PIGRAM: An Interpretable Patch–Motif Interaction Grammar for Protein–Nucleic-Acid Recognition

Protein–nucleic-acid recognition underlies aptamer discovery and nucleic-acid therapeutics, yet computational models often trade mechanistic interpretability for predictive flexibility. Hybrid interpretable–latent architectures risk fallback takeover, where the latent branch silently becomes the true predictor while the explicit pathway remains decorative. We introduce PIGRAM, a patch–motif grammar model that decomposes proteins into local residue patches and nucleic-acid partners into secondary-structure motifs, learning a sign-separated grammar over explicit physicochemical and geometric attributes to preserve rare favorable interactions. Heterogeneous rule families are selectively refined into a coarse-to-fine atlas, and a frozen-grammar residual Transformer provides sparse, bounded corrections without displacing the grammar as the primary predictor. On a strict sequence-pair-disjoint benchmark, PIGRAM achieves a Pearson correlation of 0.494, approaching the unconstrained Transformer (0.511) while exposing signed rules, fine subtypes, and route-gated corrections for each prediction. The learned rules recover bidirectional patch–motif mechanisms, and residual corrections concentrate selectively on grammar-hard regions. In fluorescence ELISA experiments, suppressive-rule relief improved GFP binding, whereas favorable-rule disruption weakened NELF binding, providing direct experimental support for grammar-guided design. Together, these results show that interpretable grammar can serve as both a predictive scaffold and an experimentally actionable design principle for aptamer optimization.