From Static to Dynamic Structures: Improving Binding Affinity Prediction with Graph-Based Deep Learning
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , |
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| Format: | Preprint |
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2022
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| _version_ | 1866917764173660160 |
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| author | Min, Yaosen Wei, Ye Wang, Peizhuo Wang, Xiaoting Li, Han Wu, Nian Bauer, Stefan Zheng, Shuxin Shi, Yu Wang, Yingheng Wu, Ji Zhao, Dan Zeng, Jianyang |
| author_facet | Min, Yaosen Wei, Ye Wang, Peizhuo Wang, Xiaoting Li, Han Wu, Nian Bauer, Stefan Zheng, Shuxin Shi, Yu Wang, Yingheng Wu, Ji Zhao, Dan Zeng, Jianyang |
| contents | Accurate prediction of protein-ligand binding affinities is an essential challenge in structure-based drug design. Despite recent advances in data-driven methods for affinity prediction, their accuracy is still limited, partially because they only take advantage of static crystal structures while the actual binding affinities are generally determined by the thermodynamic ensembles between proteins and ligands. One effective way to approximate such a thermodynamic ensemble is to use molecular dynamics (MD) simulation. Here, an MD dataset containing 3,218 different protein-ligand complexes is curated, and Dynaformer, a graph-based deep learning model is further developed to predict the binding affinities by learning the geometric characteristics of the protein-ligand interactions from the MD trajectories. In silico experiments demonstrated that the model exhibits state-of-the-art scoring and ranking power on the CASF-2016 benchmark dataset, outperforming the methods hitherto reported. Moreover, in a virtual screening on heat shock protein 90 (HSP90) using Dynaformer, 20 candidates are identified and their binding affinities are further experimentally validated. Dynaformer displayed promising results in virtual drug screening, revealing 12 hit compounds (two are in the submicromolar range), including several novel scaffolds. Overall, these results demonstrated that the approach offer a promising avenue for accelerating the early drug discovery process. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2208_10230 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | From Static to Dynamic Structures: Improving Binding Affinity Prediction with Graph-Based Deep Learning Min, Yaosen Wei, Ye Wang, Peizhuo Wang, Xiaoting Li, Han Wu, Nian Bauer, Stefan Zheng, Shuxin Shi, Yu Wang, Yingheng Wu, Ji Zhao, Dan Zeng, Jianyang Biomolecules Machine Learning Chemical Physics Quantitative Methods Accurate prediction of protein-ligand binding affinities is an essential challenge in structure-based drug design. Despite recent advances in data-driven methods for affinity prediction, their accuracy is still limited, partially because they only take advantage of static crystal structures while the actual binding affinities are generally determined by the thermodynamic ensembles between proteins and ligands. One effective way to approximate such a thermodynamic ensemble is to use molecular dynamics (MD) simulation. Here, an MD dataset containing 3,218 different protein-ligand complexes is curated, and Dynaformer, a graph-based deep learning model is further developed to predict the binding affinities by learning the geometric characteristics of the protein-ligand interactions from the MD trajectories. In silico experiments demonstrated that the model exhibits state-of-the-art scoring and ranking power on the CASF-2016 benchmark dataset, outperforming the methods hitherto reported. Moreover, in a virtual screening on heat shock protein 90 (HSP90) using Dynaformer, 20 candidates are identified and their binding affinities are further experimentally validated. Dynaformer displayed promising results in virtual drug screening, revealing 12 hit compounds (two are in the submicromolar range), including several novel scaffolds. Overall, these results demonstrated that the approach offer a promising avenue for accelerating the early drug discovery process. |
| title | From Static to Dynamic Structures: Improving Binding Affinity Prediction with Graph-Based Deep Learning |
| topic | Biomolecules Machine Learning Chemical Physics Quantitative Methods |
| url | https://arxiv.org/abs/2208.10230 |