{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/72401"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/72401","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Information-Based Torsion Angle Potential for Proteins and Applications","abstract":"Statistical potentials offer a significant bioinformation-based alternative to physical potentials in protein structure prediction and design. As an introduction we survey the aspects and methods involved in the derivation and applicability of various statistical potentials. The statistical potential created in this work is based on a reduced protein representation described by the residue sequence and the backbone structure including the Cbeta atoms. This potential has two components constructed separately. The component characterizes the protein interactions between the neighboring residues along the sequence. As structural variables we use the principal internal degrees of freedom, which are the backbone ϕ and psi dihedral angles corresponding to each residue. We quantify the correlation between every pair of adjacent dihedrals (ϕi, psii) and (psii, ϕ i+1) in the context of local residue sequence (Ri--1, Ri, Ri+1) by extracting and processing the related information from a database of protein loops structures. We focus in detail on an important application of our local potential: the sequence design of peptidic inhibitors for HIV-1 protease. The second component of our statistical potential is designed mainly for assessing the "non-local" protein interactions and is obtained from a distance-based potential applied to our particular protein representation. In association with an adequate search technique we find an efficient way to assemble the two parts of our statistical potential, which appropriately combines both local and non-local contributions. We successfully apply the resulting method to the protein loop modeling.","abstract_html":"Statistical potentials offer a significant bioinformation-based alternative to physical potentials in protein structure prediction and design. As an introduction we survey the aspects and methods involved in the derivation and applicability of various statistical potentials. The statistical potential created in this work is based on a reduced protein representation described by the residue sequence and the backbone structure including the Cbeta atoms. This potential has two components constructed separately. The component characterizes the protein interactions between the neighboring residues along the sequence. As structural variables we use the principal internal degrees of freedom, which are the backbone ϕ and psi dihedral angles corresponding to each residue. We quantify the correlation between every pair of adjacent dihedrals (ϕi, psii) and (psii, ϕ i+1) in the context of local residue sequence (Ri--1, Ri, Ri+1) by extracting and processing the related information from a database of protein loops structures. We focus in detail on an important application of our local potential: the sequence design of peptidic inhibitors for HIV-1 protease. The second component of our statistical potential is designed mainly for assessing the "non-local" protein interactions and is obtained from a distance-based potential applied to our particular protein representation. In association with an adequate search technique we find an efficient way to assemble the two parts of our statistical potential, which appropriately combines both local and non-local contributions. We successfully apply the resulting method to the protein loop modeling.","abstract_has_math":false,"creators":["Rata, Ionel"],"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":["Jakobsson, Eric"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2014,"date_issued":"2014-12-17T22:22:48Z","date_published":"2014-12-17T22:22:48Z","updated_at":"2026-07-22T22:26:06Z","subjects":["Biophysics, General"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["(UMI)AAI3363068"],"render_values":[{"text":"(UMI)AAI3363068","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/2142/72401","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Jakobsson, Eric"]},{"key":"dc:creator","label":"Author","values":["Rata, Ionel"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2014-12-17T22:22:48Z","10000-01-01","2009"]},{"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":["Biophysics, General"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/72401","(UMI)AAI3363068"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Statistical potentials offer a significant bioinformation-based alternative to physical potentials in protein structure prediction and design. As an introduction we survey the aspects and methods involved in the derivation and applicability of various statistical potentials. The statistical potential created in this work is based on a reduced protein representation described by the residue sequence and the backbone structure including the Cbeta atoms. This potential has two components constructed separately. The component characterizes the protein interactions between the neighboring residues along the sequence. As structural variables we use the principal internal degrees of freedom, which are the backbone ϕ and psi dihedral angles corresponding to each residue. We quantify the correlation between every pair of adjacent dihedrals (ϕi, psii) and (psii, ϕ i+1) in the context of local residue sequence (Ri--1, Ri, Ri+1) by extracting and processing the related information from a database of protein loops structures. We focus in detail on an important application of our local potential: the sequence design of peptidic inhibitors for HIV-1 protease. The second component of our statistical potential is designed mainly for assessing the "non-local" protein interactions and is obtained from a distance-based potential applied to our particular protein representation. In association with an adequate search technique we find an efficient way to assemble the two parts of our statistical potential, which appropriately combines both local and non-local contributions. We successfully apply the resulting method to the protein loop modeling.","Made available in DSpace on 2014-12-17T22:22:48Z (GMT). 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As an introduction we survey the aspects and methods involved in the derivation and applicability of various statistical potentials. The statistical potential created in this work is based on a reduced protein representation described by the residue sequence and the backbone structure including the Cbeta atoms. This potential has two components constructed separately. The component characterizes the protein interactions between the neighboring residues along the sequence. As structural variables we use the principal internal degrees of freedom, which are the backbone ϕ and psi dihedral angles corresponding to each residue. We quantify the correlation between every pair of adjacent dihedrals (ϕi, psii) and (psii, ϕ i+1) in the context of local residue sequence (Ri--1, Ri, Ri+1) by extracting and processing the related information from a database of protein loops structures. We focus in detail on an important application of our local potential: the sequence design of peptidic inhibitors for HIV-1 protease. The second component of our statistical potential is designed mainly for assessing the "non-local" protein interactions and is obtained from a distance-based potential applied to our particular protein representation. In association with an adequate search technique we find an efficient way to assemble the two parts of our statistical potential, which appropriately combines both local and non-local contributions. We successfully apply the resulting method to the protein loop modeling.","Made available in DSpace on 2014-12-17T22:22:48Z (GMT). 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