# KATLAS
<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->
<img alt="Katlas logo" width="600" caption="Katlas logo" src="https://github.com/sky1ove/katlas/raw/main/logo.png" id="logo"/>
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](https://www.nature.com/articles/s41586-022-05575-3) and paper
[The intrinsic substrate specificity of the human tyrosine
kinome](https://www.nature.com/articles/s41586-024-07407-y)
- The kinase substrate datasets used for generating PSSMs are derived
from
[PhosphoSitePlus](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3245126/)
and paper [Large-scale Discovery of Substrates of the Human
Kinome](https://www.nature.com/articles/s41598-019-46385-4)
- Phosphorylation sites are acquired from
[PhosphoSitePlus](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3245126/),
paper [The functional landscape of the human
phosphoproteome](https://www.nature.com/articles/s41587-019-0344-3),
and [CPTAC](https://pdc.cancer.gov/pdc/cptac-pancancer) /
[LinkedOmics](https://academic.oup.com/nar/article/46/D1/D956/4607804)
## Reproduce datasets & figures
Follow the instructions in katlas_raw:
https://github.com/sky1ove/katlas_raw
## 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](https://kinase-atlas.com/)
## Install
UV:
``` bash
uv add -U python-katlas
```
pip:
``` bash
pip install -U python-katlas
```
If using machine-learning related modules, need to install development
verison: `pip install -U "python-katlas[dev]"`
## Import
``` python
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
``` python
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
``` python
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
``` python
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](https://kinase-library.mit.edu/site?s=AEEKEy*HSEGG&pp=false&scp=true),
and use log2(score) to rank, it shows same results with the below (with
slight differences due to rounding).
``` python
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](https://kinase-library.phosphosite.org)
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.
``` python
# Percentile reference sheet
y_pct = Data.get_pspa_tyr_pct()
```
``` python
get_pct('AEEKEyHSEGG',pct_ref = y_pct,**Params("PSPA_y"))
```
considering string: ['-5A', '-4E', '-3E', '-2K', '-1E', '0Y', '1H', '2S', '3E', '4G', '5G']
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| | log2(score) | percentile |
|-------|-------------|------------|
| EGFR | 3.181 | 96.787423 |
| FGFR4 | 2.390 | 94.012303 |
| CSK | 2.308 | 95.201640 |
| ZAP70 | 2.068 | 88.380041 |
| SYK | 1.998 | 85.522898 |
| ... | ... | ... |
| EPHA1 | -3.501 | 12.139440 |
| FES | -3.699 | 21.216678 |
| TNK1 | -4.269 | 5.481887 |
| TNK2 | -4.577 | 2.050581 |
| DDR2 | -4.920 | 10.403281 |
<p>93 rows × 2 columns</p>
</div>
### Site scoring in a df
Load your csv:
``` python
# df = pd.read_csv('your_file.csv')
```
Or load a demo df
``` python
# Load a demo df with phosphorylation sites
df = Data.get_ochoa_site().head()
df.iloc[:,-2:]
```
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| | site_seq | gene_site |
|-----|-----------------|----------------|
| 0 | VDDEKGDSNDDYDSA | A0A075B6Q4_S24 |
| 1 | YDSAGLLSDEDCMSV | A0A075B6Q4_S35 |
| 2 | IADHLFWSEETKSRF | A0A075B6Q4_S57 |
| 3 | KSRFTEYSMTSSVMR | A0A075B6Q4_S68 |
| 4 | FTEYSMTSSVMRRNE | A0A075B6Q4_S71 |
</div>
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.
