Scoring

Scoring functions to calculate kinase score based on substrate sequence

Setup

from katlas.score import *

Utils

source

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'

source

STY2sty

 STY2sty (input_string:str)

Replace all ‘STY’ with ‘sty’ in a sequence

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

source

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']

Scoring func

Multiply

source

multiply

 multiply (values, kinase=None, num_aa=23)

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
kinase NoneType None
num_aa int 23 number of amino acids, 23 for standard CDDM, 20 for all uppercase CDDM

\[ \text{Score} = \log_2 \left( \frac{ \prod P_{\text{KinX}}(\text{AA}, \text{Position}) }{ \left( \frac{1}{\#\text{Random AA}} \right)^{\text{length(Position except 0)}} } \right) \]

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

source

multiply_pspa

 multiply_pspa (values, kinase, num_aa_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_pspa(values=[1,2,3,4,5],kinase='PDHK1')
np.float64(22.906890595608516)

Sum

source

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

Predict kinase

source

duplicate_ref_zero

 duplicate_ref_zero (df:pandas.core.frame.DataFrame)

If ‘0S’, ‘0T’, ‘0Y’ exist with non-zero values, create ‘0s’, ‘0t’, ‘0y’ with same values. If ‘0s’, ‘0t’, ‘0y’ exist with non-zero values, create ‘0S’, ‘0T’, ‘0Y’ with same values.

source

preprocess_ref

 preprocess_ref (ref)

Convert pS/T/Y in ref columns to s/t/y if any; mirror 0S/T/Y to 0s/t/y.

source

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

PSPA scoring:

pspa_ref = Data.get_pspa_all_norm()
predict_kinase("PSVEPPLsQETFSDL",ref=pspa_ref,func=multiply_pspa)
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
          ...  
PKN1     -7.275
P70S6K   -7.295
AKT3     -7.375
PKCI     -7.742
NEK3     -8.254
Length: 303, dtype: float64

CDDM scoring, LO + sum

ref=Data.get_cddm_LO() # Data.get_cddm_LO_upper()
predict_kinase("PSVEPPLsQETFSDL",ref=ref,func=sumup)
considering string: ['-7P', '-6S', '-5V', '-4E', '-3P', '-2P', '-1L', '0s', '1Q', '2E', '3T', '4F', '5S', '6D', '7L']
ATR        12.751
ATM        10.960
DNAPK       6.039
SRPK2       2.079
SMMLCK      1.876
           ...   
ROR1      -89.216
CDC7      -91.457
CAMK1B    -91.577
TNNI3K   -118.835
BRAF     -134.851
Length: 328, dtype: float64

CDDM scoring, PSSM + multiply (#23aa)

ref=Data.get_cddm() # Data.get_cddm_upper()
predict_kinase("PSVEPPLsQETFSDL",ref=ref,func=multiply_23)
considering string: ['-7P', '-6S', '-5V', '-4E', '-3P', '-2P', '-1L', '0s', '1Q', '2E', '3T', '4F', '5S', '6D', '7L']
ATR        16.824
ATM        15.033
DNAPK      10.112
SRPK2       6.152
SMMLCK      5.949
           ...   
ROR1      -85.143
CDC7      -87.384
CAMK1B    -87.503
TNNI3K   -114.762
BRAF     -130.778
Length: 328, dtype: float64

CDDM scoring, PSSM + multiply (#20aa)

ref=Data.get_cddm_upper() # Data.get_cddm_upper()
predict_kinase("PSVEPPLsQETFSDL",ref=ref,func=multiply_20)
considering string: ['-7P', '-6S', '-5V', '-4E', '-3P', '-2P', '-1L', '0s', '1Q', '2E', '3T', '4F', '5S', '6D', '7L']
ATR        16.587
ATM        14.362
DNAPK      10.430
SRPK2       8.044
CHK2        7.955
           ...   
TTK       -43.375
GAK       -45.159
CAMK1B    -69.395
TNNI3K    -70.993
BRAF     -109.130
Length: 328, dtype: float64

Params

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

source

Params

 Params (name=None, load=True)
Params()
['CDDM', 'CDDM_upper', 'PSPA_st', 'PSPA_y', 'PSPA']
for p in ['PSPA', 'CDDM','CDDM_upper']:
    print(predict_kinase("PSVEPPLsQETFSDL",**Params(p)).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']
ATR       12.751
ATM       10.960
DNAPK      6.039
SRPK2      2.079
SMMLCK     1.876
dtype: float64
considering string: ['-7P', '-6S', '-5V', '-4E', '-3P', '-2P', '-1L', '0S', '1Q', '2E', '3T', '4F', '5S', '6D', '7L']
ATR      11.815
ATM       9.590
DNAPK     5.659
SRPK2     3.272
CHK2      3.183
dtype: float64

Predict kinase in df

source

multiply_generic

 multiply_generic (merged_df, kinases, df_index, divide_factor_func)

Multiply-based log-sum aggregation across kinases.

source

predict_kinase_df

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

Predict kinase scores based on reference PSSM or weight matrix. Applies preprocessing, merges long format keys, then aggregates using given func.

df=Data.get_human_site()
# for p in ['CDDM', 'CDDM_upper', 'PSPA_st', 'PSPA_y', 'PSPA']:
#     out = predict_kinase_df2(df.head(10), seq_col='site_seq', **Params(p))
#     print(out.head())
out = predict_kinase_df(df.head(100), seq_col='site_seq', **Params('PSPA'))
Input dataframe has 100 rows
Preprocessing...
Preprocessing done. Expanding sequences...
Merging reference...
Merge complete.
Computing multiply_generic:   0%|          | 0/396 [00:00<?, ?it/s]Computing multiply_generic: 100%|██████████| 396/396 [00:00<00:00, 650.95it/s]

Percentile scoring

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()
out = get_pct('PSVEPPLyQETFSDL',**Params('PSPA_y'), pct_ref=y_pct)
out.sort_values('percentile',ascending=False)
considering string: ['-5V', '-4E', '-3P', '-2P', '-1L', '0Y', '1Q', '2E', '3T', '4F', '5S']
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',**Params('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

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 
# substrate score first
score_df = predict_kinase_df(df_sty,'site_seq', **Params('PSPA_st'))

#get percentile reference
pct_ref = Data.get_pspa_st_pct()

# calculate percentile score
pct = get_pct_df(score_df,pct_ref)

End