Integrating genome-wide CRISPR screens and in silico drug profiling for targeted antidote development

Bei Wang, Yu Xu, Arabella H. Wan, Guohui Wan, Qiao-Ping Wang

Published: 2024-05-29 DOI: 10.1038/s41596-024-00995-z

genome-wide

CRISPR screens

antidote development

drug profiling

targeted therapy

AI 解读

Supplementary information

Reporting Summary

Supplementary Data 1

Scripts and example files for the virtual screen

Generation of mouse–human chimeric embryos

Genome-wide quantification of transcription factor binding at single-DNA-molecule resolution using methyl-transferase footprinting

Generation of CRISPR–Cas9-mediated genetic knockout human intestinal tissue–derived enteroid lines by lentivirus transduction and single-cell cloning

A puromycin-dependent activity-based sensing probe for histochemical staining of hydrogen peroxide in cells and animal tissues

Decoding personal biotic and abiotic airborne exposome

A clinically relevant model of focal embolic cerebral ischemia by thrombus and thrombolysis in rhesus monkeys

Bioengineering human intestinal mucosal grafts using patient-derived organoids, fibroblasts and scaffolds

Biomimetic tail-to-head terpene cyclizations using the resorcin[4]arene capsule catalyst

Neuroscience robotics for controlled induction and real-time assessment of hallucinations

