{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/85416"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/85416","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Protein Structure Analysis and Prediction","abstract":"The protein structure prediction problem consists of three separate subproblems: modeling the protein structure, modeling the protein energetics, and the problem of searching for low-energy structures in high-dimensional space in the presence of many local minima. This study presents a new approach to protein structure prediction based on innovative methods in all three subproblems. The protein structure is modeled using natural coordinates, with less than one degree of freedom per residue. The protein energetics are modeled using two-dimensional statistical potentials and a linearized Hamiltonian. The two-dimensional statistical potentials are modeled as a function of the inter atomic distance and the local chain compression. The linearized Hamiltonian accounts for the fact that there is no one-to-one mapping between protein energy mechanisms and statistical potentials. The search for low-energy structures is done using a phenomenological structure model consisting of local structure fragments and tertiary contacts. The search space is reduced by searching along the finite set of possible tertiary contacts. The initial implementation of this approach is producing promising results. The results of this study also suggest a new method for structural alignment using natural coordinates, and new evidence for long-range sequence patterns.","abstract_html":"The protein structure prediction problem consists of three separate subproblems: modeling the protein structure, modeling the protein energetics, and the problem of searching for low-energy structures in high-dimensional space in the presence of many local minima. This study presents a new approach to protein structure prediction based on innovative methods in all three subproblems. The protein structure is modeled using natural coordinates, with less than one degree of freedom per residue. The protein energetics are modeled using two-dimensional statistical potentials and a linearized Hamiltonian. The two-dimensional statistical potentials are modeled as a function of the inter atomic distance and the local chain compression. The linearized Hamiltonian accounts for the fact that there is no one-to-one mapping between protein energy mechanisms and statistical potentials. The search for low-energy structures is done using a phenomenological structure model consisting of local structure fragments and tertiary contacts. The search space is reduced by searching along the finite set of possible tertiary contacts. The initial implementation of this approach is producing promising results. The results of this study also suggest a new method for structural alignment using natural coordinates, and new evidence for long-range sequence patterns.","abstract_has_math":false,"creators":["Hunter, Cornelius George"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Biophysics and Computational Biology","degree_department":null,"school":null,"contributors":["Subramaniam, Shankar"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015-09-25T22:46:00Z","date_published":"2015-09-25T22:46:00Z","updated_at":"2026-07-22T22:26:25Z","subjects":["Biology, Molecular"],"languages":["eng"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["(MiAaPQ)AAI3017108"],"render_values":[{"text":"(MiAaPQ)AAI3017108","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/2142/85416","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Subramaniam, Shankar"]},{"key":"dc:creator","label":"Author","values":["Hunter, Cornelius George"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2015-09-25T22:46:00Z","10000-01-01","2001"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Biophysics and Computational Biology"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Biology, Molecular"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/85416","(MiAaPQ)AAI3017108"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["The protein structure prediction problem consists of three separate subproblems: modeling the protein structure, modeling the protein energetics, and the problem of searching for low-energy structures in high-dimensional space in the presence of many local minima. This study presents a new approach to protein structure prediction based on innovative methods in all three subproblems. The protein structure is modeled using natural coordinates, with less than one degree of freedom per residue. The protein energetics are modeled using two-dimensional statistical potentials and a linearized Hamiltonian. The two-dimensional statistical potentials are modeled as a function of the inter atomic distance and the local chain compression. The linearized Hamiltonian accounts for the fact that there is no one-to-one mapping between protein energy mechanisms and statistical potentials. The search for low-energy structures is done using a phenomenological structure model consisting of local structure fragments and tertiary contacts. The search space is reduced by searching along the finite set of possible tertiary contacts. The initial implementation of this approach is producing promising results. The results of this study also suggest a new method for structural alignment using natural coordinates, and new evidence for long-range sequence patterns.","Made available in DSpace on 2015-09-25T22:46:00Z (GMT). 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This study presents a new approach to protein structure prediction based on innovative methods in all three subproblems. The protein structure is modeled using natural coordinates, with less than one degree of freedom per residue. The protein energetics are modeled using two-dimensional statistical potentials and a linearized Hamiltonian. The two-dimensional statistical potentials are modeled as a function of the inter atomic distance and the local chain compression. The linearized Hamiltonian accounts for the fact that there is no one-to-one mapping between protein energy mechanisms and statistical potentials. The search for low-energy structures is done using a phenomenological structure model consisting of local structure fragments and tertiary contacts. The search space is reduced by searching along the finite set of possible tertiary contacts. The initial implementation of this approach is producing promising results. 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