CBBL

DeepPro-v2 is a multi-view, multi-backbone deep learning model for prokaryotic promoter prediction across 23 species. It aggregates five complementary genomic foundation model experts through a learnable anchor–prototype fusion module with a soft-MoE aggregator, together with a differentiable motif-grammar side branch. Training follows a three-stage pipeline: task-adaptive pretraining of each backbone on the pooled 23-species corpus, followed by per-species two-stage 5-fold training, and finally ensemble voting. Findings: DeepPro-v2 attains a mean test MCC of approximately 0.81 across the 23 species, exceeding strong genomic foundation model baselines (GROVER, HyenaDNA, DNABERT2-CAMP, msBERT) retrained under a faithful, paper-matched reproduction protocol. Beyond headline accuracy, we subject the model to an end-to-end analysis and interpretability campaign spanning statistical significance testing (Friedman–Nemenyi and pairwise Wilcoxon), probabilistic calibration (ROC/PR and reliability curves), per-view contribution analysis, representation-space separability (UMAP), motif discovery, error stratification, and a full 23×23 cross-species transfer matrix. This campaign also yields honest negative results: the soft-MoE gate does not aggressively prune experts—itself evidence that the five views are non-redundant—and an anchor-prototype probe revealed no latent class structure, which we report as a negative finding. Most strikingly, the standalone differentiable motif-grammar branch spontaneously recovered the sigma-70 −10 box (TATAAT; best per-expert correlation r = 0.67) from binary promoter/non-promoter labels alone, without any motif-level supervision. Why it matters: Accurate and interpretable promoter identification underpins genome annotation and synthetic biology circuit design. By pairing fair, paper-matched benchmarking with a comprehensive, reproducible analysis and interpretability suite—rather than reporting a single accuracy number—we provide a rigorous reference for this task and make transparent both where the model succeeds and where it does not. All of the work presented here was built during a one-week hackathon: "v2" denotes an in-week iteration of our own model rather than an extension of any pre-existing project.