kinase-library 1.0.2

The Kinase Library Project: a Global Atlas of the Human Protein Kinome

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Click here for The Kinase Library Web Tool

   

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The Kinase Library is a comprehensive Python package for analyzing phosphoproteomics data, focusing on kinase-substrate relationships. It provides tools for kinase prediction, enrichment analysis, and visualization, enabling researchers to gain insights into kinase activities and signaling pathways from phosphoproteomics datasets.

Features

• Kinase Prediction: Predict potential kinases responsible for phosphorylation sites using a built-in kinase-substrate prediction algorithm.
• Enrichment Analysis: Perform kinase enrichment analysis using binary enrichment or differential phosphorylation analysis.
• Motif Enrichment Analysis (MEA): Identify kinases potentially regulated in your dataset using MEA with the GSEA algorithm.
• Visualization: Generate volcano plots, bubble maps, and other visualizations to interpret enrichment results.
• Downstream Substrate Identification: Explore putative downstream substrates of enriched kinases.

Installation

You can install the package via pip:

pip install kinase-library

Getting Started

The Kinase Library package offers several tools for analyzing kinase phosphorylation sites. Below are some basic examples to help you get started. Please refer to Notebooks for more comprehensive usage.

Example: Identify kinases capable of phosphorylating a site using Substrate

import kinase_library as kl

# Create a Substrate object with a target sequence (example: p53 S33)
s = kl.Substrate('PSVEPPLsQETFSDL')  # Lowercase 's' indicates a phosphoserine

# Predict potential kinase interactions for the substrate
s.predict()

Here’s an example of the output you can expect from using the Substrate.predict() function.

Kinase	Score	Score Rank	Percentile	Percentile Rank
ATM	5.0385	1	99.83	1
SMG1	4.2377	2	99.77	2
ATR	3.5045	4	99.69	3
DNAPK	3.8172	3	99.21	4
FAM20C	3.1716	5	95.23	5
...	...	...	...	...
BRAF	-4.4003	241	7.86	305
AKT2	-5.6530	283	6.79	306
P70S6KB	-3.9915	221	6.64	307
NEK3	-8.2455	309	4.85	308
P70S6K	-7.2917	305	4.19	309

Example: Identify kinases capable of phosphorylating a site for multiple sites using PhosphoProteomics

Assuming you have a CSV file called "pps_data.csv" containing the following list of phosphosites:

uniprot,protein,gene,description,position,residue,best_localization_prob,sequence window
Q15149,PLEC,PLEC,Plectin,113,T,1.000000,MVMPARRtPHVQAVQ
O43865,SAHH2,AHCYL1,S-adenosylhomocysteine hydrolase-like protein 1,29,S,0.911752,EDAEKysFMATVT
Q8WX93,PALLD,PALLD,Palladin,35,S,0.999997,PGLsAFLSQEEINKS
Q96NY7,CLIC6,CLIC6,Chloride intracellular channel protein 6,322,S,1.000000,AGESAGRsPG_____
Q02790,FKBP4,FKBP4,Peptidyl-prolyl cis-trans isomerase FKBP4,336,S,0.999938,PDRRLGKLKLQAFsAXXESCHCGGPSA

import kinase_library as kl
import pandas as pd

phosphosites_data = pd.read_csv('pps_data.csv')
pps = kl.PhosphoProteomics(phosphosites_data, seq_col='sequence window')
pps.predict(kin_type='ser_thr')

This is the expected output from using the PhosphoProteomics.predict() function.

uniprot	protein	gene	description	position	residue	best_localization_prob	sequence window	phos_res	Sequence	...	YSK1_percentile	YSK1_percentile_rank	YSK4_score	YSK4_score_rank	YSK4_percentile	YSK4_percentile_rank	ZAK_score	ZAK_score_rank	ZAK_percentile	ZAK_percentile_rank
Q15149	PLEC	PLEC	Plectin	113	T	1.000000	MVMPARRtPHVQAVQ	t	MVMPARRtPHVQAVQ	...	80.44	130	-3.004	249	32.17	244	-1.210	159	80.90	128
O43865	SAHH2	AHCYL1	S-adenosylhomocysteine hydrolase-like protein 1	29	S	0.911752	EDAEKysFMATVT	s	_EDAEKYsFMATVT_	...	63.85	150	-1.431	125	71.22	108	-1.481	129	76.87	82
Q8WX93	PALLD	PALLD	Palladin	35	S	0.999997	PGLsAFLSQEEINKS	s	PGLSAFLsQEEINKS	...	11.73	250	-2.567	128	44.07	119	-4.899	228	6.80	291
Q96NY7	CLIC6	CLIC6	Chloride intracellular channel protein 6	322	S	1.000000	AGESAGRsPG_____	s	AGESAGRsPG_____	...	52.69	134	-3.300	213	24.37	284	-2.839	182	47.81	163
Q02790	FKBP4	FKBP4	Peptidyl-prolyl cis-trans isomerase FKBP4	336	S	0.999938	PDRRLGKLKLQAFsAXXESCHCGGPSA	s	KLKLQAFsAXXESCH	...	46.82	216	-2.265	186	52.25	178	-3.020	240	43.29	233

Citations

Please cite the following papers when using this package:

For the serine/threonine kinome:

Johnson, J. L., Yaron, T. M., Huntsman, E. M., Kerelsky, A., Song, J., Regev, A., ... & Cantley, L. C. (2023). An atlas of substrate specificities for the human serine/threonine kinome. Nature, 613(7945), 759-766. https://doi.org/10.1074/mcp.TIR118.000943

For the tyrosine kinome:

Yaron-Barir, T. M., Joughin, B. A., Huntsman, E. M., Kerelsky, A., Cizin, D. M., Cohen, B. M., ... & Johnson, J. L. (2024). The intrinsic substrate specificity of the human tyrosine kinome. Nature, 1-8. https://doi.org/10.1038/s41586-024-07407-y

If you are using the MEA tool, please also cite:

Fang, Z., Liu, X., & Peltz, G. (2023). GSEApy: a comprehensive package for performing gene set enrichment analysis in Python. Bioinformatics, 39(1), btac757. https://doi.org/10.1093/bioinformatics/btac757

License

This package is distributed under the Creative Commons License. See LICENSE for more information.