Pre-dynamical Ordering in Protein Folding via Fixed-Kernel Convolution

This work explores protein folding from a signal-processing perspective rather than a dynamical simulation framework. We show that global folding constraints — including hydrophobic core formation and long-range tertiary contacts — can be extracted directly from an amino-acid sequence through convolution with a fixed kernel representing an environmental response. The approach is pre-dynamical: it does not simulate atomic trajectories or forces, but instead identifies permitted global modes that bias folding outcomes. The method is sequence-specific, non-local, and computationally lightweight, and is intended as a rapid constraint-extraction or screening tool rather than a replacement for molecular dynamics or full structure prediction pipelines. Reference implementation (Python): https://github.com/kyrondamon/nomadic-folder