``` python
results = predict_kinase_df(df,'site_seq',**Params("CDDM_upper"))
results
```
input dataframe has a length 5
Preprocessing
Finish preprocessing
Merging reference
Finish merging
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| | SRC | EPHA3 | FES | NTRK3 | ALK | ABL1 | FLT3 | EPHA8 | EPHB2 | EPHB1 | ... | VRK1 | PKMYT1 | GRK3 | CAMK1B | CDC7 | SMMLCK | ROR1 | GAK | MAST2 | BRAF |
|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|
| 0 | -2.440640 | -0.818753 | -1.663990 | -0.738991 | -2.047628 | -3.602344 | -3.200998 | -0.935176 | -1.388444 | -1.859450 | ... | -17.103237 | -113.698143 | -16.848783 | -41.520172 | -41.646187 | 1.284159 | -26.566362 | -69.165062 | -17.706400 | -87.763214 |
| 1 | -3.838486 | -2.735969 | -2.533986 | -2.150399 | -3.792498 | -4.725527 | -5.711791 | -4.534240 | -3.148449 | -2.511518 | ... | -67.889053 | -68.652641 | -45.833855 | -64.171600 | -39.465572 | -65.061722 | -109.561707 | -85.911224 | -60.105064 | -63.889122 |
| 2 | -2.610423 | -2.370090 | -3.235637 | -1.508413 | -2.571347 | -3.740941 | -3.025596 | -3.373504 | -2.776297 | -3.060740 | ... | -15.798462 | -45.905319 | -61.440742 | -67.695694 | -55.047962 | -42.135216 | -38.501572 | -62.624382 | -56.119389 | -107.060989 |
| 3 | -5.180541 | -4.201880 | -5.766463 | -3.038421 | -3.836897 | -4.249900 | -5.029885 | -5.411311 | -4.713308 | -4.827825 | ... | -96.978317 | -83.419777 | -22.559393 | -110.611588 | -63.283070 | -37.240440 | -24.497492 | -112.878151 | -43.538158 | -60.348518 |
| 4 | -2.844254 | -3.322700 | -3.681745 | -1.766435 | -2.666579 | -3.748774 | -4.083619 | -3.912834 | -3.724181 | -3.948160 | ... | -35.824612 | -87.983566 | -83.312317 | -107.162407 | -61.478374 | -85.793571 | -43.738819 | -47.004211 | -42.281624 | -59.518513 |
<p>5 rows × 328 columns</p>
</div>
``` python
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
``` python
df = Data.get_cptac_ensembl_site()
df.head(3)
```
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| | 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 |
</div>
### [Ochoa et al. human phosphoproteome](https://www.nature.com/articles/s41587-019-0344-3)
``` python
df = Data.get_ochoa_site()
df.head(3)
```
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| | 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 | 1.0 | 0.91 | 6.839384 | 1.0 | A0A075B6Q4_24 | 0.149257 | A0A075B6Q4 | A0A075B6Q4_HUMAN | None | MDIQKSENEDDSEWEDVDDEKGDSNDDYDSAGLLSDEDCMSVPGKT... | True | VDDEKGDSNDDYDSA | A0A075B6Q4_S24 |
| 1 | A0A075B6Q4 | 35 | S | 1.0 | 0.87 | 9.192622 | 0.0 | A0A075B6Q4_35 | 0.136966 | A0A075B6Q4 | A0A075B6Q4_HUMAN | None | MDIQKSENEDDSEWEDVDDEKGDSNDDYDSAGLLSDEDCMSVPGKT... | True | YDSAGLLSDEDCMSV | A0A075B6Q4_S35 |
| 2 | A0A075B6Q4 | 57 | S | 0.0 | 0.28 | 0.818834 | 0.0 | A0A075B6Q4_57 | 0.125364 | A0A075B6Q4 | A0A075B6Q4_HUMAN | None | MDIQKSENEDDSEWEDVDDEKGDSNDDYDSAGLLSDEDCMSVPGKT... | True | IADHLFWSEETKSRF | A0A075B6Q4_S57 |
</div>
### PhosphoSitePlus human phosphorylation site
``` python
df = Data.get_psp_human_site()
df.head(3)
