Harnessing Machine Learning To Unravel Protein Degradation in <named-content content-type="genus-species">Escherichia coli</named-content>

ABSTRACT Degradation of intracellular proteins in Gram-negative bacteria regulates various cellular processes and serves as a quality control mechanism by eliminating damaged proteins. To understand what causes the proteolytic machinery of the cell to degrade some proteins while sparing others, we e...

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Autores principales:	Natan Nagar, Noa Ecker, Gil Loewenthal, Oren Avram, Daniella Ben-Meir, Dvora Biran, Eliora Ron, Tal Pupko
Formato:	article
Lenguaje:	EN
Publicado:	American Society for Microbiology 2021
Materias:	protein degradation proteomics machine learning SILAC Microbiology QR1-502
Acceso en línea:	https://doaj.org/article/14a68527806f4b45bf2bac5665f46503
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spelling	oai:doaj.org-article:14a68527806f4b45bf2bac5665f465032021-12-02T19:36:37ZHarnessing Machine Learning To Unravel Protein Degradation in <named-content content-type="genus-species">Escherichia coli</named-content>10.1128/mSystems.01296-202379-5077https://doaj.org/article/14a68527806f4b45bf2bac5665f465032021-02-01T00:00:00Zhttps://journals.asm.org/doi/10.1128/mSystems.01296-20https://doaj.org/toc/2379-5077ABSTRACT Degradation of intracellular proteins in Gram-negative bacteria regulates various cellular processes and serves as a quality control mechanism by eliminating damaged proteins. To understand what causes the proteolytic machinery of the cell to degrade some proteins while sparing others, we employed a quantitative pulsed-SILAC (stable isotope labeling with amino acids in cell culture) method followed by mass spectrometry analysis to determine the half-lives for the proteome of exponentially growing Escherichia coli, under standard conditions. We developed a likelihood-based statistical test to find actively degraded proteins and identified dozens of fast-degrading novel proteins. Finally, we used structural, physicochemical, and protein-protein interaction network descriptors to train a machine learning classifier to discriminate fast-degrading proteins from the rest of the proteome, achieving an area under the receiver operating characteristic curve (AUC) of 0.72. IMPORTANCE Bacteria use protein degradation to control proliferation, dispose of misfolded proteins, and adapt to physiological and environmental shifts, but the factors that dictate which proteins are prone to degradation are mostly unknown. In this study, we have used a combined computational-experimental approach to explore protein degradation in E. coli. We discovered that the proteome of E. coli is composed of three protein populations that are distinct in terms of stability and functionality, and we show that fast-degrading proteins can be identified using a combination of various protein properties. Our findings expand the understanding of protein degradation in bacteria and have implications for protein engineering. Moreover, as rapidly degraded proteins may play an important role in pathogenesis, our findings may help to identify new potential antibacterial drug targets.Natan NagarNoa EckerGil LoewenthalOren AvramDaniella Ben-MeirDvora BiranEliora RonTal PupkoAmerican Society for Microbiologyarticleprotein degradationproteomicsmachine learningSILACMicrobiologyQR1-502ENmSystems, Vol 6, Iss 1 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	protein degradation proteomics machine learning SILAC Microbiology QR1-502
spellingShingle	protein degradation proteomics machine learning SILAC Microbiology QR1-502 Natan Nagar Noa Ecker Gil Loewenthal Oren Avram Daniella Ben-Meir Dvora Biran Eliora Ron Tal Pupko Harnessing Machine Learning To Unravel Protein Degradation in <named-content content-type="genus-species">Escherichia coli</named-content>
description	ABSTRACT Degradation of intracellular proteins in Gram-negative bacteria regulates various cellular processes and serves as a quality control mechanism by eliminating damaged proteins. To understand what causes the proteolytic machinery of the cell to degrade some proteins while sparing others, we employed a quantitative pulsed-SILAC (stable isotope labeling with amino acids in cell culture) method followed by mass spectrometry analysis to determine the half-lives for the proteome of exponentially growing Escherichia coli, under standard conditions. We developed a likelihood-based statistical test to find actively degraded proteins and identified dozens of fast-degrading novel proteins. Finally, we used structural, physicochemical, and protein-protein interaction network descriptors to train a machine learning classifier to discriminate fast-degrading proteins from the rest of the proteome, achieving an area under the receiver operating characteristic curve (AUC) of 0.72. IMPORTANCE Bacteria use protein degradation to control proliferation, dispose of misfolded proteins, and adapt to physiological and environmental shifts, but the factors that dictate which proteins are prone to degradation are mostly unknown. In this study, we have used a combined computational-experimental approach to explore protein degradation in E. coli. We discovered that the proteome of E. coli is composed of three protein populations that are distinct in terms of stability and functionality, and we show that fast-degrading proteins can be identified using a combination of various protein properties. Our findings expand the understanding of protein degradation in bacteria and have implications for protein engineering. Moreover, as rapidly degraded proteins may play an important role in pathogenesis, our findings may help to identify new potential antibacterial drug targets.
format	article
author	Natan Nagar Noa Ecker Gil Loewenthal Oren Avram Daniella Ben-Meir Dvora Biran Eliora Ron Tal Pupko
author_facet	Natan Nagar Noa Ecker Gil Loewenthal Oren Avram Daniella Ben-Meir Dvora Biran Eliora Ron Tal Pupko
author_sort	Natan Nagar
title	Harnessing Machine Learning To Unravel Protein Degradation in <named-content content-type="genus-species">Escherichia coli</named-content>
title_short	Harnessing Machine Learning To Unravel Protein Degradation in <named-content content-type="genus-species">Escherichia coli</named-content>
title_full	Harnessing Machine Learning To Unravel Protein Degradation in <named-content content-type="genus-species">Escherichia coli</named-content>
title_fullStr	Harnessing Machine Learning To Unravel Protein Degradation in <named-content content-type="genus-species">Escherichia coli</named-content>
title_full_unstemmed	Harnessing Machine Learning To Unravel Protein Degradation in <named-content content-type="genus-species">Escherichia coli</named-content>
title_sort	harnessing machine learning to unravel protein degradation in <named-content content-type="genus-species">escherichia coli</named-content>
publisher	American Society for Microbiology
publishDate	2021
url	https://doaj.org/article/14a68527806f4b45bf2bac5665f46503
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Harnessing Machine Learning To Unravel Protein Degradation in <named-content content-type="genus-species">Escherichia coli</named-content>

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