Genomic Perception Fusion: A Lightweight, Interpretable Kernel for Protein Functional Tuning

Protein design has been transformed by deep generative models--but at the cost of interpretability, accessibility, and integration with sparse experimental feedback. Here, we introduce Genomic Perception Fusion (GPF), a biologically inspired algorithm that treats DNA not as inert code, but as a linear signal awaiting perceptual reconstruction. GPF transforms nucleotide sequences--augmented with non-coding regulatory context--into a high-order functional representation that predicts stability, solubility, and expression. Built from physicochemical first principles and literature-derived parameters, GPF runs on a laptop in under a second, yet accurately forecasts the effects of surface mutations in green fluorescent protein (GFP). Validated against computational benchmarks, GPF offers a frugal, transparent alternative to black-box design for rapid protein engineering.