On the efficient analysis and sampling of mutant free energy landscapes
Abstract/Contents
- Abstract
- Molecular dynamics (MD) simulations are a computational technique capable of providing detailed atomic level understanding of molecular processes. They do this by numerically solving the microscopic interactions that govern these processes. Akin to a "computational microscope", MD simulations are used to predict and understand these protein free-energy landscapes. Given simulations from related mutant proteins, MD simulations could even predict the atomic level effects of mutations. These mutations might be oncogenic thereby increasing cell proliferation or they might abrogate an inhibitors' binding affinity or they might be completely benign. A structural and quantitative model for these mutations would be invaluable in both understanding the system and potentially designing personal therapeutics. However, MD simulations are very computationally expensive to converge and it is not immediately obvious how to compare multiple mutant simulations to one another in a statistically significant and efficient manner. In this thesis, I describe methods for analyzing and sampling mutant free energy landscapes. The first few chapters are dedicated towards building Markov state models (MSMs) for multiple protein kinases using milliseconds of aggregate simulation data. Currently, these are some of the largest kinase simulation datasets ever recorded. The latter chapters present several methodological advances on how to more efficiently predict mutational effects using a combination of enhanced sampling simulations and existing MSMs. This thesis attempts to merge the fields of Machine learning, enhanced sampling, and Markov state modeling for the efficient analysis and sampling of protein mutation landscapes.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2018; ©2018 |
| Publication date | 2018; 2018 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Sultan, Mohammad Muneeb |
|---|---|
| Degree supervisor | Martinez, Todd J. (Todd Joseph), 1968- |
| Degree supervisor | Pande, Vijay |
| Thesis advisor | Martinez, Todd J. (Todd Joseph), 1968- |
| Thesis advisor | Pande, Vijay |
| Thesis advisor | Frank, C. W |
| Degree committee member | Frank, C. W |
| Associated with | Stanford University, Department of Chemistry. |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Mohammad Muneeb Sultan. |
|---|---|
| Note | Submitted to the Department of Chemistry. |
| Thesis | Thesis Ph.D. Stanford University 2018. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2018 by Mohammad Muneeb Sultan
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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