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Backbone Flexibility in Computational Protein Design

Abstract

Over the past two decades the field of computational protein design has produced striking successes, both by improving our understanding of the fundamental principles governing protein structure, dynamics and function, and by engineering new and modified proteins with useful properties for scientific inquiry and industrial applications. Generally, these successes have arisen from design strategies that sample amino side-chains on a polypeptide backbone with fixed atomic coordinates. Despite the well-known tendency for protein backbones to adjust in the face of sequence mutations, the fixed backbone assumption is typically maintained due to the challenge of efficiently sampling backbone and side-chain conformations, and accurately evaluating their physical favorability. This dissertation addresses these issues of sampling and evaluating protein conformations and applies the developed methods to predict proteins with new functions. The dissertation first provides a quantitative assessment of hydrogen bonding involving amino acid phosphorylation, a key post-translational modification that can alter protein function by inducing conformational rearrangements. The dissertation then introduces a robotics-inspired method for modeling backbone flexibility in proteins, and demonstrates sub-angstrom accuracy in an application to predict the conformations of regions lacking secondary structure in protein monomers and interfaces. Finally, the dissertation describes the coupling of the developed flexible backbone method with computational sequence design to predict proteins that dimerize only in the presence of small molecule targets to act as in vitro or in vivo biosensors. Fusing split reporter fragments to the chemically induced dimer partners provides a modular approach that can, in principle, be used to detect any small molecule that has been crystallized in complex with a protein and drive a variety of enzymatic, fluorescent, and transcriptional outputs.

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