<?xml version="1.0" encoding="UTF-8"?><?xml-stylesheet type="text/xsl" href="static/style.xsl"?><OAI-PMH xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd"><responseDate>2026-09-18T22:33:16Z</responseDate><request verb="GetRecord" identifier="oai:dspace.mit.edu:1721.1/104575" metadataPrefix="dim">https://dspace.mit.edu/server/oai/request</request><GetRecord><record><header><identifier>oai:dspace.mit.edu:1721.1/104575</identifier><datestamp>2026-06-16T18:13:57Z</datestamp><setSpec>com_1721.1_7582</setSpec><setSpec>com_1721.1_7581</setSpec><setSpec>col_1721.1_131022</setSpec></header><metadata><dim:dim xmlns:dim="http://www.dspace.org/xmlns/dspace/dim" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:doc="http://www.lyncode.com/xoai" xsi:schemaLocation="http://www.dspace.org/xmlns/dspace/dim http://www.dspace.org/schema/dim.xsd">
   <dim:field mdschema="dc" element="contributor" qualifier="advisor" lang="en_US">Jeremy England.</dim:field>
   <dim:field mdschema="dc" element="contributor" qualifier="author" lang="en_US">Brock, Kelly Paige</dim:field>
   <dim:field mdschema="dc" element="contributor" qualifier="other" lang="en_US">Massachusetts Institute of Technology. Computational and Systems Biology Program.</dim:field>
   <dim:field mdschema="dc" element="contributor" qualifier="department">Massachusetts Institute of Technology. Computational and Systems Biology Program</dim:field>
   <dim:field mdschema="dc" element="date" qualifier="accessioned">2016-09-30T19:36:27Z</dim:field>
   <dim:field mdschema="dc" element="date" qualifier="available">2016-09-30T19:36:27Z</dim:field>
   <dim:field mdschema="dc" element="date" qualifier="copyright" lang="en_US">2016</dim:field>
   <dim:field mdschema="dc" element="date" qualifier="issued" lang="en_US">2016</dim:field>
   <dim:field mdschema="dc" element="identifier" qualifier="uri">http://hdl.handle.net/1721.1/104575</dim:field>
   <dim:field mdschema="dc" element="identifier" qualifier="oclc" lang="en_US">958679342</dim:field>
   <dim:field mdschema="dc" element="description" lang="en_US">Thesis: Ph. D., Massachusetts Institute of Technology, Computational and Systems Biology Program, 2016.</dim:field>
   <dim:field mdschema="dc" element="description" lang="en_US">Cataloged from PDF version of thesis.</dim:field>
   <dim:field mdschema="dc" element="description" lang="en_US">Includes bibliographical references.</dim:field>
   <dim:field mdschema="dc" element="description" qualifier="abstract" lang="en_US">A protein's function in the cell depends on its structure, which in turn depends on the intracellular environment. Stress like heat shock or nutrient starvation can alter intracellular conditions, leading to protein misfolding - i.e. the inability of a protein to reach or maintain its native conformation. Since many proteins interact with each other, protein misfolding and cellular stress response must be examined both on the scale of individual protein conformational changes and on a more global level, where interaction patterns can reveal larger-scale protein responses to cellular stress. On the individual scale, one example of a protein particularly susceptible to misfolding is the human von Hippel-Lindau (VHL) tumor suppressor. When expressed in the absence of its cofactors, VHL cannot fold correctly and is quickly degraded by the cell's quality control machinery. Here, I present a biophysical characterization of a VHL mutation that confers increased resistance to misfolding. Mathematical modeling provides an explanation for this mutant's increased stability in the cell by predicting how its cofactor and chaperone interaction sites are buried or exposed in the protein's predicted conformation. On a more global level, a budding yeast cell undergoing glucose deprivation both acidifies its cytosol and exhibits widespread protein clustering. By employing a proteome-wide computational assay, I examine how this drop in pH could lead to the formation of higher order protein structures. This modeling framework also provides a rationale for why these two related phenotypes might be beneficial, since protein clustering can help regulate relevant metabolic pathways and provide protection from protein misfolding and/or degradation.</dim:field>
   <dim:field mdschema="dc" element="description" qualifier="statementofresponsibility" lang="en_US">by Kelly Paige Brock.</dim:field>
   <dim:field mdschema="dc" element="description" qualifier="degree" lang="en_US">Ph.D.</dim:field>
   <dim:field mdschema="dc" element="format" qualifier="extent" lang="en_US">135 pages</dim:field>
   <dim:field mdschema="dc" element="language" qualifier="iso" lang="en_US">eng</dim:field>
   <dim:field mdschema="dc" element="publisher" lang="en_US">Massachusetts Institute of Technology</dim:field>
   <dim:field mdschema="dc" element="rights" lang="en_US">M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.</dim:field>
   <dim:field mdschema="dc" element="rights" qualifier="uri" lang="en_US">http://dspace.mit.edu/handle/1721.1/7582</dim:field>
   <dim:field mdschema="dc" element="subject" lang="en_US">Computational and Systems Biology Program.</dim:field>
   <dim:field mdschema="dc" element="title" lang="en_US">Protein structure and interaction under environmental stress : from quality control recognition to evolution of collective behavior</dim:field>
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   	&lt;Title>Protein structure and interaction under environmental stress : from quality control recognition to evolution of collective behavior&lt;/Title>
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   	&lt;PublicationDate>2016&lt;/PublicationDate>
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        	&lt;DisplayName>Brock, Kelly Paige&lt;/DisplayName>
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    &lt;Keyword>Computational and Systems Biology Program.&lt;/Keyword>
   	&lt;Abstract>A protein&amp;apos;s function in the cell depends on its structure, which in turn depends on the intracellular environment. Stress like heat shock or nutrient starvation can alter intracellular conditions, leading to protein misfolding - i.e. the inability of a protein to reach or maintain its native conformation. Since many proteins interact with each other, protein misfolding and cellular stress response must be examined both on the scale of individual protein conformational changes and on a more global level, where interaction patterns can reveal larger-scale protein responses to cellular stress. On the individual scale, one example of a protein particularly susceptible to misfolding is the human von Hippel-Lindau (VHL) tumor suppressor. When expressed in the absence of its cofactors, VHL cannot fold correctly and is quickly degraded by the cell&amp;apos;s quality control machinery. Here, I present a biophysical characterization of a VHL mutation that confers increased resistance to misfolding. Mathematical modeling provides an explanation for this mutant&amp;apos;s increased stability in the cell by predicting how its cofactor and chaperone interaction sites are buried or exposed in the protein&amp;apos;s predicted conformation. On a more global level, a budding yeast cell undergoing glucose deprivation both acidifies its cytosol and exhibits widespread protein clustering. By employing a proteome-wide computational assay, I examine how this drop in pH could lead to the formation of higher order protein structures. This modeling framework also provides a rationale for why these two related phenotypes might be beneficial, since protein clustering can help regulate relevant metabolic pathways and provide protection from protein misfolding and/or degradation.&lt;/Abstract>
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