Benchmarking reverse docking through AlphaFold2 human proteome
Predicting the binding of ligands to the human proteome via reverse-docking methods enables the understanding of ligand's interactions with potential protein targets in the human body, thereby facilitating drug repositioning and the evaluation of potential off-target effects or toxic side effec...
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sg-ntu-dr.10356-1807942024-10-28T01:09:48Z Benchmarking reverse docking through AlphaFold2 human proteome Luo, Qing Wang, Sheng Li, Hoi Yeung Zheng, Liangzhen Mu, Yuguang Guo, Jingjing School of Biological Sciences Medicine, Health and Life Sciences Human proteome Reverse docking Predicting the binding of ligands to the human proteome via reverse-docking methods enables the understanding of ligand's interactions with potential protein targets in the human body, thereby facilitating drug repositioning and the evaluation of potential off-target effects or toxic side effects of drugs. In this study, we constructed 11 reverse docking pipelines by integrating site prediction tools (PointSite and SiteMap), docking programs (Glide and AutoDock Vina), and scoring functions (Glide, Autodock Vina, RTMScore, DeepRMSD, and OnionNet-SFCT), and then thoroughly benchmarked their predictive capabilities. The results show that the Glide_SFCT (PS) pipeline exhibited the best target prediction performance based on the atomic structure models in AlphaFold2 human proteome. It achieved a success rate of 27.8% when considering the top 100 ranked prediction. This pipeline effectively narrows the range of potential targets within the human proteome, laying a foundation for drug target prediction, off-target assessment, and toxicity prediction, ultimately boosting drug development. By facilitating these critical aspects of drug discovery and development, our work has the potential to ultimately accelerate the identification of new therapeutic agents and improve drug safety. Ministry of Education (MOE) L. Z. acknowledges the National Key R&D Program of China (2023YFA0915500), J. G. acknowledges the internal grant from Macao Polytechnic University (RP/CAI-01/2022, RP/CAI-01/2023), and Y. M. acknowledges the Ministry of Education Singapore (MOE), Tier 1 grant RG97/22. 2024-10-28T01:09:48Z 2024-10-28T01:09:48Z 2024 Journal Article Luo, Q., Wang, S., Li, H. Y., Zheng, L., Mu, Y. & Guo, J. (2024). Benchmarking reverse docking through AlphaFold2 human proteome. Protein Science, 33(10), e5167-. https://dx.doi.org/10.1002/pro.5167 0961-8368 https://hdl.handle.net/10356/180794 10.1002/pro.5167 39276010 2-s2.0-85203983076 10 33 e5167 en RG97/22 Protein Science © 2024 The Protein Society. All rights reserved. |
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Medicine, Health and Life Sciences Human proteome Reverse docking Luo, Qing Wang, Sheng Li, Hoi Yeung Zheng, Liangzhen Mu, Yuguang Guo, Jingjing Benchmarking reverse docking through AlphaFold2 human proteome |
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Predicting the binding of ligands to the human proteome via reverse-docking methods enables the understanding of ligand's interactions with potential protein targets in the human body, thereby facilitating drug repositioning and the evaluation of potential off-target effects or toxic side effects of drugs. In this study, we constructed 11 reverse docking pipelines by integrating site prediction tools (PointSite and SiteMap), docking programs (Glide and AutoDock Vina), and scoring functions (Glide, Autodock Vina, RTMScore, DeepRMSD, and OnionNet-SFCT), and then thoroughly benchmarked their predictive capabilities. The results show that the Glide_SFCT (PS) pipeline exhibited the best target prediction performance based on the atomic structure models in AlphaFold2 human proteome. It achieved a success rate of 27.8% when considering the top 100 ranked prediction. This pipeline effectively narrows the range of potential targets within the human proteome, laying a foundation for drug target prediction, off-target assessment, and toxicity prediction, ultimately boosting drug development. By facilitating these critical aspects of drug discovery and development, our work has the potential to ultimately accelerate the identification of new therapeutic agents and improve drug safety. |
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School of Biological Sciences |
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School of Biological Sciences Luo, Qing Wang, Sheng Li, Hoi Yeung Zheng, Liangzhen Mu, Yuguang Guo, Jingjing |
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Article |
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Luo, Qing Wang, Sheng Li, Hoi Yeung Zheng, Liangzhen Mu, Yuguang Guo, Jingjing |
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Luo, Qing |
title |
Benchmarking reverse docking through AlphaFold2 human proteome |
title_short |
Benchmarking reverse docking through AlphaFold2 human proteome |
title_full |
Benchmarking reverse docking through AlphaFold2 human proteome |
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Benchmarking reverse docking through AlphaFold2 human proteome |
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Benchmarking reverse docking through AlphaFold2 human proteome |
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benchmarking reverse docking through alphafold2 human proteome |
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2024 |
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https://hdl.handle.net/10356/180794 |
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