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Covalent docking in CDOCKER

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Abstract

Targeted covalent inhibitors (TCIs) are considered to be an important component in the toolbox of drug discovery and about 30% of currently marketed drugs are TCIs. Although these drugs raise concerns about toxicity, their high potencies and prolonged effects result in less-frequent drug dosing and wide therapeutic margins for patients. This leads to increased interests in developing new computational methods to identify novel covalent inhibitors. The implementation of successful in silico docking algorithms have the potential to provide significant savings of time and money in the discovery of lead compounds. In this paper, we describe the implementation and testing of a covalent docking methodology in Rigid CDOCKER and the optimization of the corresponding physics-based scoring function with an additional customizable covalent bond grid potential which represents the free energy change of bond formation between the ligand and the receptor. We optimize the covalent bond grid potential for different common covalent bond formation reaction in TCIs. The average runtime for docking one covalent compound is 15 minutes which is comparable or faster than other well-established covalent docking methods. We demonstrate comparable top rank accuracy compared with other covalent docking algorithms using the pose prediction benchmark dataset for covalent docking algorithms developed by the Keserű group. Finally, we construct a retrospective virtual screening benchmark dataset containing 8 different receptor targets with different covalent bond formation reactions. To our knowledge, this is the largest dataset for benchmarking covalent docking methods. We show that our new covalent docking algorithm has the ability to identify lead compounds among a large chemical space. The largest AUC value is 0.909 for the target receptor CATK and the warhead chemistry of the covalent inhibitors is addition to the aldehyde functionality.

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Data availability

The Supporting Information (Virtual screening dataset construction; Ligand preparation workflow; Covalent docking searching algorithm; Pose prediction results.) is available free of charge via the Internet. CHARMM license is free for academic users. The full source code and license information for CHARMM are available at http://charmm.chemistry.harvard.edu/ The benchmark dataset and code examples are provided on Github https://github.com/wyujin/Covalent-Docking-in-CDOCKER and are listed below\(\bullet\)Example of covalent docking in CDOCKER with CHARMM scripting language\(\bullet\)Example of covalent docking in pyCHARMM CDOCKER;\(\bullet\)SMILES strings of the retrospective virtual screening datasets used in this study;\(\bullet\)Pose prediction result (rank-result.tsv);\(\bullet\)Scripts for general ligand preparation.

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Acknowledgements

This work is supported by Grants from the NIH(GM130587, GM037554, and GM107233).

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Authors and Affiliations

  1. Department of Chemistry, University of Michigan, Ann Arbor, 500 S State St, Ann Arbor, Michigan, 48109, USA

    Yujin Wu & Charles L. Brooks III

  2. Biophysics Program, University of Michigan, Ann Arbor, 500 S State St, Ann Arbor, Michigan, 48109, USA

    Charles L. Brooks III

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  1. Yujin Wu
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Correspondence to Charles L. Brooks III.

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Wu, Y., Brooks III, C.L. Covalent docking in CDOCKER. J Comput Aided Mol Des 36, 563–574 (2022). https://doi.org/10.1007/s10822-022-00472-3

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