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KinFragLib: Exploring the Kinase Inhibitor Space Using Subpocket-Focused Fragmentation and Recombination

Cite this: J. Chem. Inf. Model. 2020, 60, 12, 6081–6094
Publication Date (Web):November 6, 2020
https://doi.org/10.1021/acs.jcim.0c00839
Copyright © 2020 American Chemical Society
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    Abstract

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    Protein kinases play a crucial role in many cell signaling processes, making them one of the most important families of drug targets. In this context, fragment-based drug design strategies have been successfully applied to develop novel kinase inhibitors. These strategies usually follow a knowledge-driven approach to optimize a focused set of fragments to a potent kinase inhibitor. Alternatively, KinFragLib explores and extends the chemical space of kinase inhibitors using data-driven fragmentation and recombination. The method builds on available structural kinome data from the KLIFS database for over 2500 kinase DFG-in structures cocrystallized with noncovalent kinase ligands. The computational fragmentation method splits the ligands into fragments with respect to their 3D proximity to six predefined functionally relevant subpocket centers. The resulting fragment library consists of six subpocket pools with over 7000 fragments, available at https://github.com/volkamerlab/KinFragLib. KinFragLib offers two main applications: on the one hand, in-depth analyses of the chemical space of known kinase inhibitors, subpocket characteristics, and connections, and on the other hand, subpocket-informed recombination of fragments to generate potential novel inhibitors. The latter showed that recombining only a subset of 624 representative fragments generated 6.7 million molecules. This combinatorial library contains, besides some known kinase inhibitors, more than 99% novel chemical matter compared to ChEMBL and 63% molecules compliant with Lipinski’s rule of five.

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    The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jcim.0c00839.

    • Data and Methods: KLIFS data (Details S1); Structures with covalent ligands that were excluded from the KinFragLib dataset (Details S2); Exceptions for anchor residue definitions (Details S3); Fragment information storage (Details S4); Fragment data reduction before recombination (Details S5). Fragment library: Number of fragments, deduplicated fragments, singletons per subpocket pool and average pairwise Tanimoto similarity between fragments in each subpocket (Table S1); Ligands occupying 1 subpocket (Details S6); Ligands/fragments which show special subpocket occupancies (Figure S1); Disallowed subpocket connections/special cases (Details S7); Disallowed subpocket connections/special cases (Table S2); 50 most common fragments in the AP, FP, SE, GA, B1, and B2 subpockets (Figure S2-S7); Comparison of AP fragments reported in this study to hinge fragments from literature (Table S3). Combinatorial library analysis: ChEMBL details on recombined molecules with reported activity in ChEMBL against at least one kinase with IC50 ≤ 5 nM (Table S4); Structures of recombined molecules with reported activity in ChEMBL against at least one kinase with IC50 ≤ 500 nM (Figure S8); Distribution of Tanimoto similarities for recombined ligands each to their most similar molecule in ChEMBL (Figure S9) (PDF)

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    Cited By

    This article is cited by 11 publications.

    1. Merveille Eguida, Christel Schmitt-Valencia, Marcel Hibert, Pascal Villa, Didier Rognan. Target-Focused Library Design by Pocket-Applied Computer Vision and Fragment Deep Generative Linking. Journal of Medicinal Chemistry 2022, 65 (20) , 13771-13783. https://doi.org/10.1021/acs.jmedchem.2c00931
    2. Dominique Sydow, Eva Aßmann, Albert J. Kooistra, Friedrich Rippmann, Andrea Volkamer. KiSSim: Predicting Off-Targets from Structural Similarities in the Kinome. Journal of Chemical Information and Modeling 2022, 62 (10) , 2600-2616. https://doi.org/10.1021/acs.jcim.2c00050
    3. Iwan J. P. de Esch, Daniel A. Erlanson, Wolfgang Jahnke, Christopher N. Johnson, Louise Walsh. Fragment-to-Lead Medicinal Chemistry Publications in 2020. Journal of Medicinal Chemistry 2022, 65 (1) , 84-99. https://doi.org/10.1021/acs.jmedchem.1c01803
    4. Grigorii V. Andrianov, Wern Juin Gabriel Ong, Ilya Serebriiskii, John Karanicolas. Efficient Hit-to-Lead Searching of Kinase Inhibitor Chemical Space via Computational Fragment Merging. Journal of Chemical Information and Modeling 2021, 61 (12) , 5967-5987. https://doi.org/10.1021/acs.jcim.1c00630
    5. Lizhao Hu, Yuyao Yang, Shuangjia Zheng, Jun Xu, Ting Ran, Hongming Chen. Kinase Inhibitor Scaffold Hopping with Deep Learning Approaches. Journal of Chemical Information and Modeling 2021, 61 (10) , 4900-4912. https://doi.org/10.1021/acs.jcim.1c00608
    6. Hye Ree Yoon, Gyoung Jin Park, Anand Balupuri, Nam Sook Kang. TWN-FS method: A novel fragment screening method for drug discovery. Computational and Structural Biotechnology Journal 2023, 16 https://doi.org/10.1016/j.csbj.2023.09.037
    7. Yongchao Luo, Panpan Wang, Minjie Mou, Hanqi Zheng, Jiajun Hong, Lin Tao, Feng Zhu. A novel strategy for designing the magic shotguns for distantly related target pairs. Briefings in Bioinformatics 2023, 24 (1) https://doi.org/10.1093/bib/bbac621
    8. Christina Humer, Henry Heberle, Floriane Montanari, Thomas Wolf, Florian Huber, Ryan Henderson, Julian Heinrich, Marc Streit. ChemInformatics Model Explorer (CIME): exploratory analysis of chemical model explanations. Journal of Cheminformatics 2022, 14 (1) https://doi.org/10.1186/s13321-022-00600-z
    9. Yu Feng, Yuyao Yang, Wenbin Deng, Hongming Chen, Ting Ran. SyntaLinker-Hybrid: A deep learning approach for target specific drug design. Artificial Intelligence in the Life Sciences 2022, 2 , 100035. https://doi.org/10.1016/j.ailsci.2022.100035
    10. Yi Chen, Zhi-Zheng Wang, Ge-Fei Hao, Bao-An Song. Web support for the more efficient discovery of kinase inhibitors. Drug Discovery Today 2022, 27 (8) , 2216-2225. https://doi.org/10.1016/j.drudis.2022.04.002
    11. Jin-Rong Yang, Qiang Chen, Hao Wang, Xu-Yang Hu, Ya-Min Guo, Jian-Zhong Chen. Reliable CA-(Q)SAR generation based on entropy weight optimized by grid search and correction factors. Computers in Biology and Medicine 2022, 146 , 105573. https://doi.org/10.1016/j.compbiomed.2022.105573
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