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Clementi, Cecilia Onuchic, José Chen, Justin 2019-05-17T18:31:53Z 2019-11-01T05:01:18Z 2019-05 2019-03-20 May 2019 2019-05-17T18:31:54Z Chen, Justin. "Data Driven Modeling of Proteins." (2019) Diss., Rice University. <a href="https://hdl.handle.net/1911/105931">https://hdl.handle.net/1911/105931</a>. https://hdl.handle.net/1911/105931 Proteins are tiny molecular machines that perform the vast majority of the functions in living cells. In order for the protein to perform its function, it has to be able to fold from a disordered coil into a specific compact structure. Two new computational methods are developed that take advantage of the large amount of data generated in both experiments and computer simulations in order to better understand how proteins work. The first method (pyODEM) improves the modeling of proteins on the global scale, while a second method (pyFrustration) probes the protein's local frustration that might impede the folding process. Use of these methods allows us to construct more dynamically accurate protein models and improves our understanding of how a protein folds and performs its function. application/pdf eng Copyright 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. protein physics frustration protein folding molecular dynamics protein design non-linear optimization Data Driven Modeling of Proteins Thesis Text 2019-11-01 Physics and Astronomy Natural Sciences Rice University Doctoral Doctor of Philosophy open.access