SafePro: Generation of Safe and Functional Proteins via Cross-Task Conflict-Aware Alignment

Protein language models (PLMs) enable the \textit{de novo} design of functional proteins at scale, yet they lack intrinsic safety constraints, raising dual-use concerns. We introduce SafePro, a reinforcement-learning framework that aligns PLMs against three simultaneous objectives: structural foldability, toxicity avoidance, and virulence avoidance. SafePro features two novel components: (1) Cross-Task Attentive Fusion (CTAF), a joint reward model that employs gated cross-attention between toxicity and virulence experts to capture shared biological signals without inducing negative transfer, and (2)Task-Conflict-Saliency MGDA (TCS-MGDA), an adaptive weight-calibration procedure for Proximal Policy Optimization (PPO). TCS-MGDA resolves gradient Gram-matrix dilution via parameter-space saliency filtering. On ProGen2, SafePro improves composite alignment scores by $+5.21$ over the strongest fixed-weight baseline, significantly reducing toxicity ($0.0523 \to 0.0139$) and virulence ($0.6926 \to 0.5407$) while maintaining structural quality. Among structurally designable outputs (scRMSD $< 2$\,\AA), SafePro reduces the harmful fraction from $89\%$ to $34\%$ while producing more than twice as many designable sequences as the base model. We also release a curated virulence dataset of $54{,}166$ sequences from VFDB.