Machine Learning Methods for Protein Model Quality Estimation

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dc.contributor.authorShuvo, Md Hossainen
dc.contributor.committeechairBhattacharya, Debswapnaen
dc.contributor.committeememberZhang, Liqingen
dc.contributor.committeememberHeath, Lenwood S.en
dc.contributor.committeememberOnufriev, Alexeyen
dc.contributor.committeememberEmrich, Scotten
dc.contributor.departmentComputer Science and Applicationsen
dc.date.accessioned2023-12-22T09:02:24Zen
dc.date.available2023-12-22T09:02:24Zen
dc.date.issued2023-12-21en
dc.description.abstractgeneralIn my research, I developed protein model quality estimation methods aimed at evaluating the reliability of computationally predicted protein models in the absence of experimentally solved ground truth structures. These methods specifically focus on estimating errors within the protein models to quantify their structural accuracy. Recognizing that even the most advanced protein structure prediction techniques may produce models with errors, I also developed a complementary protein model refinement method. This refinement method iteratively optimizes the weakly modeled regions, guided by the error estimation module of my quality estimation approach. The development of these model quality estimation methods, therefore, not only offers valuable insights into the structural reliability of protein models but also contributes to optimizing the overall reliability of protein models generated by state-of-the-art computational methods.en
dc.description.degreeDoctor of Philosophyen
dc.format.mediumETDen
dc.identifier.othervt_gsexam:39314en
dc.identifier.urihttps://hdl.handle.net/10919/117278en
dc.language.isoenen
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectKeywords: Protein scoringen
dc.subjectAccuracy estimationen
dc.subjectDeep learningen
dc.subjectProtein structure predictionen
dc.titleMachine Learning Methods for Protein Model Quality Estimationen
dc.typeDissertationen
thesis.degree.disciplineComputer Science and Applicationsen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.leveldoctoralen
thesis.degree.nameDoctor of Philosophyen

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