This thesis describes improvements to our protein loop structure prediction algorithm and use of this algorithm to inform a computational investigation of anti-HIV antibodies. First, in Section I, we outline improvements to the Protein Local Optimization Program ("Plop") that allow us to reliably restore long loops containing secondary structure elements, and predict nativelike conformations for loops whose surroundings deviate from the native crystal structure context. Shifting to focus exclusively on antibody hypervariable loop prediction, we also benchmark our results in the community-wide Second Antibody Modeling Assessment. Plop can now be reliably deployed as a tool for understanding important biological systems. In Section II, we start from a system of interest - broadly neutralizing antibodies against HIV-1 - with the long-term goal of computationally identifying more potent VRC01-class anti-HIV antibodies. We show proof of concept results for predicting relative binding affinity upon mutation using free energy perturbation (FEP) simulations for the VRC01 antibody binding to glycoprotein gp120. Using the protocols developed in Section I, we provide case studies for using Plop to understand key FEP results and guide future experiments.
| Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/D8HQ3XZS |
| Date | January 2015 |
| Creators | Murrett, Colleen |
| Source Sets | Columbia University |
| Language | English |
| Detected Language | English |
| Type | Theses |
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