```
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| | gene | protein | uniprot | site | gene_site | SITE_GRP_ID | species | site_seq | LT_LIT | MS_LIT | MS_CST | CST_CAT# | Ambiguous_Site |
|----|----|----|----|----|----|----|----|----|----|----|----|----|----|
| 0 | YWHAB | 14-3-3 beta | P31946 | T2 | YWHAB_T2 | 15718712 | human | \_\_\_\_\_\_MtMDksELV | NaN | 3.0 | 1.0 | None | 0 |
| 1 | YWHAB | 14-3-3 beta | P31946 | S6 | YWHAB_S6 | 15718709 | human | \_\_MtMDksELVQkAk | NaN | 8.0 | NaN | None | 0 |
| 2 | YWHAB | 14-3-3 beta | P31946 | Y21 | YWHAB_Y21 | 3426383 | human | LAEQAERyDDMAAAM | NaN | NaN | 4.0 | None | 0 |
</div>
### Unique sites of combined Ochoa & PhosphoSitePlus
``` python
df = Data.get_combine_site_psp_ochoa()
df.head(3)
```
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| | uniprot | gene | site | site_seq | source | AM_pathogenicity | CDDM_upper | CDDM_max_score |
|----|----|----|----|----|----|----|----|----|
| 0 | A0A024R4G9 | C19orf48 | S20 | ITGSRLLSMVPGPAR | psp | NaN | PRKX,AKT1,PKG1,P90RSK,HIPK4,AKT3,HIPK1,PKACB,H... | 2.407041 |
| 1 | A0A075B6Q4 | None | S24 | VDDEKGDSNDDYDSA | ochoa | NaN | CK2A2,CK2A1,GRK7,GRK5,CK1G1,CK1A,IKKA,CK1G2,CA... | 2.295654 |
| 2 | A0A075B6Q4 | None | S35 | YDSAGLLSDEDCMSV | ochoa | NaN | CK2A2,CK2A1,IKKA,ATM,IKKB,CAMK1D,MARK2,GRK7,IK... | 2.488683 |
</div>
## 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
Raw data
{
"_id": null,
"home_page": "https://github.com/sky1ove/katlas",
"name": "python-katlas",
"maintainer": null,
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"requires_python": ">=3.7",
"maintainer_email": null,
"keywords": "nbdev jupyter notebook python",
"author": "lily",
"author_email": "lcai888666@gmail.com",
"download_url": "https://files.pythonhosted.org/packages/9a/9b/bb087ef004e42ca232351ffb69a086ff924aa6f85ed128ed95fc80b7507e/python_katlas-2025.10.20.2.tar.gz",
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"description": "# KATLAS\n\n\n<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->\n\n<img alt=\"Katlas logo\" width=\"600\" caption=\"Katlas logo\" src=\"https://github.com/sky1ove/katlas/raw/main/logo.png\" id=\"logo\"/>\n\nKATLAS is a repository containing python tools to predict kinases given\na substrate sequence. It also contains datasets of kinase substrate\nspecificities and human phosphoproteomics.\n\n***References***: Please cite the appropriate papers if KATLAS is\nhelpful to your research.\n\n- KATLAS was described in the paper \\[Computational Decoding of Human\n Kinome Substrate Specificities and Functions\\]\n\n- The positional scanning peptide array (PSPA) data is from paper [An\n atlas of substrate specificities for the human serine/threonine\n kinome](https://www.nature.com/articles/s41586-022-05575-3) and paper\n [The intrinsic substrate specificity of the human tyrosine\n kinome](https://www.nature.com/articles/s41586-024-07407-y)\n\n- The kinase substrate datasets used for generating PSSMs are derived\n from\n [PhosphoSitePlus](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3245126/)\n