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Deep Learning in Phosphoproteomics: Methods and Application in Cancer Drug Discovery

Protein phosphorylation is a key post-translational modification (PTM) that is a central regulatory mechanism of many cellular signaling pathways. Several protein kinases and phosphatases precisely control this biochemical process. Defects in the functions of these proteins have been implicated in m...

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Autores principales: Varshney, Neha, Mishra, Abhinava K.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10204361/
https://www.ncbi.nlm.nih.gov/pubmed/37218921
http://dx.doi.org/10.3390/proteomes11020016
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author Varshney, Neha
Mishra, Abhinava K.
author_facet Varshney, Neha
Mishra, Abhinava K.
author_sort Varshney, Neha
collection PubMed
description Protein phosphorylation is a key post-translational modification (PTM) that is a central regulatory mechanism of many cellular signaling pathways. Several protein kinases and phosphatases precisely control this biochemical process. Defects in the functions of these proteins have been implicated in many diseases, including cancer. Mass spectrometry (MS)-based analysis of biological samples provides in-depth coverage of phosphoproteome. A large amount of MS data available in public repositories has unveiled big data in the field of phosphoproteomics. To address the challenges associated with handling large data and expanding confidence in phosphorylation site prediction, the development of many computational algorithms and machine learning-based approaches have gained momentum in recent years. Together, the emergence of experimental methods with high resolution and sensitivity and data mining algorithms has provided robust analytical platforms for quantitative proteomics. In this review, we compile a comprehensive collection of bioinformatic resources used for the prediction of phosphorylation sites, and their potential therapeutic applications in the context of cancer.
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spelling pubmed-102043612023-05-24 Deep Learning in Phosphoproteomics: Methods and Application in Cancer Drug Discovery Varshney, Neha Mishra, Abhinava K. Proteomes Review Protein phosphorylation is a key post-translational modification (PTM) that is a central regulatory mechanism of many cellular signaling pathways. Several protein kinases and phosphatases precisely control this biochemical process. Defects in the functions of these proteins have been implicated in many diseases, including cancer. Mass spectrometry (MS)-based analysis of biological samples provides in-depth coverage of phosphoproteome. A large amount of MS data available in public repositories has unveiled big data in the field of phosphoproteomics. To address the challenges associated with handling large data and expanding confidence in phosphorylation site prediction, the development of many computational algorithms and machine learning-based approaches have gained momentum in recent years. Together, the emergence of experimental methods with high resolution and sensitivity and data mining algorithms has provided robust analytical platforms for quantitative proteomics. In this review, we compile a comprehensive collection of bioinformatic resources used for the prediction of phosphorylation sites, and their potential therapeutic applications in the context of cancer. MDPI 2023-05-02 /pmc/articles/PMC10204361/ /pubmed/37218921 http://dx.doi.org/10.3390/proteomes11020016 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Varshney, Neha
Mishra, Abhinava K.
Deep Learning in Phosphoproteomics: Methods and Application in Cancer Drug Discovery
title Deep Learning in Phosphoproteomics: Methods and Application in Cancer Drug Discovery
title_full Deep Learning in Phosphoproteomics: Methods and Application in Cancer Drug Discovery
title_fullStr Deep Learning in Phosphoproteomics: Methods and Application in Cancer Drug Discovery
title_full_unstemmed Deep Learning in Phosphoproteomics: Methods and Application in Cancer Drug Discovery
title_short Deep Learning in Phosphoproteomics: Methods and Application in Cancer Drug Discovery
title_sort deep learning in phosphoproteomics: methods and application in cancer drug discovery
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10204361/
https://www.ncbi.nlm.nih.gov/pubmed/37218921
http://dx.doi.org/10.3390/proteomes11020016
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