Data Driven Modeling of Proteins

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.

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

Doctoral

Discipline thesis:degree_discipline

Natural Sciences

Grantor

Rice University

Year dc:date.issued

2019

Author dc:creator

• Chen, Justin

Advisor dc:contributor.advisor

• Clementi, Cecilia

Committee member dc:contributor.committeemember

• Onuchic, José

• protein
• physics
• frustration
• protein folding
• molecular dynamics
• protein design
• non-linear optimization

Statement dc:rights

• 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.

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1911/105931

OAI identifier oai:identifier

oai:repository.rice.edu:1911/105931