and paper [Large-scale Discovery of Substrates of the Human\n Kinome](https://www.nature.com/articles/s41598-019-46385-4)\n\n- Phosphorylation sites are acquired from\n [PhosphoSitePlus](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3245126/),\n paper [The functional landscape of the human\n phosphoproteome](https://www.nature.com/articles/s41587-019-0344-3),\n and [CPTAC](https://pdc.cancer.gov/pdc/cptac-pancancer) /\n [LinkedOmics](https://academic.oup.com/nar/article/46/D1/D956/4607804)\n\n## Reproduce datasets & figures\n\nFollow the instructions in katlas_raw:\nhttps://github.com/sky1ove/katlas_raw\n\n## Web applications\n\nUsers can now run the analysis directly on the web without needing to\ncode.\n\nCheck out our latest web platform:\n[kinase-atlas.com](https://kinase-atlas.com/)\n\n## Install\n\nUV:\n\n``` bash\nuv add -U python-katlas\n```\n\npip:\n\n``` bash\npip install -U python-katlas\n```\n\nIf using machine-learning related modules, need to install development\nverison: `pip install -U \"python-katlas[dev]\"`\n\n## Import\n\n``` python\nfrom katlas.common import *\n```\n\n# Quick start\n\nWe provide two methods to calculate substrate sequence:\n\n- Computational Data-Driven Method (CDDM)\n- Positional Scanning Peptide Array (PSPA)\n\nWe consider the input in two formats:\n\n- a single input string (phosphorylation site)\n- a csv/dataframe that contains a column of phosphorylation sites\n\nFor input sequences, we also consider it in two conditions:\n\n- all capital\n- contains lower cases indicating phosphorylation status\n\n## Quick start\n\n### Site scoring\n\nCDDM, all capital\n\n``` python\npredict_kinase('AAAAAAASGAGSDN',**Params(\"CDDM_upper\"))\n```\n\n considering string: ['-7A', '-6A', '-5A', '-4A', '-3A', '-2A', '-1A', '0S', '1G', '2A', '3G', '4S', '5D', '6N']\n\n GCN2 4.556\n MPSK1 4.425\n MEKK2 4.253\n WNK3 4.213\n WNK1 4.064\n ... \n PDK1 -25.077\n PDHK3 -25.346\n CLK2 -27.251\n ROR2 -27.582\n DDR1 -53.581\n Length: 328, dtype: float64\n\nCDDM, with lower case indicating phosphorylation status\n\n``` python\npredict_kinase('AAAAAAAsGGAGsDN',**Params(\"CDDM\"))\n```\n\n considering string: ['-7A', '-6A', '-5A', '-4A', '-3A', '-2A', '-1A', '0s', '1G', '2G', '3A', '4G', '5s', '6D', '7N']\n\n ROR1 8.355\n WNK1 4.907\n WNK2 4.782\n ERK5 4.466\n RIPK2 4.045\n ... \n DDR1 -29.393\n TNNI3K -29.884\n CHAK1 -31.775\n VRK1 -45.287\n BRAF -49.403\n Length: 328, dtype: float64\n\nPSPA, with lower case indicating phosphorylation status\n\n``` python\npredict_kinase('AEEKEyHsEGG',**Params(\"PSPA\"))\n```\n\n considering string: ['-5A', '-4E', '-3E', '-2K', '-1E', '0y', '1H', '2s', '3E', '4G', '5G']\n\n kinase\n EGFR 4.013\n FGFR4 3.568\n ZAP70 3.412\n CSK 3.241\n SYK 3.209\n ... \n JAK1 -3.837\n DDR2 -4.421\n TNK2 -4.534\n TNNI3K_TYR -4.651\n TNK1 -5.320\n Length: 93, dtype: float64\n\nTo replicate the results from The Kinase Library (PSPA)\n\nCheck this link: [The Kinase\nLibrary](https://kinase-library.mit.edu/site?s=AEEKEy*HSEGG&pp=false&scp=true),\nand use log2(score) to rank, it shows same results with the below (with\nslight differences due to rounding).