用主动学习融合深度学习与物理模型，高效筛选高亲和力抗体突变体。
Active learning for energy-based antibody optimization and enhanced screening
- 结合RDE-Network与Rosetta能量函数，通过主动学习迭代优化预测模型。
- 在HER2抗体突变体筛选中性能显著优于随机选择，无需实验数据即发现更优突变体。
- 适合计算抗体设计、药物研发领域，尤其适用于无现有结合物的目标。
准确预测和优化蛋白质-蛋白质结合亲和力对治疗性抗体开发至关重要。尽管基于机器学习的ΔΔG预测方法适用于大规模突变体筛选，但在缺乏现有结合物的目标上难以预测多重突变的影响。基于能量函数的方法虽更准确，但耗时且不适合大规模筛选。为此，我们提出一种主动学习工作流，高效训练深度学习模型以学习特定靶标的能量函数，融合两类方法优势。该方法将RDE-Network深度学习模型与Rosetta的能量函数驱动的Flex ddG相结合，高效探索突变体。以针对HER2结合的曲妥珠单抗突变体为例，本方法显著优于随机筛选，并能在无实验ΔΔG数据的情况下识别出结合性能更优的突变体。该工作流通过融合机器学习、物理计算与主动学习，推动了计算抗体设计的发展。
原文摘要 · Abstract (English)
Accurate prediction and optimization of protein-protein binding affinity is crucial for therapeutic antibody development. Although machine learning-based prediction methods $ΔΔG$ are suitable for large-scale mutant screening, they struggle to predict the effects of multiple mutations for targets without existing binders. Energy function-based methods, though more accurate, are time consuming and not ideal for large-scale screening. To address this, we propose an active learning workflow that efficiently trains a deep learning model to learn energy functions for specific targets, combining the advantages of both approaches. Our method integrates the RDE-Network deep learning model with Rosetta's energy function-based Flex ddG to efficiently explore mutants. In a case study targeting HER2-binding Trastuzumab mutants, our approach significantly improved the screening performance over random selection and demonstrated the ability to identify mutants with better binding properties without experimental $ΔΔG$ data. This workflow advances computational antibody design by combining machine learning, physics-based computations, and active learning to achieve more efficient antibody development.
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