Refined Coordinate Structures and Pocket Analyses for Immunology and Autoimmune Disease Targets

Dataset Title: Refined Coordinate Structures and Pocket Analyses for Immunology and Autoimmune Disease Targets Overview: Focuses on inflammatory cytokines, kinase regulators, and immunological checkpoints (e.g., TNF, IL6, JAK1, MMP9, FOXP3, HLA-DRB1). Models target the design of high-selectivity small-molecule inhibitors to modulate auto-immune responses while minimizing off-target immunotoxicity. 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