Target-specific drug design necessitates the manipulation of multifaceted networks of biomolecules. At the level of proteins, such networks are composed of amino acids, and can be affected by biological processes such as allostery, point mutations and ligand binding. Binding an effector to an allosteric site, for instance, may limit the active site's binding affinity, thus inhibiting the protein's function. The complexity of amino acid interactions makes difficult the process of predicting protein behavior and network dynamics in the face of such intricate biological processes. In this study, we show that a combination of molecular dynamics (MD) simulations, network analysis, and spectral decomposition of pairwise interaction matrices progresses the ability to pick out targets residues with characteristics beneficial to the design of therapeutics. We observed in silico that the p53 tumor suppressor protein contains a dense network of cohesive residues which may have the ability to transduce longrange allosteric signals. By engineering point mutations to residues within this cohesive network, we found that disturbing the network's connectivity impacted the overall function of p53. The network was outputted via an updated implementation MD sectors, which analyzes and spectrally decomposes correlated motions between residues within an MD trajectory. The application of MD sectors to p53 could aid in the of design drugs which interact directly with sector residues. MD sectors has the potential to successfully pick out allosteric drug targets in a wide variety of biological systems in an accurate and costeffective manner. VI Contents ACKNOWLEDGEMENTS .