ProVADA: Generation of Subcellular Protein Variants via Ensemble-Guided Test-Time Steering
Lu, W. S.; Zhang, X.; Mille-Fragoso, L. S.; Dai, H.; Gao, X. J.; Wong, W. H.
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Engineering protein variants to function in exogenous environments remains a significant challenge due to the complexity of sequence and fitness landscapes. Experimental strategies often require extensive labor and domain expertise. While recent advances in protein generative modeling offer a promising in silico alternative, many of these methods rely on differentiable fitness predictors, which limits their applicability. To this end, we introduce Protein Variant ADAptation (ProVADA), an ensemble-guided, test-time steering framework that combines implicit generative priors with fitness oracles via a composite functional objective. ProVADA leverages Mixture-Adaptation Directed Annealing (MADA), a novel sampler integrating population-annealing, adaptive mixture proposals, and directed local mutations. Furthermore, ProVADA requires no gradients or explicit likelihoods, yet efficiently concentrates sampling on high-fitness, low-divergence variants. We demonstrate its effectiveness by in silico redesigning human renin for cytosolic functionality. Our results achieve significant gains in predicted localization fitness while preserving structural integrity.
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