{"id":{"repo_id":"washington","oai_identifier":"oai:digital.lib.washington.edu:1773/53023"},"canonical_url":"https://search.dev.ndltd.org/etd/washington/oai:digital.lib.washington.edu:1773/53023","repository":{"repo_id":"washington","name":"University of Washington","base_url":"https://digital.lib.washington.edu/server/oai/request"},"display":{"title":"Computational design of functional cyclic peptides using deep learning","abstract":"Cyclic peptides have gained significant traction as a therapeutic modality. Given their ease of synthesis, expansive chemical space, and the promising pharmacokinetic properties of existing cyclic peptide drugs, cyclic peptides have been proposed as a mid-point between biologics and small molecules. Computational design of structured cyclic peptides has been successful using Rosetta, even design of membrane traversing cyclic peptides, but efforts to design binders to protein targets have led to only a handful of successful cases. Deep learning (DL) networks have recently shown considerable opportunities for accurate structure prediction and design of biomolecules that are potent inhibitors of therapeutically relevant protein interfaces. This work describes the application of a cyclic offset to the AlphaFold2 network as well RFdiffusion, resulting in accurate prediction and design of structured de novo cyclic peptides and high affinity cyclic peptide binders against protein targets of interest of diverse shape and function.","abstract_html":"Cyclic peptides have gained significant traction as a therapeutic modality. Given their ease of synthesis, expansive chemical space, and the promising pharmacokinetic properties of existing cyclic peptide drugs, cyclic peptides have been proposed as a mid-point between biologics and small molecules. Computational design of structured cyclic peptides has been successful using Rosetta, even design of membrane traversing cyclic peptides, but efforts to design binders to protein targets have led to only a handful of successful cases. Deep learning (DL) networks have recently shown considerable opportunities for accurate structure prediction and design of biomolecules that are potent inhibitors of therapeutically relevant protein interfaces. This work describes the application of a cyclic offset to the AlphaFold2 network as well RFdiffusion, resulting in accurate prediction and design of structured de novo cyclic peptides and high affinity cyclic peptide binders against protein targets of interest of diverse shape and function.","abstract_has_math":false,"creators":["Rettie, Stephen Allan"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Bhardwaj, Gaurav"],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-05-12","date_published":"2025-05-12","updated_at":"2026-07-24T05:58:07Z","subjects":["Computational protein design","Peptides","Biochemistry","Computational chemistry"],"languages":["en_US"],"rights":["CC BY-NC"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/1773/53023","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Bhardwaj, Gaurav"]},{"key":"dc:creator","label":"Author","values":["Rettie, Stephen Allan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-05-12T22:50:35Z"]},{"key":"dc:date.issued","label":"Date","values":["2025-05-12"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Computational protein design","Peptides","Biochemistry","Computational chemistry"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en_US"]},{"key":"dc:rights","label":"Dc Rights","values":["CC BY-NC"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["Rettie_washington_0250E_27888.pdf"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/1773/53023"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Thesis (Ph.D.)--University of Washington, 2025"]},{"key":"dc:description.abstract","label":"Abstract","values":["Cyclic peptides have gained significant traction as a therapeutic modality. Given their ease of synthesis, expansive chemical space, and the promising pharmacokinetic properties of existing cyclic peptide drugs, cyclic peptides have been proposed as a mid-point between biologics and small molecules. Computational design of structured cyclic peptides has been successful using Rosetta, even design of membrane traversing cyclic peptides, but efforts to design binders to protein targets have led to only a handful of successful cases. Deep learning (DL) networks have recently shown considerable opportunities for accurate structure prediction and design of biomolecules that are potent inhibitors of therapeutically relevant protein interfaces. This work describes the application of a cyclic offset to the AlphaFold2 network as well RFdiffusion, resulting in accurate prediction and design of structured de novo cyclic peptides and high affinity cyclic peptide binders against protein targets of interest of diverse shape and function."]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Computational design of functional cyclic peptides using deep learning"]}]}],"canonical_facts":{"dc:contributor.advisor":["Bhardwaj, Gaurav"],"dc:creator":["Rettie, Stephen Allan"],"dc:date.accessioned":["2025-05-12T22:50:35Z"],"dc:date.issued":["2025-05-12"],"dc:description":["Thesis (Ph.D.)--University of Washington, 2025"],"dc:description.abstract":["Cyclic peptides have gained significant traction as a therapeutic modality. Given their ease of synthesis, expansive chemical space, and the promising pharmacokinetic properties of existing cyclic peptide drugs, cyclic peptides have been proposed as a mid-point between biologics and small molecules. Computational design of structured cyclic peptides has been successful using Rosetta, even design of membrane traversing cyclic peptides, but efforts to design binders to protein targets have led to only a handful of successful cases. Deep learning (DL) networks have recently shown considerable opportunities for accurate structure prediction and design of biomolecules that are potent inhibitors of therapeutically relevant protein interfaces. This work describes the application of a cyclic offset to the AlphaFold2 network as well RFdiffusion, resulting in accurate prediction and design of structured de novo cyclic peptides and high affinity cyclic peptide binders against protein targets of interest of diverse shape and function."],"dc:format.mimetype":["application/pdf"],"dc:identifier.other":["Rettie_washington_0250E_27888.pdf"],"dc:identifier.uri":["https://hdl.handle.net/1773/53023"],"dc:language.iso":["en_US"],"dc:rights":["CC BY-NC"],"dc:subject":["Computational protein design","Peptides","Biochemistry","Computational chemistry"],"dc:title":["Computational design of functional cyclic peptides using deep learning"],"dc:type":["Thesis"]},"updated_at":"2026-07-24T05:58:07Z"}