The automated protein structure analysis (APSA) has been developed that describes protein structure via its backbone in a novel way. APSA generates a smooth line for the backbone which is completely described using curvature κ and torsion τ as a function of arc lengths. Diagrams of κ(s) and τ(s) reveal conformational features as typical patterns. In this way ideal and natural helices (α, 310 and π) and β-strands (left and right-handed, parallel and antiparallel) can be rapidly distinguished, their distortions classified, and a detailed picture of secondary structure developed. Such foundations make it possible to qualitatively and quantitatively compare domain structure utilizing calculated κ(s) and τ(s) patterns of proteins. Focusing on the torsion diagrams alone, 16 regions of τ(s) values that correspond to unique groups of conformations have been identified and encoded into 16 letters. The entire protein backbone is described, effectively projecting its three-dimensional (3D) conformation into a one-dimensional (1D) string of letters called the primary code (3D-ID projection), which is APSA's conformational equivalent of a protein's primary structure. The secondary structure is obtained from specific patterns of the primary code (resulting in secondary code). The letter code is used to describe supersecondary structure, which involves a unique characterization of the tum. It contains sufficient information to reconstruct the overall shape of a protein in an unambiguous 1D→3D translation step. Therefore, it is possible to classify supersecondary structure with the help of the letter code in form of a novel labeling system (F.#.M.X.O.L.N.R.U.S) that collects information on the relative orientations of tum and flanking structures (helices, strands). The overall shape of supersecondary structure is obtained by partitioning the surrounding space into octants and cones and assigning the parts of a supersecondary structure to these sub spaces via its labels. This approach can be easily extended to tertiary structure.
","abstract_html":"<p>The automated protein structure analysis (APSA) has been developed that describes protein structure via its backbone in a novel way. APSA generates a smooth line for the backbone which is completely described using curvature κ and torsion τ as a function of arc lengths. Diagrams of κ(s) and τ(s) reveal conformational features as typical patterns. In this way ideal and natural helices (α, 3<sub>10</sub> and π) and β-strands (left and right-handed, parallel and antiparallel) can be rapidly distinguished, their distortions classified, and a detailed picture of secondary structure developed. Such foundations make it possible to qualitatively and quantitatively compare domain structure utilizing calculated κ(s) and τ(s) patterns of proteins. Focusing on the torsion diagrams alone, 16 regions of τ(s) values that correspond to unique groups of conformations have been identified and encoded into 16 letters. The entire protein backbone is described, effectively projecting its three-dimensional (3D) conformation into a one-dimensional (1D) string of letters called the primary code (3D-ID projection), which is APSA's conformational equivalent of a protein's primary structure. The secondary structure is obtained from specific patterns of the primary code (resulting in secondary code). The letter code is used to describe supersecondary structure, which involves a unique characterization of the tum. It contains sufficient information to reconstruct the overall shape of a protein in an unambiguous 1D→3D translation step. Therefore, it is possible to classify supersecondary structure with the help of the letter code in form of a novel labeling system (F.#.M.X.O.L.N.R.U.S) that collects information on the relative orientations of tum and flanking structures (helices, strands). The overall shape of supersecondary structure is obtained by partitioning the surrounding space into octants and cones and assigning the parts of a supersecondary structure to these sub spaces via its labels. This approach can be easily extended to tertiary structure.</p>","abstract_has_math":false,"creators":["Ranganathan, Sushilee"],"institution":null,"degree_name":"Master of Science (M.S.)","degree_level":"Thesis - Pacific Access Restricted","degree_discipline":"Chemistry","degree_department":null,"school":null,"contributors":["Elfi Kraka"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2008,"date_issued":"2008-01-01T08:00:00Z","date_published":"2008-01-01T08:00:00Z","updated_at":"2026-07-24T05:36:46Z","subjects":["Proteins Structure Data processing","Proteins Structure Computer simulation","Bioinformatics","Chemistry","Physical Sciences and Mathematics"],"languages":[],"rights":[],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://scholarlycommons.pacific.edu/uop_etds/707","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Elfi Kraka"]},{"key":"dc:creator","label":"Author","values":["Ranganathan, Sushilee"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2018-06-29T08:49:25Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Chemistry"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis - Pacific Access Restricted"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (M.S.)