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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.