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Machine Learning Methods for Protein Model Quality Estimation

Doctor of Philosophy / In 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.

Identiferoai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/117278
Date21 December 2023
CreatorsShuvo, Md Hossain
ContributorsComputer Science and Applications, Bhattacharya, Debswapna, Zhang, Liqing, Heath, Lenwood S., Onufriev, Alexey, Emrich, Scott
PublisherVirginia Tech
Source SetsVirginia Tech Theses and Dissertation
LanguageEnglish
Detected LanguageEnglish
TypeDissertation
FormatETD, application/pdf
RightsIn Copyright, http://rightsstatements.org/vocab/InC/1.0/

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