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

Reproduce datasets & figures

Follow the instructions in katlas_raw: https://github.com/sky1ove/katlas_raw

Need to install the package via: pip install 'python-katlas[dev]' -U

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

Tutorials on Colab

• 1. Substrate scoring on a single substrate sequence
• 2. High throughput substrate scoring on phosphoproteomics dataset
• 3. Kinase enrichment analysis for AKT inhibitor

Install

pip install python-katlas -U

To use other modules besides the core, do pip install 'python-katlas[dev]' -U

Import

from katlas.core 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

Single sequence as input

CDDM, all capital

predict_kinase('AAAAAAASGGAGSDN',**param_CDDM_upper)

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

kinase
PAK6     2.032
ULK3     2.032
PRKX     2.012
ATR      1.991
PRKD1    1.988
         ...  
DDR2     0.928
EPHA4    0.928
TEK      0.921
KIT      0.915
FGFR3    0.910
Length: 289, dtype: float64

CDDM, with lower case indicating phosphorylation status

predict_kinase('AAAAAAAsGGAGsDN',**param_CDDM)

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

kinase
ULK3     1.987
PAK6     1.981
PRKD1    1.946
PIM3     1.944
PRKX     1.939
         ...  
EPHA4    0.905
EGFR     0.900
TEK      0.898
FGFR3    0.894
KIT      0.882
Length: 289, dtype: float64

PSPA, with lower case indicating phosphorylation status

predict_kinase('AEEKEyHsEGG',**param_PSPA).head()

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
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).

predict_kinase('AEEKEyHSEGG',**param_PSPA).head(10)

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
PDHK1_TYR    1.922
RET          1.732
MATK         1.688
FLT1         1.627
BMPR2_TYR    1.456
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',**param_PSPA_y, pct_ref = y_pct)

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

	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

93 rows × 2 columns

High-throughput substrate scoring on a dataframe

Load your csv

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

Load a demo df

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

	site_seq	gene_site
0	VDDEKGDSNDDYDSA	A0A075B6Q4_S24
1	YDSAGLLSDEDCMSV	A0A075B6Q4_S35
2	IADHLFWSEETKSRF	A0A075B6Q4_S57
3	KSRFTEYSMTSSVMR	A0A075B6Q4_S68
4	FTEYSMTSSVMRRNE	A0A075B6Q4_S71

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',**param_CDDM_upper)
results

input dataframe has a length 5
Preprocessing
Finish preprocessing
Calculating position: [-7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7]

100%|██████████| 289/289 [00:05<00:00, 56.64it/s]

kinase	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

Phosphorylation sites

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)

	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

Ochoa et al. human phosphoproteome

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

	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

PhosphoSitePlus human phosphorylation site

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

	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

Unique sites of combined Ochoa & PhosphoSitePlus

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

	site_seq	gene_site	gene	source	num_site	acceptor	-7	-6	-5	-4	...	-2	-1	0	1	2	3	4	5	6	7
0	AAAAAAASGGAGSDN	PBX1_S136	PBX1	ochoa	1	S	A	A	A	A	...	A	A	S	G	G	A	G	S	D	N
1	AAAAAAASGGGVSPD	PBX2_S146	PBX2	ochoa	1	S	A	A	A	A	...	A	A	S	G	G	G	V	S	P	D
2	AAAAAAASGVTTGKP	CLASR_S349	CLASR	ochoa	1	S	A	A	A	A	...	A	A	S	G	V	T	T	G	K	P

3 rows × 21 columns

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