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Network theory analysis has emerged as a powerful approach for investigating the complex behavior of dynamic and interactive systems, including proteomic systems. One key application of these methods is the study of long-range signaling dynamics in proteins, a phenomenon known as allostery. In this study, we applied computational models using network theory analysis to explore long-range electrostatic interactions and allosteric drug rescue mechanisms in the DNA-binding domain (DBD) of the p53 protein, a critical tumor suppressor whose dysfunction, often caused by missense mutations, is implicated in over 50% of human cancers. Using heat kernel and Wasserstein distance-based analyses, we explored the allosteric behavior of p53-DBD constructs with the Y220C mutation in the presence or absence of allosteric effector drugs. Our results demonstrated that these network theory-based protocols effectively detected the differential efficacies of small molecule allosteric effector drug compounds in restoring long-range electrostatic dynamics in the Y220C mutant. Furthermore, our approach identified key long-range electrostatic interactions critical to both the nominal and drug-rescued functionality of the p53-DBD, providing valuable insights into allosteric modulation and its therapeutic potential. 

The link between p53 tumor suppressive functions and organismal lifespan is multifaceted. Its DNA-repair mechanism is longevity-enhancing while its role in cellular senescence pathways induces pro-aging phenotypes. To understand how p53 may regulate organismal lifespan, cross-species genotype-phenotype (GP) studies of the p53 DNA-binding domain (DBD) have been used to assess the correlation of amino acid changes to lifespan. Amino acid changes in non-DNA-binding regions such as the transactivation (TAD), proline-rich (PRD), regulatory (REG), and tetramerization (TET) are largely unexplored. In addition, existing GP correlation tools such as SigniSite do not account for phylogenetic relationships between aligned sequences in correlating genotypic differences to phenotypes such as lifespan. To identify phylogenetically significant, longevity-correlated residues in full-length p53 alignments, we developed a Python- and R-based workflow, Relative Evolutionary Scoring (RES). While RES-predicted longevity-associated residues (RPLARs) are concentrated primarily in the DBD, the PRD, TET, and REG domains also house RPLARs. While yeast functional assay enrichment reveals that RPLARs may be dispensable for p53-mediated transactivation, PEPPI and Rosetta-based protein-protein interaction prediction suggests a role for RPLARs in p53 stability and interaction interfaces of tumor suppressive protein-protein complexes. With experimental validation of the RPLARs’ roles in p53 stability, transactivation, and involvement in senescence-regulatory pathways, we can gain crucial insights into mechanisms underlying dysregulated tumor suppression and accelerated aging.