Deterministic Design of a Heptapeptide (Vorkul-INS-1) for Insulin Receptor Signaling Restoration: An Information-Theoretic Approach in GF(2) Fields

We present the deterministic computational design of Vorkul-INS-1 (LPEVAGS), a novel heptapeptide aimed at restoring signal integrity in the mutated Insulin Receptor (INSR). Shifting from traditional biochemical screening to an information-theoretic paradigm, we modeled the INSR tyrosine kinase domain as a binary sequence in a Galois Field GF(2). By applying a multi-resolution signal decomposition, we identified critical informational entropy (noise) correlating with insulin resistance. In silico application of the Vorkul-INS-1 binary mask via logical XOR operations yielded a 72.73% targeted reduction in pathological signal entropy on a clinical genomic model, while preserving natural polymorphisms (SNPs). Structural validation via AlphaFold 3 confirmed the peptide's absolute physical stability (pLDDT > 90, PAE ≈ 0). Released under the principles of White Ethics, these findings establish Prior Art to prevent patent monopolization and invite immediate in vitro validation.