Protein Folding as Identity Stabilization under Kearon Conditional Unlocking Fields

This repository presents a necessity-based framework for protein folding formulated under Kearon Conditional Unlocking Fields (CUF). Rather than treating folding as an energy minimization or large-scale simulation problem, the work reframes protein folding as a process of identity stabilization under unavoidable physical and biological constraints. Proteins are modeled as identity-bearing systems whose correct fold emerges when re-identifiability is preserved under perturbation and recursive updates. Folding is defined as complete at the earliest point where identity ceases to change, enforced through hard physical constraints and sequentially activated soft biological constraints. The paper introduces a minimal CUF folding rule, an identity instability functional, and a closure criterion that replaces global optimization with identity-lock stabilization. The framework is intended as a pre-folding and convergence-guiding method that collapses the conformational search space before numerical refinement, rather than as a replacement for existing AI or physics-based structure prediction tools. This work is suitable for researchers in protein science, computational biology, and drug discovery seeking principled methods to reduce false-positive structures, improve structural decision accuracy, and accelerate end-to-end discovery pipelines through necessity-driven filtering.