Feature analysis and in silico prediction of lower solubility proteins in three eukaryotic model systems
department or school: Science, Faculty of
data provider: DSpace
degree campus: UBCV
creator: Chan, Gerard
issued: 2015-11-10T07:49:31
issued: 2015
related degree: Master of Science - MSc
degree grantor: University of British Columbia
description: Regulation of protein solubility, or the ability of proteins to remain soluble within the cell, is an important part of protein homeostasis. This is highlighted with the disruption of protein homeostasis and dysregulation of solubility being associated with various neurodegenerative diseases. Using quantitative mass spectrometry and computational analyses, we identify low solubility proteins under unstressed conditions in three eukaryotic model systems: yeast cells, human neuroblastoma cells, and mouse brain tissue. Using an internal reference, we account for protein abundance, and allow for the analysis of proteins based on their partitioning between the soluble and insoluble fractions, rather than purely on their abundance within the insoluble fraction. We identified several intrinsic traits such as length, disorder, abundance, molecular recognition features, and low complexity regions which are correlated with protein solubility. These features have been previously shown to be associated with protein-protein interactions. This suggests that, under unstressed conditions, lower solubility in proteins may be linked to functional aggregation, rather than aberrant aggregation. We then present two predictors which may be used to predict the in vivo solubility of proteins, built using the many traits examined in this work. The linear regression model is able to give estimates of protein solubility, although proteins near the threshold between low and normal solubility may be misclassified. The Support Vector Machine is able to reliably distinguish between low and high solubility proteins, but is unable to reliably distinguish low and normal solubility proteins. We have identified several traits that distinguish low solubility proteins from other proteins, as well as developed two models that are able to estimate the solubility of proteins.
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/52711
note: Feature analysis and in silico prediction of lower solubilityproteins in three eukaryotic model systemsbyGerard ChanB.Sc. (Hons.) in Life Sciences, National University of Singapore, 2012A THESIS SUBMITTED IN PARTIAL FULFILLMENTOF THE REQUIREMENTS FOR THE DEGREE OFMaster of ScienceinTHE FACULTY OF GRADUATE AND POSTDOCTORALSTUDIES(Genome Science and Technology)The University of British Columbia(Vancouver)April 2015c© Gerard Chan, 2015AbstractRegulation of protein solubility, or the ability of proteins to remain soluble withinthe cell, is an important part of protein homeostasis. This is highlighted with thedisruption of protein homeostasis and dysregulation of solubility being associatedwith various neurodegenerative diseases. Using quantitative mass spectrometryand computational analyses, we identify low solubility proteins under unstressedconditions in three eukaryotic model systems: yeast cells, human neuroblastomacells, and mouse brain tissue. Using an internal reference, we account for proteinabundance, and allow for the analysis of proteins based on their partitioning be-tween the soluble and insoluble fractions, rather than purely on their abundancewithin the insoluble fraction. We identified several intrinsic traits such as length,disorder, abundance, molecular recognition features, and low complexity regionswhich are correlated with protein solubility. These features have been previouslyshown to be associated with protein-protein interactions. This suggests that, underunstressed conditions, lower solubility in proteins may be linked to functional ag-gregation, rather than aberrant aggregation. We then present two predictors whichmay be used to predict the in vivo solubility of proteins, built using the many traitsexamined in this work. The linear regression model is able to give estimates ofprotein solubility, although proteins near the threshold between low and normalsolubility may be misclassified. The Support Vector Machine is able to reliablydistinguish b…
has type: Thesis/Dissertation
date issued: 2015-05
is shown at: 10.14288/1.0166155
language: eng
degree discipline: Genome Science and Technology
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivs 2.5 Canada
rights URI: http://creativecommons.org/licenses/by-nc-nd/2.5/ca/
scholar level: Graduate
type: Text
identifier URI: http://hdl.handle.net/2429/52711