{"id":{"repo_id":"rice","oai_identifier":"oai:repository.rice.edu:1911/96198"},"canonical_url":"https://search.dev.ndltd.org/etd/rice/oai:repository.rice.edu:1911/96198","repository":{"repo_id":"rice","name":"Rice University","base_url":"https://repository.rice.edu/server/oai/request"},"display":{"title":"Improving Protein Conformational Sampling by Using Guiding Projections","abstract":"The ability of a protein to perform its function is mainly dened by the spatial shape it exists in and the way the protein alternates between several stable shapes. To prevent or cure diseases related to protein malfunctioning we study the conformational space of proteins. Sampling-based motion planning algorithms from the eld of robotics have been very successful at this task. However, studying the conformational space of large proteins with hundreds or thousands of Degrees of Freedom remains a big challenge. In this work we investigate how the dimensionality curse can be mitigated by means of low-dimensional projections. Our experiments demonstrate that incorporating the information available on the studied protein into the projection can benefit the conformational exploration process. The techniques we developed to generate efficient low-dimensional projections can enable sampling-based planners to study protein systems, such as viruses, that are currently too large to be investigated by other methods.","abstract_html":"The ability of a protein to perform its function is mainly dened by the spatial shape it exists in and the way the protein alternates between several stable shapes. To prevent or cure diseases related to protein malfunctioning we study the conformational space of proteins. Sampling-based motion planning algorithms from the eld of robotics have been very successful at this task. However, studying the conformational space of large proteins with hundreds or thousands of Degrees of Freedom remains a big challenge. In this work we investigate how the dimensionality curse can be mitigated by means of low-dimensional projections. Our experiments demonstrate that incorporating the information available on the studied protein into the projection can benefit the conformational exploration process. The techniques we developed to generate efficient low-dimensional projections can enable sampling-based planners to study protein systems, such as viruses, that are currently too large to be investigated by other methods.","abstract_has_math":false,"creators":["Novinskaya, Anastasia"],"institution":"Rice University","degree_name":"Master of Science","degree_level":"Masters","degree_discipline":"Engineering","degree_department":null,"school":null,"contributors":[],"advisors":["Kavraki, Lydia"],"committee_chairs":[],"committee_members":[],"year":2016,"date_issued":"2016-04-08","date_published":"2016-04-08","updated_at":"2026-07-24T04:10:22Z","subjects":["Protein conformational sampling","sampling-based methods"],"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/1911/96198","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Kavraki, Lydia"]},{"key":"dc:creator","label":"Author","values":["Novinskaya, Anastasia"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2017-08-02T16:35:59Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2017-08-02T16:35:59Z"]},{"key":"dc:date.issued","label":"Date","values":["2016-04-08"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science"]},{"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 conformational sampling","sampling-based methods"]}]},{"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. 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."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/1911/96198"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The ability of a protein to perform its function is mainly dened by the spatial shape it exists in and the way the protein alternates between several stable shapes. To prevent or cure diseases related to protein malfunctioning we study the conformational space of proteins. Sampling-based motion planning algorithms from the eld of robotics have been very successful at this task. However, studying the conformational space of large proteins with hundreds or thousands of Degrees of Freedom remains a big challenge. In this work we investigate how the dimensionality curse can be mitigated by means of low-dimensional projections. Our experiments demonstrate that incorporating the information available on the studied protein into the projection can benefit the conformational exploration process. The techniques we developed to generate efficient low-dimensional projections can enable sampling-based planners to study protein systems, such as viruses, that are currently too large to be investigated by other methods."]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Improving Protein Conformational Sampling by Using Guiding Projections"]}]}],"canonical_facts":{"dc:contributor.advisor":["Kavraki, Lydia"],"dc:creator":["Novinskaya, Anastasia"],"dc:date.accessioned":["2017-08-02T16:35:59Z"],"dc:date.available":["2017-08-02T16:35:59Z"],"dc:date.issued":["2016-04-08"],"dc:description.abstract":["The ability of a protein to perform its function is mainly dened by the spatial shape it exists in and the way the protein alternates between several stable shapes. To prevent or cure diseases related to protein malfunctioning we study the conformational space of proteins. Sampling-based motion planning algorithms from the eld of robotics have been very successful at this task. However, studying the conformational space of large proteins with hundreds or thousands of Degrees of Freedom remains a big challenge. In this work we investigate how the dimensionality curse can be mitigated by means of low-dimensional projections. Our experiments demonstrate that incorporating the information available on the studied protein into the projection can benefit the conformational exploration process. The techniques we developed to generate efficient low-dimensional projections can enable sampling-based planners to study protein systems, such as viruses, that are currently too large to be investigated by other methods."],"dc:format.mimetype":["application/pdf"],"dc:identifier.uri":["https://hdl.handle.net/1911/96198"],"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 conformational sampling","sampling-based methods"],"dc:title":["Improving Protein Conformational Sampling by Using Guiding Projections"],"dc:type":["Thesis"],"thesis:degree_discipline":["Engineering"],"thesis:degree_level":["Masters"],"thesis:degree_name":["Master of Science"],"thesis:institution_name":["Rice University"]},"updated_at":"2026-07-24T04:10:22Z"}