Active learning for energy-based antibody optimization and enhanced screening
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866916398942388224 |
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| author | Furui, Kairi Ohue, Masahito |
| author_facet | Furui, Kairi Ohue, Masahito |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_10964 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Active learning for energy-based antibody optimization and enhanced screening Furui, Kairi Ohue, Masahito Biomolecules Artificial Intelligence Machine Learning Quantitative Methods 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. |
| title | Active learning for energy-based antibody optimization and enhanced screening |
| topic | Biomolecules Artificial Intelligence Machine Learning Quantitative Methods |
| url | https://arxiv.org/abs/2409.10964 |