EGRET: Exploring Generalist and Specialist Repertoires via Posterior Lineage Sampling over Epistatic Trees

Machine-learning-guided protein engineering usually optimizes one sequence at a time, although related conditions may be better served by descendants that share early mutations and specialize after branching. We introduce _assay-limited generalist-to-specialist diversification_: given a common scaffold, a finite sequence library, and limited sequence–condition measurements, a method jointly selects a condition niche and a rooted mutation tree under a shared construction budget. The objective measures how much accessible specialist headroom the tree captures beyond the best reachable generalist. This decision couples specialists by shared lineage cost and exposes condition-wise surrogate maximization to the optimizer's curse. EGRET uses a hierarchical epistatic posterior and posterior lineage sampling for assay allocation. It commits a final tree using measured leaf–condition utilities, providing a verifiable lower bound on its value. Diverse experiments evaluate repertoire quality, explanation faithfulness, and decision traceability across protein-engineering scenarios with varied objectives and assay budgets.