Proteins play a crucial role in all activities of living organisms and viruses. To perform their functions, they require specific structural characteristics that are optimal for functioning in their particular cellular environment. This restriction creates a condition that evolutionarily favors proteins with specific ‘folds’ or a combination of folds that offer significant stability, as well as geometries favoring specific functional mechanisms. The arrangement of amino acids with respect to one another in 3D space is protein packing. The protein packing regularities are reflected by low diversity in sequence space and even convergent evolution in the shapes of the proteins. This regularity presents the opportunity to incorporate features of packing into a variety of computations that can help to understand protein function. Various local protein features can be investigated, such as backbone torsion angles and sidechain conformations and how these relate to packing. Globular proteins are typically packed as densely as a sphere can pack. We see in this dissertation how protein packing can be investigated to yield a new understanding of function. We highlight and demonstrate how this property can be used to solve crucial challenges in structural bioinformatics. We use protein packing to predict the location of ‘hinges’ (hinge motion is the most common type of motion) that are the flexible hotspots on the protein structure. We further used the hinge information in a novel elastic network model that incorporates the protein packing into its core mathematics and also calculated perturbation responses using the concept of structural compliance that is often used in civil and mechanical engineering, and we also see the utility of packing for calculating the protein entropy, using only a single conformation of the protein without utilizing atomic Molecular Dynamics, therefore, making it tremendously faster and free of the butterfly effect and statistical problems associated with Molecular Dynamics. We also use Elastic Network Models to include the protein-solvent interactions in simple dynamics models and to identify the structural domains in the proteins. The software package ‘PACKMAN’ that we developed (https://github.com/Pranavkhade/PACKMAN) contains all the code to reproduce the research presented in this dissertation and the molecular toolbox to modify and customize the different features to carry out further research with ease. The PACKMAN has more tens of thousands of downloads on The Python Package Index (PyPI)
