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.