Harnessing Computational Approaches to Engineer Protein Flexibility for Immunological Applications

Graduate Thesis Or Dissertation

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Citeable URL: https://scholar.colorado.edu/concern/graduate_thesis_or_dissertations/9k41zg46c

Abstract

Molecular Dynamics (MD) simulations have been extensively employed to investigate protein dynamics. In this context, MD simulations aim to understand intra- and inter-molecular forces between proteins, or between proteins and solvents, enabling measurements of key properties such as the root-mean-square deviation (RMSD) and fluctuation (RMSF) of the proteins to assess temporal changes in their conformations and dynamics, respectively. With specific regards to protein optimization, MD simulations have been utilized to understand how changes in a protein’s sequence or solvent environment can be leveraged to improve protein affinity and/or stability. While protein flexibility can be viewed as detrimental to stability, the impacts of changes in protein flexibility are, in actuality, highly nuanced and situation-dependent. To this effect, in antigen/antibody (Ab) interactions, where Ab rigidity can strongly dictate the ability of antigen mutations to enable viral escape, flexibility plays a vital role. Conversely, encapsulating flexible proteins in nanoparticle drug delivery systems can reduce drug potency. Thus, understanding, modulating, and engineering protein flexibility is crucial to developing more robust therapeutics.

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