{"id":{"repo_id":"wichita-thes","oai_identifier":"oai:soar.wichita.edu:10057/24982"},"canonical_url":"https://search.dev.ndltd.org/etd/wichita-thes/oai:soar.wichita.edu:10057/24982","repository":{"repo_id":"wichita-thes","name":"Wichita State University","base_url":"https://soar.wichita.edu/oai/request"},"display":{"title":"Deep learning-based approaches for prediction of post-translational modification sites in proteins","abstract":"Protein post-translational modification plays an important role in a myriad of biological processes. Computational prediction approaches serve as complementary methods for the characterization of post-translational modification sites in proteins. Computational prediction of N-linked glycosylation sites confined to N-X-[S/T] sequons is an important problem. This dissertation reports on DeepNGlyPred, a deep neural network-based approach for N-linked glycosylation sites PTM prediction and it encodes the positive and negative sequences in the human proteome dataset using sequence-based features (gapped-dipeptide), predicted structural features, and evolutionary information. Similarly, this dissertation presents LMNglyPred, a deep learning-based approach to predict N-linked glycosylated sites in human proteins using embeddings from a pre-trained protein language model. To efficiently explore more undiscovered ubiquitylation sites, a novel multimodal deep learning architecture tool that identifies ubiquitination sites in proteins is studied. This study proposes a novel integrated deep learning-based approach named UbiIDN, for general ubiquitination site prediction, extracts and combines sequence and physicochemical properties information. Moreover, a novel integrated deep learning-based approach named LMPhosSite, for general phosphorylation site prediction is developed. LMPhosSite extracts and combines sequence and protein language model information. Using an independent test set of experimentally identified N-linked glycosylation, ubiquitination, and phosphorylation sites the respectively developed predictors were able to outperform state-of-the-art predictors. These results demonstrate that developed predictors are a robust computational technique to predict PTM sites in proteins.","abstract_html":"Protein post-translational modification plays an important role in a myriad of biological processes. Computational prediction approaches serve as complementary methods for the characterization of post-translational modification sites in proteins. Computational prediction of N-linked glycosylation sites confined to N-X-[S/T] sequons is an important problem. This dissertation reports on DeepNGlyPred, a deep neural network-based approach for N-linked glycosylation sites PTM prediction and it encodes the positive and negative sequences in the human proteome dataset using sequence-based features (gapped-dipeptide), predicted structural features, and evolutionary information. Similarly, this dissertation presents LMNglyPred, a deep learning-based approach to predict N-linked glycosylated sites in human proteins using embeddings from a pre-trained protein language model. To efficiently explore more undiscovered ubiquitylation sites, a novel multimodal deep learning architecture tool that identifies ubiquitination sites in proteins is studied. This study proposes a novel integrated deep learning-based approach named UbiIDN, for general ubiquitination site prediction, extracts and combines sequence and physicochemical properties information. Moreover, a novel integrated deep learning-based approach named LMPhosSite, for general phosphorylation site prediction is developed. LMPhosSite extracts and combines sequence and protein language model information. Using an independent test set of experimentally identified N-linked glycosylation, ubiquitination, and phosphorylation sites the respectively developed predictors were able to outperform state-of-the-art predictors. These results demonstrate that developed predictors are a robust computational technique to predict PTM sites in proteins.","abstract_has_math":false,"creators":["Pakhrin, Subash C."],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-12","date_published":"2022-12","updated_at":"2026-07-24T06:06:23Z","subjects":[],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["hdl:10057/24982"],"render_values":[{"text":"hdl:10057/24982","href":null,"code":true}]}]},"links":{"outbound_url":null,"outbound_label":null,"outbound_source":null},"metadata_groups":[{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.issued","label":"Date","values":["2022-12"]},{"key":"dc:type","label":"Dc Type","values":["Dissertation"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["hdl:10057/24982"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.other","label":"Dc Description Other","values":["Protein post-translational modification plays an important role in a myriad of biological processes. Computational prediction approaches serve as complementary methods for the characterization of post-translational modification sites in proteins. Computational prediction of N-linked glycosylation sites confined to N-X-[S/T] sequons is an important problem. This dissertation reports on DeepNGlyPred, a deep neural network-based approach for N-linked glycosylation sites PTM prediction and it encodes the positive and negative sequences in the human proteome dataset using sequence-based features (gapped-dipeptide), predicted structural features, and evolutionary information. Similarly, this dissertation presents LMNglyPred, a deep learning-based approach to predict N-linked glycosylated sites in human proteins using embeddings from a pre-trained protein language model. To efficiently explore more undiscovered ubiquitylation sites, a novel multimodal deep learning architecture tool that identifies ubiquitination sites in proteins is studied. This study proposes a novel integrated deep learning-based approach named UbiIDN, for general ubiquitination site prediction, extracts and combines sequence and physicochemical properties information. Moreover, a novel integrated deep learning-based approach named LMPhosSite, for general phosphorylation site prediction is developed. LMPhosSite extracts and combines sequence and protein language model information. Using an independent test set of experimentally identified N-linked glycosylation, ubiquitination, and phosphorylation sites the respectively developed predictors were able to outperform state-of-the-art predictors. These results demonstrate that developed predictors are a robust computational technique to predict PTM sites in proteins."]},{"key":"dc:title","label":"Title","values":["Deep learning-based approaches for prediction of post-translational modification sites in proteins"]}]}],"canonical_facts":{"dc:date.issued":["2022-12"],"dc:description.other":["Protein post-translational modification plays an important role in a myriad of biological processes. Computational prediction approaches serve as complementary methods for the characterization of post-translational modification sites in proteins. Computational prediction of N-linked glycosylation sites confined to N-X-[S/T] sequons is an important problem. This dissertation reports on DeepNGlyPred, a deep neural network-based approach for N-linked glycosylation sites PTM prediction and it encodes the positive and negative sequences in the human proteome dataset using sequence-based features (gapped-dipeptide), predicted structural features, and evolutionary information. Similarly, this dissertation presents LMNglyPred, a deep learning-based approach to predict N-linked glycosylated sites in human proteins using embeddings from a pre-trained protein language model. To efficiently explore more undiscovered ubiquitylation sites, a novel multimodal deep learning architecture tool that identifies ubiquitination sites in proteins is studied. This study proposes a novel integrated deep learning-based approach named UbiIDN, for general ubiquitination site prediction, extracts and combines sequence and physicochemical properties information. Moreover, a novel integrated deep learning-based approach named LMPhosSite, for general phosphorylation site prediction is developed. LMPhosSite extracts and combines sequence and protein language model information. Using an independent test set of experimentally identified N-linked glycosylation, ubiquitination, and phosphorylation sites the respectively developed predictors were able to outperform state-of-the-art predictors. These results demonstrate that developed predictors are a robust computational technique to predict PTM sites in proteins."],"dc:identifier":["hdl:10057/24982"],"dc:title":["Deep learning-based approaches for prediction of post-translational modification sites in proteins"],"dc:type":["Dissertation"]},"updated_at":"2026-07-24T06:06:23Z"}