\n\n``` python\nout = predict_kinase('AEEKEyHSEGG',**Params(\"PSPA\"))\nout\n```\n\n considering string: ['-5A', '-4E', '-3E', '-2K', '-1E', '0y', '1H', '2S', '3E', '4G', '5G']\n\n kinase\n EGFR 3.181\n FGFR4 2.390\n CSK 2.308\n ZAP70 2.068\n SYK 1.998\n ... \n EPHA1 -3.501\n FES -3.699\n TNK1 -4.269\n TNK2 -4.577\n DDR2 -4.920\n Length: 93, dtype: float64\n\n- So far [The kinase Library](https://kinase-library.phosphosite.org)\n considers all ***tyr sequences*** in capital regardless of whether or\n not they contain lower cases, which is a small bug and should be fixed\n soon.\n- Kinase with \u201c\\_TYR\u201d indicates it is a dual specificity kinase tested\n in PSPA tyrosine setting, which has not been included in\n kinase-library yet.\n\nWe can also calculate the percentile score using a referenced score\nsheet.\n\n``` python\n# Percentile reference sheet\ny_pct = Data.get_pspa_tyr_pct()\n```\n\n``` python\nget_pct('AEEKEyHSEGG',pct_ref = y_pct,**Params(\"PSPA_y\"))\n```\n\n considering string: ['-5A', '-4E', '-3E', '-2K', '-1E', '0Y', '1H', '2S', '3E', '4G', '5G']\n\n<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n .dataframe tbody tr th {\n vertical-align: top;\n }\n .dataframe thead th {\n text-align: right;\n }\n</style>\n\n| | log2(score) | percentile |\n|-------|-------------|------------|\n| EGFR | 3.181 | 96.787423 |\n| FGFR4 | 2.390 | 94.012303 |\n| CSK | 2.308 | 95.201640 |\n| ZAP70 | 2.068 | 88.380041 |\n| SYK | 1.998 | 85.522898 |\n| ... | ... | ... |\n| EPHA1 | -3.501 | 12.139440 |\n| FES | -3.699 | 21.216678 |\n| TNK1 | -4.269 | 5.481887 |\n| TNK2 | -4.577 | 2.050581 |\n| DDR2 | -4.920 | 10.403281 |\n\n<p>93 rows \u00d7 2 columns</p>\n</div>\n\n### Site scoring in a df\n\nLoad your csv:\n\n``` python\n# df = pd.read_csv('your_file.csv')\n```\n\nOr load a demo df\n\n``` python\n# Load a demo df with phosphorylation sites\ndf = Data.get_ochoa_site().head()\ndf.iloc[:,-2:]\n```\n\n<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n .dataframe tbody tr th {\n vertical-align: top;\n }\n .dataframe thead th {\n text-align: right;\n }\n</style>\n\n| | site_seq | gene_site |\n|-----|-----------------|----------------|\n| 0 | VDDEKGDSNDDYDSA | A0A075B6Q4_S24 |\n| 1 | YDSAGLLSDEDCMSV | A0A075B6Q4_S35 |\n| 2 | IADHLFWSEETKSRF | A0A075B6Q4_S57 |\n| 3 | KSRFTEYSMTSSVMR | A0A075B6Q4_S68 |\n| 4 | FTEYSMTSSVMRRNE | A0A075B6Q4_S71 |\n\n</div>\n\nSet the column name and param to calculate\n\nHere we choose param_CDDM_upper, as the sequences in the demo df are all\nin capital. You can also choose other params.