Problem
Research questions and friction points this paper is trying to address.
Protein optimization
Experimental budget
Predictive reliability
High-fitness ranking
Machine learning
Feedback-Calibrated Protein Optimization with Batch-Aligned Tail Arbitration
🤖 AI Summary
This study addresses the limitations of existing methods in adapting to varying prediction reliability and their susceptibility to ranking errors among critical candidates. To this end, we propose BATA (Batch-Aligned Tail Arbitration), a framework that introduces a feedback-calibrated predictor arbitration mechanism. BATA dynamically integrates prior knowledge with task-specific rankings through adaptive rank fusion, while leveraging high-fitness region calibration to precisely guide the selection of subsequent batches. Experimental evaluations on fitness landscapes such as GB1 demonstrate that BATA achieves the best average task ranking of 1.67. These results effectively validate the superiority of the proposed calibration and alignment strategies for batch optimization tasks.
📝 Abstract
Protein optimization aims to discover high-fitness sequences under a limited experimental budget. Existing machine-learning methods use task-specific predictors, biological priors, or ranking-aware objectives to guide which variants are tested in the next experimental round. However, these methods cannot adapt to shifts in the reliability of predictive evidence as measurements accumulate and ensure the correct ranking of key high-fitness candidates. To address these challenges, we propose Batch-Aligned Tail Arbitration (BATA), which uses experimental feedback to adaptively combine prior-informed and task-specific rankings for next-batch selection, with calibration focused on the batch-aligned high-fitness region. Across measured GB1, PABP, and TrpB landscapes, BATA achieves the best mean task rank (1.67) in final best fitness after 480 measurements. Controlled comparisons further show task-dependent gains from high-fitness calibration and batch alignment. Our work introduces feedback-calibrated predictor arbitration, where experimental feedback dynamically determines how predictive evidence guides next-batch selection, opening a new direction for protein optimization.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Protein Optimization
Batch-Aligned Tail Arbitration
Feedback-Calibrated Predictor Arbitration
Active Learning
High-Fitness Calibration
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