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Definition of the Minimal Contents for the Molecular Simulation of the Yeast Cytoplasm

The cytoplasm is a densely packed environment filled with macromolecules with hindered diffusion. Molecular simulation of the diffusion of biomolecules under such macromolecular crowding conditions requires the definition of a simulation cell with a cytoplasmic-like composition. This has been previo...

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Autores principales: Kompella, Vijay Phanindra Srikanth, Stansfield, Ian, Romano, Maria Carmen, Mancera, Ricardo L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6783697/
https://www.ncbi.nlm.nih.gov/pubmed/31632983
http://dx.doi.org/10.3389/fmolb.2019.00097
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author Kompella, Vijay Phanindra Srikanth
Stansfield, Ian
Romano, Maria Carmen
Mancera, Ricardo L.
author_facet Kompella, Vijay Phanindra Srikanth
Stansfield, Ian
Romano, Maria Carmen
Mancera, Ricardo L.
author_sort Kompella, Vijay Phanindra Srikanth
collection PubMed
description The cytoplasm is a densely packed environment filled with macromolecules with hindered diffusion. Molecular simulation of the diffusion of biomolecules under such macromolecular crowding conditions requires the definition of a simulation cell with a cytoplasmic-like composition. This has been previously done for prokaryote cells (E. coli) but not for eukaryote cells such as yeast as a model organism. Yeast proteomics datasets vary widely in terms of cell growth conditions, the technique used to determine protein composition, the reported relative abundance of proteins, and the units in which abundances are reported. We determined that the gene ontology profiles of the most abundant proteins across these datasets are similar, but their abundances vary greatly. To overcome this problem, we chose five mass spectrometry proteomics datasets that fulfilled the following criteria: high internal consistency, consistency with published experimental data, and freedom from GFP-tagging artifacts. Using these datasets, the contents of a simulation cell containing a single 80S ribosome were defined, such that the macromolecular density and the mass ratio of ribosomal-to-cytoplasmic proteins were consistent with experiment and chosen datasets. Finally, multiple tRNAs were added, consistent with their experimentally-determined number in the yeast cell. The resulting composition can be readily used in molecular simulations representative of yeast cytoplasmic macromolecular crowding conditions to characterize a variety of phenomena, such as protein diffusion, protein-protein interactions and biological processes such as protein translation.
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spelling pubmed-67836972019-10-18 Definition of the Minimal Contents for the Molecular Simulation of the Yeast Cytoplasm Kompella, Vijay Phanindra Srikanth Stansfield, Ian Romano, Maria Carmen Mancera, Ricardo L. Front Mol Biosci Molecular Biosciences The cytoplasm is a densely packed environment filled with macromolecules with hindered diffusion. Molecular simulation of the diffusion of biomolecules under such macromolecular crowding conditions requires the definition of a simulation cell with a cytoplasmic-like composition. This has been previously done for prokaryote cells (E. coli) but not for eukaryote cells such as yeast as a model organism. Yeast proteomics datasets vary widely in terms of cell growth conditions, the technique used to determine protein composition, the reported relative abundance of proteins, and the units in which abundances are reported. We determined that the gene ontology profiles of the most abundant proteins across these datasets are similar, but their abundances vary greatly. To overcome this problem, we chose five mass spectrometry proteomics datasets that fulfilled the following criteria: high internal consistency, consistency with published experimental data, and freedom from GFP-tagging artifacts. Using these datasets, the contents of a simulation cell containing a single 80S ribosome were defined, such that the macromolecular density and the mass ratio of ribosomal-to-cytoplasmic proteins were consistent with experiment and chosen datasets. Finally, multiple tRNAs were added, consistent with their experimentally-determined number in the yeast cell. The resulting composition can be readily used in molecular simulations representative of yeast cytoplasmic macromolecular crowding conditions to characterize a variety of phenomena, such as protein diffusion, protein-protein interactions and biological processes such as protein translation. Frontiers Media S.A. 2019-10-02 /pmc/articles/PMC6783697/ /pubmed/31632983 http://dx.doi.org/10.3389/fmolb.2019.00097 Text en Copyright © 2019 Kompella, Stansfield, Romano and Mancera. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Molecular Biosciences
Kompella, Vijay Phanindra Srikanth
Stansfield, Ian
Romano, Maria Carmen
Mancera, Ricardo L.
Definition of the Minimal Contents for the Molecular Simulation of the Yeast Cytoplasm
title Definition of the Minimal Contents for the Molecular Simulation of the Yeast Cytoplasm
title_full Definition of the Minimal Contents for the Molecular Simulation of the Yeast Cytoplasm
title_fullStr Definition of the Minimal Contents for the Molecular Simulation of the Yeast Cytoplasm
title_full_unstemmed Definition of the Minimal Contents for the Molecular Simulation of the Yeast Cytoplasm
title_short Definition of the Minimal Contents for the Molecular Simulation of the Yeast Cytoplasm
title_sort definition of the minimal contents for the molecular simulation of the yeast cytoplasm
topic Molecular Biosciences
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6783697/
https://www.ncbi.nlm.nih.gov/pubmed/31632983
http://dx.doi.org/10.3389/fmolb.2019.00097
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