\n\n``` python\nresults = predict_kinase_df(df,'site_seq',**Params(\"CDDM_upper\"))\nresults\n```\n\n input dataframe has a length 5\n Preprocessing\n Finish preprocessing\n Merging reference\n Finish merging\n\n<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n .dataframe tbody tr th {\n vertical-align: top;\n }\n .dataframe thead th {\n text-align: right;\n }\n</style>\n\n| | SRC | EPHA3 | FES | NTRK3 | ALK | ABL1 | FLT3 | EPHA8 | EPHB2 | EPHB1 | ... | VRK1 | PKMYT1 | GRK3 | CAMK1B | CDC7 | SMMLCK | ROR1 | GAK | MAST2 | BRAF |\n|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|\n| 0 | -2.440640 | -0.818753 | -1.663990 | -0.738991 | -2.047628 | -3.602344 | -3.200998 | -0.935176 | -1.388444 | -1.859450 | ... | -17.103237 | -113.698143 | -16.848783 | -41.520172 | -41.646187 | 1.284159 | -26.566362 | -69.165062 | -17.706400 | -87.763214 |\n| 1 | -3.838486 | -2.735969 | -2.533986 | -2.150399 | -3.792498 | -4.725527 | -5.711791 | -4.534240 | -3.148449 | -2.511518 | ... | -67.889053 | -68.652641 | -45.833855 | -64.171600 | -39.465572 | -65.061722 | -109.561707 | -85.911224 | -60.105064 | -63.889122 |\n| 2 | -2.610423 | -2.370090 | -3.235637 | -1.508413 | -2.571347 | -3.740941 | -3.025596 | -3.373504 | -2.776297 | -3.060740 | ... | -15.798462 | -45.905319 | -61.440742 | -67.695694 | -55.047962 | -42.135216 | -38.501572 | -62.624382 | -56.119389 | -107.060989 |\n| 3 | -5.180541 | -4.201880 | -5.766463 | -3.038421 | -3.836897 | -4.249900 | -5.029885 | -5.411311 | -4.713308 | -4.827825 | ... | -96.978317 | -83.419777 | -22.559393 | -110.611588 | -63.283070 | -37.240440 | -24.497492 | -112.878151 | -43.538158 | -60.348518 |\n| 4 | -2.844254 | -3.322700 | -3.681745 | -1.766435 | -2.666579 | -3.748774 | -4.083619 | -3.912834 | -3.724181 | -3.948160 | ... | -35.824612 | -87.983566 | -83.312317 | -107.162407 | -61.478374 | -85.793571 | -43.738819 | -47.004211 | -42.281624 | -59.518513 |\n\n<p>5 rows \u00d7 328 columns</p>\n</div>\n\n``` python\nresults.iloc[0].sort_values(ascending=False)\n```\n\n TLK2 8.264621\n GCN2 8.101542\n TLK1 7.693897\n HRI 6.691402\n PLK3 6.579368\n ... \n NIK -64.605148\n SRPK2 -67.300667\n GAK -69.165062\n BRAF -87.763214\n PKMYT1 -113.698143\n Name: 0, Length: 328, dtype: float32\n\n## Dataset\n\nBesides calculating sequence scores, we also provides multiple datasets\nof phosphorylation sites.\n\n### CPTAC pan-cancer phosphoproteomics\n\n``` python\ndf = Data.get_cptac_ensembl_site()\ndf.head(3)\n```\n\n<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n .dataframe tbody tr th {\n vertical-align: top;\n }\n .dataframe thead th {\n text-align: right;\n }\n</style>\n\n| | gene | site | site_seq | protein | gene_name | gene_site | protein_site |\n|----|----|----|----|----|----|----|----|\n| 0 | ENSG00000003056.8 | S267 | DDQLGEESEERDDHL | ENSP00000000412.3 | M6PR | M6PR_S267 | ENSP00000000412_S267 |\n| 1 | ENSG00000003056.8 | S267 | DDQLGEESEERDDHL | ENSP00000440488.2 | M6PR | M6PR_S267 | ENSP00000440488_S267 |\n| 2 | ENSG00000048028.11 | S1053 | PPTIRPNSPYDLCSR | ENSP00000003302.4 | USP28 | USP28_S1053 | ENSP00000003302_S1053 |\n\n</div>\n\n### [Ochoa et al.