反馈校准的蛋白质优化与批量对齐尾部仲裁

中文摘要 AI 辅助

蛋白质优化旨在有限的实验预算下发现高适应度序列。现有的机器学习方法使用任务特定的预测器、生物学先验或排序感知目标来指导下一轮实验中测试哪些变体。然而，这些方法无法适应随着测量积累而变化的预测证据可靠性，也无法确保关键高适应度候选体的正确排序。为了解决这些挑战，我们提出了批量对齐尾部仲裁（BATA），它利用实验反馈自适应地结合先验信息和任务特定的排序来选择下一批次，校准聚焦于批量对齐的高适应度区域。在测量的GB1、PABP和TrpB景观上，BATA在480次测量后的最终最佳适应度上取得了最佳平均任务排名（1.67）。受控比较进一步显示了高适应度校准和批量对齐带来的任务相关增益。我们的工作引入了反馈校准的预测器仲裁，其中实验反馈动态决定预测证据如何指导下一批次的选择，为蛋白质优化开辟了新方向。

英文摘要

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.

发表机构

• University of Science and Technology of China（中国科学技术大学）
• Nanjing University（南京大学）
• Anhui University（安徽大学）

机构由 AI 辅助整理，请以论文原文为准。