350.10	11402696.32	8575155.23	9671765.02
-4	9382375.57	10685938.26	8357249.08	7761083.75	11217909.10	10855959.77	9079043.40	9130790.82	7898317.44	9322057.05	...	9672268.74	8379245.47	9377210.68	10952415.10	9895845.34	7886254.77	13908900.76	11217909.10	8025228.06	11415154.10
-3	9566806.27	10274228.62	7860338.75	6664677.78	12646646.40	9136758.39	10619788.43	10815274.55	7575486.39	10510394.47	...	8973502.17	8383343.00	8378836.06	15571737.29	10373422.50	9253028.96	17526458.60	12646646.40	6558017.14	8706611.00
-2	8874823.78	11219554.16	7104673.31	6607581.65	11937469.77	13445698.89	11887506.94	8049058.41	6643874.14	9617614.67	...	7548109.53	8208440.55	9307590.91	20205849.32	13325121.79	7839573.90	16355323.34	11937469.77	4944830.56	8422409.78
-1	10110169.52	14777201.90	12784916.61	5507173.44	8406884.45	8990141.98	10109111.77	6409587.79	5295768.52	7469514.59	...	6981606.35	6472612.56	6069925.70	19309187.20	22395646.37	6650117.78	9773567.40	8406884.45	4625731.13	5606047.19

5 rows × 22 columns

Set normalize to True can normalize the data based on previous normalization method.

one_normalized = get_one_kinase(raw,'PDHK1',normalize=True)
one_normalized.head()

aa	A	C	D	E	F	G	H	I	K	L	...	P	Q	R	S	T	V	W	Y	t	y
position
-5	0.0594	0.0625	0.0589	0.0550	0.0775	0.0697	0.0687	0.0590	0.0515	0.0657	...	0.0451	0.0424	0.0594	0.0594	0.0594	0.0573	0.1001	0.0775	0.0583	0.0658
-4	0.0618	0.0621	0.0550	0.0511	0.0739	0.0715	0.0598	0.0601	0.0520	0.0614	...	0.0637	0.0552	0.0617	0.0608	0.0608	0.0519	0.0916	0.0739	0.0528	0.0752
-3	0.0608	0.0576	0.0499	0.0423	0.0803	0.0580	0.0674	0.0687	0.0481	0.0667	...	0.0570	0.0532	0.0532	0.0584	0.0584	0.0588	0.1113	0.0803	0.0416	0.0553
-2	0.0587	0.0655	0.0470	0.0437	0.0790	0.0890	0.0787	0.0533	0.0440	0.0637	...	0.0500	0.0543	0.0616	0.0565	0.0565	0.0519	0.1082	0.0790	0.0327	0.0557
-1	0.0782	0.1009	0.0989	0.0426	0.0650	0.0695	0.0782	0.0496	0.0409	0.0578	...	0.0540	0.0500	0.0469	0.0594	0.0594	0.0514	0.0756	0.0650	0.0358	0.0433

5 rows × 22 columns

To further scale the data based on the scaling method from Johnson et al. Nature: An atlas of substrate specificities for the human serine/threonine kinome, we can multiply all values by a certain factor (16 for most kinases, and 17 for PDHK)

All kinases are divided by 1/17 (#Random AA); PDHK1 or 4 are divided by 1/16.

num_dict = Data.get_num_dict()

# multiply all values by a scale factor (number of random amino acids)
scale2 = norm.apply(lambda r: r*num_dict.get(r.name), axis=1)

We can compare the calculated one with the original one from the paper. They are same.

scale.head(2).round(2)

	-5P	-5G	-5A	-5C	-5S	-5T	-5V	-5I	-5L	-5M	...	4H	4K	4R	4Q	4N	4D	4E	4s	4t	4y
kinase
AAK1	1.22	0.42	0.48	0.78	0.72	0.72	1.62	2.64	1.69	1.47	...	0.95	1.41	1.58	1.08	1.01	0.66	0.78	0.43	0.43	0.46
ACVR2A	0.71	0.82	0.99	0.83	0.98	0.98	1.02	1.06	1.01	0.89	...	0.97	0.90	0.84	1.05	0.95	1.09	1.09	1.20	1.20	1.00

2 rows × 207 columns

scale2.head(2).round(2)

	-5P	-5G	-5A	-5C	-5S	-5T	-5V	-5I	-5L	-5M	...	4H	4K	4R	4Q	4N	4D	4E	4s	4t	4y
kinase
AAK1	1.22	0.42	0.48	0.78	0.72	0.72	1.62	2.64	1.69	1.47	...	0.95	1.41	1.58	1.08	1.01	0.66	0.78	0.43	0.43	0.46
ACVR2A	0.71	0.82	0.99	0.83	0.98	0.98	1.02	1.06	1.01	0.89	...	0.97	0.90	0.83	1.05	0.95	1.09	1.09	1.20	1.20	1.00

2 rows × 207 columns

End