Active learning for energy-based antibody optimization and enhanced screening

• URL: http://arxiv.org/abs/2409.10964v2
• Date: Wed, 18 Sep 2024 07:37:31 GMT
• Title: Active learning for energy-based antibody optimization and enhanced screening
• Authors: Kairi Furui, Masahito Ohue,
• Abstract summary: We propose an active learning workflow that efficiently trains a deep learning model to learn energy functions for specific targets. In a case study targeting HER2-binding Trastuzumab mutants, our approach significantly improved the screening performance over random selection.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate prediction and optimization of protein-protein binding affinity is crucial for therapeutic antibody development. Although machine learning-based prediction methods $\Delta\Delta G$ 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 $\Delta\Delta G$ 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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