PLM-CARE: Biosafety Assessment and Quality-Matched Preference Alignment for Protein Language Models

Protein language models (PLMs) enable increasingly controllable protein design, yet it remains unclear whether different PLM architectures produce outputs with predicted virulence-factor (VF) association, and whether such signals can be reduced without degrading prespecified sequence-quality proxies. We introduce PLM-CARE, a cross-architecture framework that generates candidates from ESM3 and PoET-2 through their native masked and causal paths, converts VirulentHunter-scored candidates into quality-matched preferences, and fine-tunes each model with a shared reference-relative preference objective instantiated through architecture-specific scoring. Under matched native regeneration on held-out, risk-enriched contexts, preference fine-tuning lowered the mean DTVF probability (R_DTVF) from 0.67 to 0.52 for ESM3 and from 0.88 to 0.74 for PoET-2; a second external classifier, PSSM-based VirulentPred 2.0, showed mean paired prompt-level reductions in predicted-positive fraction (R_VP2) of 17.5 and 18.9 percentage points, respectively, on its supported-input subsets. Both external classifier endpoints decreased across three training runs per architecture. Prespecified quality proxies showed no observed degradation for either architecture. Together, these results indicate that computationally predicted VF-associated signals in outputs from the evaluated masked and causal PLMs under these contexts can be reduced by architecture-adapted fine-tuning. The code is available at https://anonymous.4open.science/r/plm-care-review-B2DF/.