Empowering AlphaFold2 for protein conformation selective drug discovery with AlphaFold2-RAVE
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866917713309335552 |
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| author | Gu, Xinyu Aranganathan, Akashnathan Tiwary, Pratyush |
| author_facet | Gu, Xinyu Aranganathan, Akashnathan Tiwary, Pratyush |
| contents | Small molecule drug design hinges on obtaining co-crystallized ligand-protein structures. Despite AlphaFold2's strides in protein native structure prediction, its focus on apo structures overlooks ligands and associated holo structures. Moreover, designing selective drugs often benefits from the targeting of diverse metastable conformations. Therefore, direct application of AlphaFold2 models in virtual screening and drug discovery remains tentative. Here, we demonstrate an AlphaFold2 based framework combined with all-atom enhanced sampling molecular dynamics and induced fit docking, named AF2RAVE-Glide, to conduct computational model based small molecule binding of metastable protein kinase conformations, initiated from protein sequences. We demonstrate the AF2RAVE-Glide workflow on three different protein kinases and their type I and II inhibitors, with special emphasis on binding of known type II kinase inhibitors which target the metastable classical DFG-out state. These states are not easy to sample from AlphaFold2. Here we demonstrate how with AF2RAVE these metastable conformations can be sampled for different kinases with high enough accuracy to enable subsequent docking of known type II kinase inhibitors with more than 50% success rates across docking calculations. We believe the protocol should be deployable for other kinases and more proteins generally. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_07102 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Empowering AlphaFold2 for protein conformation selective drug discovery with AlphaFold2-RAVE Gu, Xinyu Aranganathan, Akashnathan Tiwary, Pratyush Biological Physics Disordered Systems and Neural Networks Soft Condensed Matter Computational Physics Small molecule drug design hinges on obtaining co-crystallized ligand-protein structures. Despite AlphaFold2's strides in protein native structure prediction, its focus on apo structures overlooks ligands and associated holo structures. Moreover, designing selective drugs often benefits from the targeting of diverse metastable conformations. Therefore, direct application of AlphaFold2 models in virtual screening and drug discovery remains tentative. Here, we demonstrate an AlphaFold2 based framework combined with all-atom enhanced sampling molecular dynamics and induced fit docking, named AF2RAVE-Glide, to conduct computational model based small molecule binding of metastable protein kinase conformations, initiated from protein sequences. We demonstrate the AF2RAVE-Glide workflow on three different protein kinases and their type I and II inhibitors, with special emphasis on binding of known type II kinase inhibitors which target the metastable classical DFG-out state. These states are not easy to sample from AlphaFold2. Here we demonstrate how with AF2RAVE these metastable conformations can be sampled for different kinases with high enough accuracy to enable subsequent docking of known type II kinase inhibitors with more than 50% success rates across docking calculations. We believe the protocol should be deployable for other kinases and more proteins generally. |
| title | Empowering AlphaFold2 for protein conformation selective drug discovery with AlphaFold2-RAVE |
| topic | Biological Physics Disordered Systems and Neural Networks Soft Condensed Matter Computational Physics |
| url | https://arxiv.org/abs/2404.07102 |