{"id":{"repo_id":"rice","oai_identifier":"oai:repository.rice.edu:1911/105931"},"canonical_url":"https://search.dev.ndltd.org/etd/rice/oai:repository.rice.edu:1911/105931","repository":{"repo_id":"rice","name":"Rice University","base_url":"https://repository.rice.edu/server/oai/request"},"display":{"title":"Data Driven Modeling of Proteins","abstract":"Proteins are tiny molecular machines that perform the vast majority of the functions in living cells. In order for the protein to perform its function, it has to be able to fold from a disordered coil into a specific compact structure. Two new computational methods are developed that take advantage of the large amount of data generated in both experiments and computer simulations in order to better understand how proteins work. The first method (pyODEM) improves the modeling of proteins on the global scale, while a second method (pyFrustration) probes the protein's local frustration that might impede the folding process. Use of these methods allows us to construct more dynamically accurate protein models and improves our understanding of how a protein folds and performs its function.","abstract_html":"Proteins are tiny molecular machines that perform the vast majority of the functions in living cells. In order for the protein to perform its function, it has to be able to fold from a disordered coil into a specific compact structure. Two new computational methods are developed that take advantage of the large amount of data generated in both experiments and computer simulations in order to better understand how proteins work. The first method (pyODEM) improves the modeling of proteins on the global scale, while a second method (pyFrustration) probes the protein's local frustration that might impede the folding process. Use of these methods allows us to construct more dynamically accurate protein models and improves our understanding of how a protein folds and performs its function.","abstract_has_math":false,"creators":["Chen, Justin"],"institution":"Rice University","degree_name":"Doctor of Philosophy","degree_level":"Doctoral","degree_discipline":"Natural Sciences","degree_department":null,"school":null,"contributors":[],"advisors":["Clementi, Cecilia"],"committee_chairs":[],"committee_members":["Onuchic, José"],"year":2019,"date_issued":"2019-03-20","date_published":"2019-03-20","updated_at":"2026-07-24T04:10:41Z","subjects":["protein","physics","frustration","protein folding","molecular dynamics","protein design","non-linear optimization"],"languages":["eng"],"rights":["Copyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/1911/105931","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Clementi, Cecilia"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Onuchic, José"]},{"key":"dc:creator","label":"Author","values":["Chen, Justin"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2019-05-17T18:31:53Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2019-11-01T05:01:18Z"]},{"key":"dc:date.issued","label":"Date","values":["2019-03-20"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Natural Sciences"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Doctoral"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Rice University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["protein","physics","frustration","protein folding","molecular dynamics","protein design","non-linear optimization"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright is held by the author, unless otherwise indicated. 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The first method (pyODEM) improves the modeling of proteins on the global scale, while a second method (pyFrustration) probes the protein's local frustration that might impede the folding process. Use of these methods allows us to construct more dynamically accurate protein models and improves our understanding of how a protein folds and performs its function."]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Data Driven Modeling of Proteins"]}]}],"canonical_facts":{"dc:contributor.advisor":["Clementi, Cecilia"],"dc:contributor.committeemember":["Onuchic, José"],"dc:creator":["Chen, Justin"],"dc:date.accessioned":["2019-05-17T18:31:53Z"],"dc:date.available":["2019-11-01T05:01:18Z"],"dc:date.issued":["2019-03-20"],"dc:description.abstract":["Proteins are tiny molecular machines that perform the vast majority of the functions in living cells. In order for the protein to perform its function, it has to be able to fold from a disordered coil into a specific compact structure. Two new computational methods are developed that take advantage of the large amount of data generated in both experiments and computer simulations in order to better understand how proteins work. The first method (pyODEM) improves the modeling of proteins on the global scale, while a second method (pyFrustration) probes the protein's local frustration that might impede the folding process. Use of these methods allows us to construct more dynamically accurate protein models and improves our understanding of how a protein folds and performs its function."],"dc:format.mimetype":["application/pdf"],"dc:identifier.uri":["https://hdl.handle.net/1911/105931"],"dc:language.iso":["eng"],"dc:rights":["Copyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder."],"dc:subject":["protein","physics","frustration","protein folding","molecular dynamics","protein design","non-linear optimization"],"dc:title":["Data Driven Modeling of Proteins"],"dc:type":["Thesis"],"thesis:degree_discipline":["Natural Sciences"],"thesis:degree_level":["Doctoral"],"thesis:degree_name":["Doctor of Philosophy"],"thesis:institution_name":["Rice University"]},"updated_at":"2026-07-24T04:10:41Z"}