Deep learning-based approaches for prediction of post-translational modification sites in proteins

dc.contributor.advisorBagai, Rajiv
dc.contributor.authorPakhrin, Subash Chandra
dc.date.accessioned2023-01-26T15:13:08Z
dc.date.available2023-01-26T15:13:08Z
dc.date.issued2022-12
dc.descriptionThesis (Ph.D.)-- Wichita State University, College of Engineering, School of Computing
dc.description.abstractProtein 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.format.extentxviii, 101 pages
dc.identifier.otherd22035s
dc.identifier.urihttps://soar.wichita.edu/handle/10057/24982
dc.language.isoen_US
dc.publisherWichita State University
dc.rights© Copyright 2022 by Subash Chandra Pakhrin All Rights Reserved
dc.subject.lcshElectronic dissertation
dc.titleDeep learning-based approaches for prediction of post-translational modification sites in proteins
dc.typeDissertation
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