Evaluation of Proliferating Cell Nuclear Antigen as a Therapeutic Target in DNA Repair
Proliferating cell nuclear antigen (PCNA) has emerged as an essential DNA scaffold protein that recruits and stabilizes interacting partners to form protein complexes. These complexes regulate a wide variety of cellular processes associated with eliciting a DNA damage response. A detailed understanding of the regulatory mechanisms that are associated with the formation of these protein complexes is necessary to understand how a cell protects itself from genetic instability. It is hypothesized that the features that modulate PCNA-protein complexes will provide insight for targeting strategies of these complexes, and subsequent inhibition of specific cellular processes associated with a DNA damage response. Trying to evaluate PCNA as a therapeutic target in DNA repair is a challenge. The list of reported PCNA binding partners and their association with a variety of cellular processes tied to DNA repair is over 140 and rapidly increasing. Unfortunately, the extent to which PCNA participates in the dysregulation of a specific biological response is not well known. To address this challenge, a novel bioinformatics approach that integrates publically available protein-protein interaction databases with genomic databases was applied to identify potential PCNA-protein complexes that may drive ovarian serous cystadenocarcinoma and glioblastoma multiforme tumors. Results from this bioinformatics study identified disease-specific upstream regulatory mechanisms that may drive dysregulation of PCNA-protein complexes and the associated DNA damage response. Targeting those proteins in combination with PCNA may provide a new synergistic treatment for those patients with refractory and resistant tumors. The second goal in evaluating PCNA as a therapeutic target is the ability to effectively identify the detailed intermolecular interactions that may promote selectivity of specific PCNA-protein complexes and regulate distinct cellular processes. A series of biochemical analyses were coupled with computational modeling to understand the molecular features that may impact complex formation. These features were validated using peptide mimics of two previously unreported PCNA binding partners. Feasibility of using these features to drive a fragment-based approach to the design and development of PCNA-protein interaction modulators was also explored. An in silico screen of a compound library representing 7 different structural types was performed and a hit compound, JK-171, was shown to compete with POGO for binding to PCNA at an estimated Kd,app of 292 &mgr;M. Despite the relatively weak binding, the molecule holds promise as a novel modulator that can be optimized to leverage the molecular features identified through computational analyses. Additional fragments can be easily appended to enhance overall affinity and drive rational drug development of small molecule modulators of PCNA-protein complexes.
Davisson, Purdue University.
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