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KinPred: A unified and sustainable approach for harnessing proteome-level human kinase-substrate predictions
Tyrosine and serine/threonine kinases are essential regulators of cell processes and are important targets for human therapies. Unfortunately, very little is known about specific kinase-substrate relationships, making it difficult to infer meaning from dysregulated phosphoproteomic datasets or for r...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7895412/ https://www.ncbi.nlm.nih.gov/pubmed/33556051 http://dx.doi.org/10.1371/journal.pcbi.1008681 |
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author | Xue, Bingjie Jordan, Benjamin Rizvi, Saqib Naegle, Kristen M. |
author_facet | Xue, Bingjie Jordan, Benjamin Rizvi, Saqib Naegle, Kristen M. |
author_sort | Xue, Bingjie |
collection | PubMed |
description | Tyrosine and serine/threonine kinases are essential regulators of cell processes and are important targets for human therapies. Unfortunately, very little is known about specific kinase-substrate relationships, making it difficult to infer meaning from dysregulated phosphoproteomic datasets or for researchers to identify possible kinases that regulate specific or novel phosphorylation sites. The last two decades have seen an explosion in algorithms to extrapolate from what little is known into the larger unknown—predicting kinase relationships with site-specific substrates using a variety of approaches that include the sequence-specificity of kinase catalytic domains and various other factors, such as evolutionary relationships, co-expression, and protein-protein interaction networks. Unfortunately, a number of limitations prevent researchers from easily harnessing these resources, such as loss of resource accessibility, limited information in publishing that results in a poor mapping to a human reference, and not being updated to match the growth of the human phosphoproteome. Here, we propose a methodological framework for publishing predictions in a unified way, which entails ensuring predictions have been run on a current reference proteome, mapping the same substrates and kinases across resources to a common reference, filtering for the human phosphoproteome, and providing methods for updating the resource easily in the future. We applied this framework on three currently available resources, published in the last decade, which provide kinase-specific predictions in the human proteome. Using the unified datasets, we then explore the role of study bias, the emergent network properties of these predictive algorithms, and comparisons within and between predictive algorithms. The combination of the code for unification and analysis, as well as the unified predictions are available under the resource we named KinPred. We believe this resource will be useful for a wide range of applications and establishes best practices for long-term usability and sustainability for new and existing predictive algorithms. |
format | Online Article Text |
id | pubmed-7895412 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-78954122021-03-01 KinPred: A unified and sustainable approach for harnessing proteome-level human kinase-substrate predictions Xue, Bingjie Jordan, Benjamin Rizvi, Saqib Naegle, Kristen M. PLoS Comput Biol Research Article Tyrosine and serine/threonine kinases are essential regulators of cell processes and are important targets for human therapies. Unfortunately, very little is known about specific kinase-substrate relationships, making it difficult to infer meaning from dysregulated phosphoproteomic datasets or for researchers to identify possible kinases that regulate specific or novel phosphorylation sites. The last two decades have seen an explosion in algorithms to extrapolate from what little is known into the larger unknown—predicting kinase relationships with site-specific substrates using a variety of approaches that include the sequence-specificity of kinase catalytic domains and various other factors, such as evolutionary relationships, co-expression, and protein-protein interaction networks. Unfortunately, a number of limitations prevent researchers from easily harnessing these resources, such as loss of resource accessibility, limited information in publishing that results in a poor mapping to a human reference, and not being updated to match the growth of the human phosphoproteome. Here, we propose a methodological framework for publishing predictions in a unified way, which entails ensuring predictions have been run on a current reference proteome, mapping the same substrates and kinases across resources to a common reference, filtering for the human phosphoproteome, and providing methods for updating the resource easily in the future. We applied this framework on three currently available resources, published in the last decade, which provide kinase-specific predictions in the human proteome. Using the unified datasets, we then explore the role of study bias, the emergent network properties of these predictive algorithms, and comparisons within and between predictive algorithms. The combination of the code for unification and analysis, as well as the unified predictions are available under the resource we named KinPred. We believe this resource will be useful for a wide range of applications and establishes best practices for long-term usability and sustainability for new and existing predictive algorithms. Public Library of Science 2021-02-08 /pmc/articles/PMC7895412/ /pubmed/33556051 http://dx.doi.org/10.1371/journal.pcbi.1008681 Text en © 2021 Xue et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Xue, Bingjie Jordan, Benjamin Rizvi, Saqib Naegle, Kristen M. KinPred: A unified and sustainable approach for harnessing proteome-level human kinase-substrate predictions |
title | KinPred: A unified and sustainable approach for harnessing proteome-level human kinase-substrate predictions |
title_full | KinPred: A unified and sustainable approach for harnessing proteome-level human kinase-substrate predictions |
title_fullStr | KinPred: A unified and sustainable approach for harnessing proteome-level human kinase-substrate predictions |
title_full_unstemmed | KinPred: A unified and sustainable approach for harnessing proteome-level human kinase-substrate predictions |
title_short | KinPred: A unified and sustainable approach for harnessing proteome-level human kinase-substrate predictions |
title_sort | kinpred: a unified and sustainable approach for harnessing proteome-level human kinase-substrate predictions |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7895412/ https://www.ncbi.nlm.nih.gov/pubmed/33556051 http://dx.doi.org/10.1371/journal.pcbi.1008681 |
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