\u00a0human phosphoproteome](https://www.nature.com/articles/s41587-019-0344-3)\n\n``` python\ndf = Data.get_ochoa_site()\ndf.head(3)\n```\n\n<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n .dataframe tbody tr th {\n vertical-align: top;\n }\n .dataframe thead th {\n text-align: right;\n }\n</style>\n\n| | 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 |\n|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|\n| 0 | A0A075B6Q4 | 24 | S | 1.0 | 0.91 | 6.839384 | 1.0 | A0A075B6Q4_24 | 0.149257 | A0A075B6Q4 | A0A075B6Q4_HUMAN | None | MDIQKSENEDDSEWEDVDDEKGDSNDDYDSAGLLSDEDCMSVPGKT... | True | VDDEKGDSNDDYDSA | A0A075B6Q4_S24 |\n| 1 | A0A075B6Q4 | 35 | S | 1.0 | 0.87 | 9.192622 | 0.0 | A0A075B6Q4_35 | 0.136966 | A0A075B6Q4 | A0A075B6Q4_HUMAN | None | MDIQKSENEDDSEWEDVDDEKGDSNDDYDSAGLLSDEDCMSVPGKT... | True | YDSAGLLSDEDCMSV | A0A075B6Q4_S35 |\n| 2 | A0A075B6Q4 | 57 | S | 0.0 | 0.28 | 0.818834 | 0.0 | A0A075B6Q4_57 | 0.125364 | A0A075B6Q4 | A0A075B6Q4_HUMAN | None | MDIQKSENEDDSEWEDVDDEKGDSNDDYDSAGLLSDEDCMSVPGKT... | True | IADHLFWSEETKSRF | A0A075B6Q4_S57 |\n\n</div>\n\n### PhosphoSitePlus human phosphorylation site\n\n``` python\ndf = Data.get_psp_human_site()\ndf.head(3)\n```\n\n<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n .dataframe tbody tr th {\n vertical-align: top;\n }\n .dataframe thead th {\n text-align: right;\n }\n</style>\n\n| | gene | protein | uniprot | site | gene_site | SITE_GRP_ID | species | site_seq | LT_LIT | MS_LIT | MS_CST | CST_CAT# | Ambiguous_Site |\n|----|----|----|----|----|----|----|----|----|----|----|----|----|----|\n| 0 | YWHAB | 14-3-3 beta | P31946 | T2 | YWHAB_T2 | 15718712 | human | \\_\\_\\_\\_\\_\\_MtMDksELV | NaN | 3.0 | 1.0 | None | 0 |\n| 1 | YWHAB | 14-3-3 beta | P31946 | S6 | YWHAB_S6 | 15718709 | human | \\_\\_MtMDksELVQkAk | NaN | 8.0 | NaN | None | 0 |\n| 2 | YWHAB | 14-3-3 beta | P31946 | Y21 | YWHAB_Y21 | 3426383 | human | LAEQAERyDDMAAAM | NaN | NaN | 4.0 | None | 0 |\n\n</div>\n\n### Unique sites of combined Ochoa & PhosphoSitePlus\n\n``` python\ndf = Data.get_combine_site_psp_ochoa()\ndf.head(3)\n```\n\n<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n .dataframe tbody tr th {\n vertical-align: top;\n }\n .dataframe thead th {\n text-align: right;\n }\n</style>\n\n| | uniprot | gene | site | site_seq | source | AM_pathogenicity | CDDM_upper | CDDM_max_score |\n|----|----|----|----|----|----|----|----|----|\n| 0 | A0A024R4G9 | C19orf48 | S20 | ITGSRLLSMVPGPAR | psp | NaN | PRKX,AKT1,PKG1,P90RSK,HIPK4,AKT3,HIPK1,PKACB,H... | 2.407041 |\n| 1 | A0A075B6Q4 | None | S24 | VDDEKGDSNDDYDSA | ochoa | NaN | CK2A2,CK2A1,GRK7,GRK5,CK1G1,CK1A,IKKA,CK1G2,CA... | 2.295654 |\n| 2 | A0A075B6Q4 | None | S35 | YDSAGLLSDEDCMSV | ochoa | NaN | CK2A2,CK2A1,IKKA,ATM,IKKB,CAMK1D,MARK2,GRK7,IK... | 2.488683 |\n\n</div>\n\n## Phosphorylation site sequence example\n\n***All capital - 15 length (-7 to +7)***\n\n- QSEEEKLSPSPTTED\n- TLQHVPDYRQNVYIP\n- TMGLSARyGPQFTLQ\n\n***All capital - 10 length (-5 to +4)***\n\n- SRDPHYQDPH\n- LDNPDyQQDF\n- AAAAAsGGAG\n\n***With lowercase - (-7 to +7)***\n\n- QsEEEKLsPsPTTED\n- TLQHVPDyRQNVYIP\n- TMGLsARyGPQFTLQ\n\n***With lowercase - (-5 to +4)***\n\n- sRDPHyQDPH\n- LDNPDyQQDF\n- AAAAAsGGAG\n",
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