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Proteins Structure Data processing","Proteins Structure Computer simulation","Bioinformatics","Chemistry","Physical Sciences and Mathematics"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholarlycommons.pacific.edu/uop_etds/707"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The automated protein structure analysis (APSA) has been developed that describes protein structure via its backbone in a novel way. APSA generates a smooth line for the backbone which is completely described using curvature κ and torsion τ as a function of arc lengths. Diagrams of κ(s) and τ(s) reveal conformational features as typical patterns. In this way ideal and natural helices (α, 310 and π) and β-strands (left and right-handed, parallel and antiparallel) can be rapidly distinguished, their distortions classified, and a detailed picture of secondary structure developed. Such foundations make it possible to qualitatively and quantitatively compare domain structure utilizing calculated κ(s) and τ(s) patterns of proteins. Focusing on the torsion diagrams alone, 16 regions of τ(s) values that correspond to unique groups of conformations have been identified and encoded into 16 letters. The entire protein backbone is described, effectively projecting its three-dimensional (3D) conformation into a one-dimensional (1D) string of letters called the primary code (3D-ID projection), which is APSA's conformational equivalent of a protein's primary structure. The secondary structure is obtained from specific patterns of the primary code (resulting in secondary code). The letter code is used to describe supersecondary structure, which involves a unique characterization of the tum. It contains sufficient information to reconstruct the overall shape of a protein in an unambiguous 1D→3D translation step. Therefore, it is possible to classify supersecondary structure with the help of the letter code in form of a novel labeling system (F.#.M.X.O.L.N.R.U.S) that collects information on the relative orientations of tum and flanking structures (helices, strands). The overall shape of supersecondary structure is obtained by partitioning the surrounding space into octants and cones and assigning the parts of a supersecondary structure to these sub spaces via its labels. This approach can be easily extended to tertiary structure.
"]},{"key":"dc:source","label":"Dc Source","values":["254"]},{"key":"dc:title","label":"Title","values":["Automated and accurate description of protein structure -- from secondary to tertiary structure"]}]}],"canonical_facts":{"dc:contributor":["Elfi Kraka"],"dc:creator":["Ranganathan, Sushilee"],"dc:date.available":["2018-06-29T08:49:25Z"],"dc:description.abstract":["The automated protein structure analysis (APSA) has been developed that describes protein structure via its backbone in a novel way. APSA generates a smooth line for the backbone which is completely described using curvature κ and torsion τ as a function of arc lengths. Diagrams of κ(s) and τ(s) reveal conformational features as typical patterns. In this way ideal and natural helices (α, 310 and π) and β-strands (left and right-handed, parallel and antiparallel) can be rapidly distinguished, their distortions classified, and a detailed picture of secondary structure developed. Such foundations make it possible to qualitatively and quantitatively compare domain structure utilizing calculated κ(s) and τ(s) patterns of proteins. Focusing on the torsion diagrams alone, 16 regions of τ(s) values that correspond to unique groups of conformations have been identified and encoded into 16 letters. The entire protein backbone is described, effectively projecting its three-dimensional (3D) conformation into a one-dimensional (1D) string of letters called the primary code (3D-ID projection), which is APSA's conformational equivalent of a protein's primary structure. The secondary structure is obtained from specific patterns of the primary code (resulting in secondary code). The letter code is used to describe supersecondary structure, which involves a unique characterization of the tum. It contains sufficient information to reconstruct the overall shape of a protein in an unambiguous 1D→3D translation step. Therefore, it is possible to classify supersecondary structure with the help of the letter code in form of a novel labeling system (F.#.M.X.O.L.N.R.U.S) that collects information on the relative orientations of tum and flanking structures (helices, strands). The overall shape of supersecondary structure is obtained by partitioning the surrounding space into octants and cones and assigning the parts of a supersecondary structure to these sub spaces via its labels. This approach can be easily extended to tertiary structure.
"],"dc:identifier":["https://scholarlycommons.pacific.edu/uop_etds/707"],"dc:rights":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:source":["254"],"dc:subject":["Proteins Structure Data processing","Proteins Structure Computer simulation","Bioinformatics","Chemistry","Physical Sciences and Mathematics"],"dc:title":["Automated and accurate description of protein structure -- from secondary to tertiary structure"],"thesis:degree_discipline":["Chemistry"],"thesis:degree_level":["Thesis - Pacific Access Restricted"],"thesis:degree_name":["Master of Science (M.S.)"]},"updated_at":"2026-07-24T05:36:46Z"}