Weighting protein ensembles with Bayesian statistics and small-angle X-ray scattering data
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.
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| Other Authors: | |
| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2018
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| Online Access: | http://hdl.handle.net/1721.1/119574 |
| _version_ | 1826212451634380800 |
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| author | Schmidt, Molly A |
| author2 | Collin M. Stultz. |
| author_facet | Collin M. Stultz. Schmidt, Molly A |
| author_sort | Schmidt, Molly A |
| collection | MIT |
| description | Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018. |
| first_indexed | 2024-09-23T15:22:06Z |
| format | Thesis |
| id | mit-1721.1/119574 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T15:22:06Z |
| publishDate | 2018 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1195742019-04-10T11:54:15Z Weighting protein ensembles with Bayesian statistics and small-angle X-ray scattering data Schmidt, Molly A Collin M. Stultz. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018. This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (pages 52-54). Intrinsically Disordered Proteins (IDPs) are involved in a number of neurodegenerative disorders such as Parkinson's and Alzheimer's diseases. Their disordered nature allows them to sample many different conformations, so their structures must be represented as ensembles. Typically, structural ensembles for IDPs are constructed by generating a set of conformations that yield ensemble averages that agree with pre-existing experimental data. However, as the number of experimental constraints is usually much smaller than the degrees of freedom in the protein, the ensemble construction process is under-determined, meaning there are many different ensembles that agree with a given set of experimental observables. The Variational Bayesian Weighting program uses Bayesian statistics to fit conformational ensembles, and in doing so also quantifies the uncertainty in the underlying ensemble. The present work sought to introduce new functionality to this program, allowing it to use data obtained from Small-Angle X-ray Scattering. by Molly A. Schmidt. M. Eng. 2018-12-11T20:40:43Z 2018-12-11T20:40:43Z 2018 2018 Thesis http://hdl.handle.net/1721.1/119574 1076345247 eng MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 54 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Schmidt, Molly A Weighting protein ensembles with Bayesian statistics and small-angle X-ray scattering data |
| title | Weighting protein ensembles with Bayesian statistics and small-angle X-ray scattering data |
| title_full | Weighting protein ensembles with Bayesian statistics and small-angle X-ray scattering data |
| title_fullStr | Weighting protein ensembles with Bayesian statistics and small-angle X-ray scattering data |
| title_full_unstemmed | Weighting protein ensembles with Bayesian statistics and small-angle X-ray scattering data |
| title_short | Weighting protein ensembles with Bayesian statistics and small-angle X-ray scattering data |
| title_sort | weighting protein ensembles with bayesian statistics and small angle x ray scattering data |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/119574 |
| work_keys_str_mv | AT schmidtmollya weightingproteinensembleswithbayesianstatisticsandsmallanglexrayscatteringdata |