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    Computational Protein Design with Ensembles, Flexibility and Mathematical Guarantees, and its Application to Drug Resistance Prediction, and Antibody Design

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    Date
    2015-01-01
    Author
    Gainza Cirauqui, Pablo
    Advisor
    Donald, Bruce R.
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    Abstract

    Proteins are involved in all of life's processes and are also responsible for many diseases. Thus, engineering proteins to perform new tasks could revolutionize many areas of biomedical research. One promising technique for protein engineering is computational structure-based protein design (CSPD). CSPD algorithms search large protein conformational spaces to approximate biophysical quantities. In this dissertation we present new algorithms to realistically and accurately model how amino acid mutations change protein structure. These algorithms model continuous flexibility, protein ensembles and positive/negative design, while providing guarantees on the output. Using these algorithms and the OSPREY protein design program we design and apply protocols for three biomedically-relevant problems: (i) prediction of new drug resistance mutations in bacteria to a new preclinical antibiotic, (ii) the redesign of llama antibodies to potentially reduce their immunogenicity for use in preclinical monkey studies, and (iii) scaffold-based anti-HIV antibody design. Experimental validation performed by our collaborators confirmed the importance of the algorithms and protocols.

    Type
    Dissertation
    Department
    Computer Science
    Subject
    Computer science
    Biophysics
    Biochemistry
    A* algorithm
    antibody design
    ensemble-based design
    protein design
    resistance prediction
    simianization
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    https://hdl.handle.net/10161/10468
    Citation
    Gainza Cirauqui, Pablo (2015). Computational Protein Design with Ensembles, Flexibility and Mathematical Guarantees, and its Application to Drug Resistance Prediction, and Antibody Design. Dissertation, Duke University. Retrieved from https://hdl.handle.net/10161/10468.
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    Creative Commons License
    This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 United States License.

    Rights for Collection: Duke Dissertations

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