{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/129159"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/129159","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Data-driven peptide drug discovery and design","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms","abstract_has_math":false,"creators":["Mi, Xuenan"],"institution":"University of Illinois Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Biophysics & Quant Biology","degree_department":null,"school":null,"contributors":["Shukla, Diwakar","Mitchell, Douglas Alan","Pogorelov, Taras","Jackson, Nicholas"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-01-13","date_published":"2025-01-13","updated_at":"2026-07-22T22:25:04Z","subjects":["Ribosomally synthesized and post-translationally modified peptides","Deep Learning","Molecular Dynamics"],"languages":["en","eng"],"rights":["Copyright 2025 Xuenan Mi"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/129159","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Shukla, Diwakar","Mitchell, Douglas Alan","Pogorelov, Taras","Jackson, Nicholas"]},{"key":"dc:creator","label":"Author","values":["Mi, Xuenan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-01-13","2025-05"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Biophysics & Quant 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 Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Ribosomally synthesized and post-translationally modified peptides","Deep Learning","Molecular Dynamics"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2025 Xuenan Mi"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/129159"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms","The student, Xuenan Mi, accepted the attached license on 2024-12-19 at 19:38.","The student, Xuenan Mi, submitted this Dissertation for approval on 2024-12-19 at 19:49.","This Dissertation was approved for publication on 2025-01-13 at 13:49.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21603 on 2025-10-19 at 18:08:35","Ribosomally synthesized and post-translationally modified peptides (RiPPs) represent a diverse class of natural products that have obtained significant attention in recent years due to their broad spectrum of biological activities. These peptides undergo extensive post-translational modifications, which impart unique structures not achievable through natural ribosomal peptides. Such modifications often restrict conformational flexibility, enhancing target recognition, metabolic and chemical stability, and functional diversity. RiPPs are particularly attractive for engineering applications, as their precursor peptides are genetically encoded, and many biosynthetic enzymes exhibit substrate tolerance. Despite their potential, the discovery and design of novel RiPPs for therapeutic applications remain challenging due to a limited understanding of the substrate tolerance mechanisms in RiPP synthetases and the folding processes involved in their biosynthesis. In this dissertation, I have employed computational approaches, including deep learning and molecular dynamics (MD) simulations, to advance peptide drug discovery and design. My research primarily focuses on two natural peptide families: lasso peptides and lanthipeptides. For lasso peptides, I developed a specialized language model, LassoESM, leveraging advancements in pre-trained protein language models to enhance lasso peptide-related properties prediction. Furthermore, I conducted extensive atomic-level MD simulations to investigate the universal folding mechanisms of 20 structurally characterized lasso peptides. Using a deep learning architecture based on Variational Autoencoder, I successfully identified distinct folding pathways for these peptides. For lanthipeptides, I utilized MD simulations to explore the site-selectivity of lanthipeptide cyclization, uncovering critical insights into the molecular mechanisms underlying their biosynthesis. Additionally, I addressed the challenges posed by limited data in peptide discovery and protein-peptide interaction prediction by implementing a deep learning framework based on one-shot learning architecture. Overall, I anticipate that LassoESM and its future iterations will serve as valuable tools for the rational design of lasso peptides with tailored properties. Insights into the folding mechanisms of lasso peptides and the site-selectivity of lanthipeptide cyclization will deepen our understanding of their biosynthetic processes. Moreover, one-shot learning provides a promising framework for accurately predicting peptide-protein interactions, particularly in low-data scenarios."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Data-driven peptide drug discovery and design"]}]}],"canonical_facts":{"dc:contributor":["Shukla, Diwakar","Mitchell, Douglas Alan","Pogorelov, Taras","Jackson, Nicholas"],"dc:creator":["Mi, Xuenan"],"dc:date":["2025-01-13","2025-05"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms","The student, Xuenan Mi, accepted the attached license on 2024-12-19 at 19:38.","The student, Xuenan Mi, submitted this Dissertation for approval on 2024-12-19 at 19:49.","This Dissertation was approved for publication on 2025-01-13 at 13:49.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21603 on 2025-10-19 at 18:08:35","Ribosomally synthesized and post-translationally modified peptides (RiPPs) represent a diverse class of natural products that have obtained significant attention in recent years due to their broad spectrum of biological activities. These peptides undergo extensive post-translational modifications, which impart unique structures not achievable through natural ribosomal peptides. Such modifications often restrict conformational flexibility, enhancing target recognition, metabolic and chemical stability, and functional diversity. RiPPs are particularly attractive for engineering applications, as their precursor peptides are genetically encoded, and many biosynthetic enzymes exhibit substrate tolerance. Despite their potential, the discovery and design of novel RiPPs for therapeutic applications remain challenging due to a limited understanding of the substrate tolerance mechanisms in RiPP synthetases and the folding processes involved in their biosynthesis. In this dissertation, I have employed computational approaches, including deep learning and molecular dynamics (MD) simulations, to advance peptide drug discovery and design. My research primarily focuses on two natural peptide families: lasso peptides and lanthipeptides. For lasso peptides, I developed a specialized language model, LassoESM, leveraging advancements in pre-trained protein language models to enhance lasso peptide-related properties prediction. Furthermore, I conducted extensive atomic-level MD simulations to investigate the universal folding mechanisms of 20 structurally characterized lasso peptides. Using a deep learning architecture based on Variational Autoencoder, I successfully identified distinct folding pathways for these peptides. For lanthipeptides, I utilized MD simulations to explore the site-selectivity of lanthipeptide cyclization, uncovering critical insights into the molecular mechanisms underlying their biosynthesis. Additionally, I addressed the challenges posed by limited data in peptide discovery and protein-peptide interaction prediction by implementing a deep learning framework based on one-shot learning architecture. Overall, I anticipate that LassoESM and its future iterations will serve as valuable tools for the rational design of lasso peptides with tailored properties. Insights into the folding mechanisms of lasso peptides and the site-selectivity of lanthipeptide cyclization will deepen our understanding of their biosynthetic processes. Moreover, one-shot learning provides a promising framework for accurately predicting peptide-protein interactions, particularly in low-data scenarios."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/129159"],"dc:language":["en","eng"],"dc:rights":["Copyright 2025 Xuenan Mi"],"dc:subject":["Ribosomally synthesized and post-translationally modified peptides","Deep Learning","Molecular Dynamics"],"dc:title":["Data-driven peptide drug discovery and design"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Biophysics & Quant Biology"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:04Z"}