Scanorama: integrating large and diverse single-cell transcriptomic datasets

Single-cell RNA sequencing (scRNA-seq)（单细胞RNA测序）

Data integration（数据集成）

Heterogeneity（异质性）

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References

Supplementary information

Lau, M.-T. et al. Molecular dissection of box jellyfish venom cytotoxicity highlights an effective venom antidote. Nat. Commun. 10, 1655 (2019).
Wang, B. et al. Identification of indocyanine green as a STT3B inhibitor against mushroom α-amanitin cytotoxicity. Nat. Commun. 14, 2241 (2023).
Joung, J. et al. Genome-scale CRISPR-Cas9 knockout and transcriptional activation screening. Nat. Protoc. 12, 828–863 (2017).
Lau, M.-T. et al. Systematic functional identification of cancer multi-drug resistance genes. Genome Biol. 21, 27 (2020).
Wei, L. et al. Genome-wide CRISPR/Cas9 library screening identified PHGDH as a critical driver for Sorafenib resistance in HCC. Nat. Commun. 10, 4681 (2019).
Reczek, C. R. et al. A CRISPR screen identifies a pathway required for paraquat-induced cell death. Nat. Chem. Biol. 13, 1274–1279 (2017).
Zhu, Q. et al. AHR mediates the aflatoxin B1 toxicity associated with hepatocellular carcinoma. Signal Transduct. Target. Ther. 6, 299 (2021).
Behan, F. M. et al. Prioritization of cancer therapeutic targets using CRISPR-Cas9 screens. Nature 568, 511–516 (2019).
Li, B. et al. Genome-wide CRISPR screen identifies host dependency factors for influenza A virus infection. Nat. Commun. 11, 164 (2020).
Wang, B. et al. The application of genome-wide CRISPR-Cas9 screens to dissect the molecular mechanisms of toxins. Comput. Struct. Biotechnol. J. 20, 5076–5084 (2022).
Xu, Y. et al. CRISPR screens in Drosophila cells identify Vsg as a Tc toxin receptor. Nature 610, 349–355 (2022).
Li, X. et al. Paeniclostridium sordellii hemorrhagic toxin targets TMPRSS2 to induce colonic epithelial lesions. Nat. Commun. 13, 4331 (2022).
Kitchen, D. B., Decornez, H., Furr, J. R. & Bajorath, J. Docking and scoring in virtual screening for drug discovery: methods and applications. Nat. Rev. Drug Discov. 3, 935–949 (2004).
Baeshen, N. A. et al. In silico screening of some compounds derived from the desert medicinal plant Rhazya stricta for the potential treatment of COVID-19. Sci. Rep. 12, 11120 (2022).
Rodrigues, D. F. et al. In vitro mechanistic studies on α-amanitin and its putative antidotes. Arch. Toxicol. 94, 2061–2078 (2020).
Arici, M. A. et al. Effects of resveratrol on alpha-amanitin-induced nephrotoxicity in BALB/c mice. Hum. Exp. Toxicol. 39, 328–337 (2020).
Shortt, K. et al. Identification of novel regulatory genes in APAP induced hepatocyte toxicity by a genome-wide CRISPR-Cas9 screen. Sci. Rep. 9, 1396 (2019).
Xiong, X. et al. Emerging enterococcus pore-forming toxins with MHC/HLA-I as receptors. Cell 185, 1157–1171.e22 (2022).
Lee, H. et al. Recognition of semaphorin proteins by P. sordellii lethal toxin reveals principles of receptor specificity in clostridial toxins. Cell 182, 345–356.e16 (2020).
Daniloski, Z. et al. Identification of required host factors for SARS-CoV-2 infection in human cells. Cell 184, 92–105.e16 (2021).
Shalem, O. et al. Genome-scale CRISPR-Cas9 knockout screening in human cells. Science 343, 84–87 (2014).
Doench, J. G. et al. Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9. Nat. Biotechnol. 34, 184–191 (2016).
Liu, G., Zhang, Y. & Zhang, T. Computational approaches for effective CRISPR guide RNA design and evaluation. Comput. Struct. Biotechnol. J. 18, 35–44 (2020).
Li, W. et al. MAGeCK enables robust identification of essential genes from genome-scale CRISPR/Cas9 knockout screens. Genome Biol. 15, 554 (2014).
König, R. et al. A probability-based approach for the analysis of large-scale RNAi screens. Nat. Methods 4, 847–849 (2007).
O’Boyle, N. M. et al. Open Babel: an open chemical toolbox. J. Cheminform. 3, 33 (2011).
Morris, G. M. et al. AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J. Comput. Chem. 30, 2785–2791 (2009).
Koes, D. R., Baumgartner, M. P. & Camacho, C. J. Lessons learned in empirical scoring with smina from the CSAR 2011 benchmarking exercise. J. Chem. Inf. Model. 53, 1893–1904 (2013).
Trott, O. & Olson, A. J. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J. Comput. Chem. 31, 455–461 (2010).
Eberhardt, J., Santos-Martins, D., Tillack, A. F. & Forli, S. AutoDock Vina 1.2.0: new docking methods, expanded force field, and Python bindings. J. Chem. Inf. Model. 61, 3891–3898 (2021).
Sanson, K. R. et al. Optimized libraries for CRISPR-Cas9 genetic screens with multiple modalities. Nat. Commun. 9, 5416 (2018).
Sherman, B. T. et al. DAVID: a web server for functional enrichment analysis and functional annotation of gene lists (2021 update). Nucleic Acids Res. 50, W216–W221 (2022).
Huang, D. W., Sherman, B. T. & Lempicki, R. A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 4, 44–57 (2009).
Liao, Y., Wang, J., Jaehnig, E. J., Shi, Z. & Zhang, B. WebGestalt 2019: gene set analysis toolkit with revamped UIs and APIs. Nucleic Acids Res. 47, W199–W205 (2019).
Zhou, Y. et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat. Commun. 10, 1523 (2019).
Irwin, J. J. et al. ZINC20—a free ultralarge-scale chemical database for ligand discovery. J. Chem. Inf. Model. 60, 6065–6073 (2020).
Wishart, D. S. et al. DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res. 46, D1074–D1082 (2018).
Berman, H. M. et al. The Protein Data Bank. Nucleic Acids Res. 28, 235–242 (2000).
Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).
Varadi, M. et al. AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models. Nucleic Acids Res. 50, D439–D444 (2022).
Durrant, J. D. & McCammon, J. A. NNScore 2.0: a neural-network receptor-ligand scoring function. J. Chem. Inf. Model. 51, 2897–2903 (2011).
Kim, S. et al. PubChem 2023 update. Nucleic Acids Res. 51, D1373–D1380 (2023).
Volkamer, A., Kuhn, D., Rippmann, F. & Rarey, M. DoGSiteScorer: a web server for automatic binding site prediction, analysis and druggability assessment. Bioinformatics 28, 2074–2075 (2012).
Jiménez, J., Doerr, S., Martínez-Rosell, G., Rose, A. S. & De Fabritiis, G. DeepSite: protein-binding site predictor using 3D-convolutional neural networks. Bioinformatics 33, 3036–3042 (2017).
Yu, J., Zhou, Y., Tanaka, I. & Yao, M. Roll: a new algorithm for the detection of protein pockets and cavities with a rolling probe sphere. Bioinformatics 26, 46–52 (2010).

Integrating genome-wide CRISPR screens and in silico drug profiling for targeted antidote development

Supplementary information

Reporting Summary

Supplementary Data 1

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