Algorithms for Scalable Structural Analysis of Class I Peptide-MHC Systems

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dc.contributor.advisorKavraki, Lydia Een_US
dc.contributor.committeeMemberNakhleh, Luay Ken_US
dc.creatorAbella, Jayvee Ralphen_US
dc.date.accessioned2020-04-27T19:03:42Zen_US
dc.date.available2020-04-27T19:03:42Zen_US
dc.date.created2020-05en_US
dc.date.issued2020-04-22en_US
dc.date.submittedMay 2020en_US
dc.date.updated2020-04-27T19:03:42Zen_US
dc.description.abstractPeptide-MHC (pMHC) complexes are central components of the immune system, and understanding the mechanism behind stable pMHC binding will aid the development of immunotherapies. Stable pMHC binding can be assessed through an analysis of structure, which contain information on the atomic interactions present between peptide and MHC. However, a large-scale analysis of pMHCs is difficult to perform, due to the lack of available structures as well as fact that pMHCs are large molecular systems with slow timescales. This thesis presents a set of approaches developed to deliver scalable structural analysis of Class I pMHC systems. First, we present APE-Gen, a fast method for generating ensembles of bound pMHC conformations. Next, we present a structure-based classifier using random forests for predicting stable pMHC binding. Finally, we present a simulation framework for generating a Markov state model of the full binding dynamics for a given pMHC system using a combination of umbrella and adaptive sampling. This work pushes the capability of computational methods for the structural analysis of pMHCs, leading to structural insight that can guide the understanding of pMHC binding.en_US
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationAbella, Jayvee Ralph. "Algorithms for Scalable Structural Analysis of Class I Peptide-MHC Systems." (2020) Diss., Rice University. <a href="https://hdl.handle.net/1911/108385">https://hdl.handle.net/1911/108385</a>.en_US
dc.identifier.urihttps://hdl.handle.net/1911/108385en_US
dc.language.isoengen_US
dc.rightsCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.en_US
dc.subjectpeptide-MHC bindingen_US
dc.subjectdockingen_US
dc.subjectmachine learningen_US
dc.subjectmolecular dynamicsen_US
dc.subjectmarkov state modelen_US
dc.subjectsimulationen_US
dc.subjectrandom forests predictionen_US
dc.titleAlgorithms for Scalable Structural Analysis of Class I Peptide-MHC Systemsen_US
dc.typeThesisen_US
dc.type.materialTexten_US
thesis.degree.departmentComputer Scienceen_US
thesis.degree.disciplineEngineeringen_US
thesis.degree.grantorRice Universityen_US
thesis.degree.levelDoctoralen_US
thesis.degree.nameDoctor of Philosophyen_US
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