Data-driven Construction of Coarse-grained Protein Models

Simple item page
dc.contributor.advisorClementi, Ceciliaen_US
dc.contributor.advisorKolomeisky, Anatolyen_US
dc.creatorYang, Wangfeien_US
dc.date.accessioned2024-01-24T21:37:08Zen_US
dc.date.available2024-01-24T21:37:08Zen_US
dc.date.created2023-08en_US
dc.date.issued2023-09-26en_US
dc.date.submittedAugust 2023en_US
dc.date.updated2024-01-24T21:37:08Zen_US
dc.description.abstractWith the rapid development of computational power over the past few decades, computational simulation has grown increasingly important in protein-related research due to its high resolution and predictability. However, simulating large proteins over extended time scales using all-atomic resolution models and explicit solvents remains infeasible due to the substantial computational burden. Consequently, researchers have turned to developing coarse-grained (CG) models, which reduce degrees of freedom to accelerate simulations. To develop a CG model, two tasks must be completed. One involves determining which degrees of freedom will be retained in the CG model, known as the CG mapping. The other task is accurately capturing equivalent interactions at the CG resolution using an appropriate force field. In this dissertation, two data-driven methods are developed for the aforementioned tasks. The first method, Variational Approach for Markov Processes (VAMP)-based coarse-graining, provides a criterion to evaluate different CG mappings under a specific resolution directly from finer-grained simulation data. This evaluation is accomplished without constructing a complete CG model and running CG simulations. Additionally, based on this method, a Markov Chain Monte Carlo sampling method is developed to attain the optimal CG mapping without enumerating all possibilities. The second method optimizes the CG force field according to the native structure based on the energy landscape theory. Specifically, this method is used to extend an existing coarse-grained model, the Associative memory, Water-mediated, Structure, and Energy Model (AWSEM), to include protein-metal ion interactions. The use of these methods enables the construction of accurate coarse-grained protein models, making it possible to simulate larger protein systems over longer time scales.en_US
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationYang, Wangfei. "Data-driven Construction of Coarse-grained Protein Models." (2023). PhD diss., Rice University. https://hdl.handle.net/1911/115388en_US
dc.identifier.urihttps://hdl.handle.net/1911/115388en_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.subjectProteinen_US
dc.subjectCoarse-grained Modelen_US
dc.subjectMetal Ionen_US
dc.titleData-driven Construction of Coarse-grained Protein Modelsen_US
dc.typeThesisen_US
dc.type.materialTexten_US
thesis.degree.departmentSystems, Synthetic and Physical Biologyen_US
thesis.degree.disciplineSystems/Synthetic/Phys Biologyen_US
thesis.degree.grantorRice Universityen_US
thesis.degree.levelDoctoralen_US
thesis.degree.nameDoctor of Philosophyen_US
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
YANG-DOCUMENT-2023.pdf
Size:
2.79 MB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 2 of 2
No Thumbnail Available
Name:
PROQUEST_LICENSE.txt
Size:
5.84 KB
Format:
Plain Text
Description:
Download
No Thumbnail Available
Name:
LICENSE.txt
Size:
2.98 KB
Format:
Plain Text
Description:
Download
Collections
Rice University Theses and Dissertations