Refined Coordinate Structures and Pocket Analyses for Oncology Targets

Dataset Title: Refined Coordinate Structures and Pocket Analyses for Oncology Targets Overview: Focuses on critical somatic drivers and tumor suppressors (e.g., KRAS, TP53, APC, and AR). These refined structures help identify novel pockets and allosteric binding sites, allowing researchers to design small-molecule inhibitors and folding stabilizers to deactivate hyperactive signaling cascades or rescue misfolded tumor-suppressor functions. Computational Methodology: Target structures are optimized using the NRC CASP-17 Pure Math Folding Engine. This pipeline applies C-alpha harmonic guide potential constraints ($k_{guide} = 0.5$) for comparative modeling (or $k_{guide} = 0.0$ for ab-initio modeling), followed by sidechain relaxation and pocket expansion simulations to identify allosteric and active cavities. All coordinates are audited using the Trageser Tensor Theorem (TTT-7) lattice-parity verification to eliminate steric clashes (minimum inter-atomic distance > 1.10 Å) and loop hallucinations. License: Distributed under Creative Commons Attribution 4.0 International (CC BY 4.0) to enable open, commercial, and academic therapeutic drug design. Principal Investigator: James Paul Trageser Affiliation: Nexus Resonance Codex ORCID: 0009-0006-6678-2908 X (Twitter): @jtrag Repository: https://GitHub.com/Nexus-Resonance-Codex/Drug-Discovery