KATLAS

KATLAS is a repository containing python tools to predict kinases given a substrate sequence. It also contains datasets of kinase substrate specificities and human phosphoproteomics.

References: Please cite the appropriate papers if KATLAS is helpful to your research.

• KATLAS was described in the paper [Computational Decoding of Human Kinome Substrate Specificities and Functions]

• The positional scanning peptide array (PSPA) data is from paper An atlas of substrate specificities for the human serine/threonine kinome and paper The intrinsic substrate specificity of the human tyrosine kinome

• The kinase substrate datasets used for generating PSSMs are derived from PhosphoSitePlus and paper Large-scale Discovery of Substrates of the Human Kinome

• Phosphorylation sites are acquired from PhosphoSitePlus, paper The functional landscape of the human phosphoproteome, and CPTAC / LinkedOmics

Web applications

Users can now run the analysis directly on the web without needing to code.

Check out our latest web platform: kinase-atlas.com

Install

pip install python-katlas

Import

from katlas.common import *

Quick start

We provide two methods to calculate substrate sequence:

• Computational Data-Driven Method (CDDM)
• Positional Scanning Peptide Array (PSPA)

We consider the input in two formats:

• a single input string (phosphorylation site)
• a csv/dataframe that contains a column of phosphorylation sites

For input sequences, we also consider it in two conditions:

• all capital
• contains lower cases indicating phosphorylation status

Quick start

Site scoring

CDDM, all capital

predict_kinase('AAAAAAASGAGSDN',**Params("CDDM_upper"))

considering string: ['-7A', '-6A', '-5A', '-4A', '-3A', '-2A', '-1A', '0S', '1G', '2A', '3G', '4S', '5D', '6N']

GCN2      4.556
MPSK1     4.425
MEKK2     4.253
WNK3      4.213
WNK1      4.064
          ...  
PDK1    -25.077
PDHK3   -25.346
CLK2    -27.251
ROR2    -27.582
DDR1    -53.581
Length: 328, dtype: float64

CDDM, with lower case indicating phosphorylation status

predict_kinase('AAAAAAAsGGAGsDN',**Params("CDDM"))

considering string: ['-7A', '-6A', '-5A', '-4A', '-3A', '-2A', '-1A', '0s', '1G', '2G', '3A', '4G', '5s', '6D', '7N']

ROR1       8.355
WNK1       4.907
WNK2       4.782
ERK5       4.466
RIPK2      4.045
           ...  
DDR1     -29.393
TNNI3K   -29.884
CHAK1    -31.775
VRK1     -45.287
BRAF     -49.403
Length: 328, dtype: float64

PSPA, with lower case indicating phosphorylation status

predict_kinase('AEEKEyHsEGG',**Params("PSPA"))

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

kinase
EGFR          4.013
FGFR4         3.568
ZAP70         3.412
CSK           3.241
SYK           3.209
              ...  
JAK1         -3.837
DDR2         -4.421
TNK2         -4.534
TNNI3K_TYR   -4.651
TNK1         -5.320
Length: 93, dtype: float64

To replicate the results from The Kinase Library (PSPA)

Check this link: The Kinase Library, and use log2(score) to rank, it shows same results with the below (with slight differences due to rounding).

out = predict_kinase('AEEKEyHSEGG',**Params("PSPA"))
out

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

kinase
EGFR     3.181
FGFR4    2.390
CSK      2.308
ZAP70    2.068
SYK      1.998
         ...  
EPHA1   -3.501
FES     -3.699
TNK1    -4.269
TNK2    -4.577
DDR2    -4.920
Length: 93, dtype: float64

• So far The kinase Library considers all tyr sequences in capital regardless of whether or not they contain lower cases, which is a small bug and should be fixed soon.
• Kinase with “_TYR” indicates it is a dual specificity kinase tested in PSPA tyrosine setting, which has not been included in kinase-library yet.

We can also calculate the percentile score using a referenced score sheet.

# Percentile reference sheet
y_pct = Data.get_pspa_tyr_pct()

get_pct('AEEKEyHSEGG',pct_ref = y_pct,**Params("PSPA_y"))

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

Site scoring in a df

Load your csv:

# df = pd.read_csv('your_file.csv')

Or load a demo df

# Load a demo df with phosphorylation sites
df = Data.get_ochoa_site().head()
df.iloc[:,-2:]

Set the column name and param to calculate

Here we choose param_CDDM_upper, as the sequences in the demo df are all in capital. You can also choose other params.

results = predict_kinase_df(df,'site_seq',**Params("CDDM_upper"))
results

input dataframe has a length 5
Preprocessing
Finish preprocessing
Merging reference
Finish merging

results.iloc[0].sort_values(ascending=False)

TLK2        8.264621
GCN2        8.101542
TLK1        7.693897
HRI         6.691402
PLK3        6.579368
             ...    
NIK       -64.605148
SRPK2     -67.300667
GAK       -69.165062
BRAF      -87.763214
PKMYT1   -113.698143
Name: 0, Length: 328, dtype: float32

Dataset

Besides calculating sequence scores, we also provides multiple datasets of phosphorylation sites.

CPTAC pan-cancer phosphoproteomics

df = Data.get_cptac_ensembl_site()
df.head(3)

Ochoa et al. human phosphoproteome

df = Data.get_ochoa_site()
df.head(3)

PhosphoSitePlus human phosphorylation site

df = Data.get_psp_human_site()
df.head(3)

Unique sites of combined Ochoa & PhosphoSitePlus

df = Data.get_combine_site_psp_ochoa()
df.head(3)

Phosphorylation site sequence example

All capital - 15 length (-7 to +7)

• QSEEEKLSPSPTTED
• TLQHVPDYRQNVYIP
• TMGLSARyGPQFTLQ

All capital - 10 length (-5 to +4)

• SRDPHYQDPH
• LDNPDyQQDF
• AAAAAsGGAG

With lowercase - (-7 to +7)

• QsEEEKLsPsPTTED
• TLQHVPDyRQNVYIP
• TMGLsARyGPQFTLQ

With lowercase - (-5 to +4)

• sRDPHyQDPH
• LDNPDyQQDF
• AAAAAsGGAG