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Virtual screening for small-molecule pathway regulators by image-profile matching.

ABSTRACT: Identifying the chemical regulators of biological pathways is a time-consuming bottleneck in developing therapeutics and research compounds. Typically, thousands to millions of candidate small molecules are tested in target-based biochemical screens or phenotypic cell-based screens, both expensive experiments customized to each disease. Here, our uncustomized, virtual, profile-based screening approach instead identifies compounds that match to pathways based on the phenotypic information in public cell image data, created using the Cell Painting assay. Our straightforward correlation-based computational strategy retrospectively uncovered the expected, known small-molecule regulators for 32% of positive-control gene queries. In prospective, discovery mode, we efficiently identified new compounds related to three query genes and validated them in subsequent gene-relevant assays, including compounds that phenocopy or pheno-oppose YAP1 overexpression and kill a Yap1-dependent sarcoma cell line. This image-profile-based approach could replace many customized labor- and resource-intensive screens and accelerate the discovery of biologically and therapeutically useful compounds.

SUBMITTER: Rohban MH

PROVIDER: S-EPMC9509476 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature

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Virtual screening for small-molecule pathway regulators by image-profile matching.

Rohban Mohammad H MH Fuller Ashley M AM Tan Ceryl C Goldstein Jonathan T JT Syangtan Deepsing D Gutnick Amos A DeVine Ann A Nijsure Madhura P MP Rigby Megan M Sacher Joshua R JR Corsello Steven M SM Peppler Grace B GB Bogaczynska Marta M Boghossian Andrew A Ciotti Gabrielle E GE Hands Allison T AT Mekareeya Aroonroj A Doan Minh M Gale Jennifer P JP Derynck Rik R Turbyville Thomas T Boerckel Joel D JD Singh Shantanu S Kiessling Laura L LL Schwarz Thomas L TL Varelas Xaralabos X Wagner Florence F FF Kafri Ran R Eisinger-Mathason T S Karin TSK Carpenter Anne E AE

Cell systems 20220902 9

Identifying the chemical regulators of biological pathways is a time-consuming bottleneck in developing therapeutics and research compounds. Typically, thousands to millions of candidate small molecules are tested in target-based biochemical screens or phenotypic cell-based screens, both expensive experiments customized to each disease. Here, our uncustomized, virtual, profile-based screening approach instead identifies compounds that match to pathways based on the phenotypic information in publ ...[more]

PMID: 36057257

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Project description:N6-Methyladenosine (m6A) is the most prevalent mRNA modification in mammalian cells that is mainly catalyzed by the methyltransferase complex of methyltransferase-like 3 and methyltransferase-like 14 (METTL3-METTL14). Many lines of evidence suggest that METTL3 plays important roles in several diseases such as cancers and viral infection. In the present study, 1,042 natural products from commercially available sources were chosen to establish a screening library, and docking-based high-throughput screening was performed to discover potential METTL3 inhibitors. The selected compounds were then further validated by an in vitro methyltransferase inhibition assay in which m6A content was determined by LC-MS/MS. A cellular assay of the inhibition of m6A methylation was performed to determine the METTL3 inhibitory activity of the selected compound. CCK-8 assay was applied to evaluate the effects of the selected compound on tumor cell viability. Additionally, binding mode analysis, molecular dynamics (MD) simulation, and binding free energy analysis were performed to study the process and characteristics of inhibitor binding. Finally, quercetin was identified as a METTL3 inhibitor with an IC50 value of 2.73 μM. The cellular assay of m6A methylation inhibition showed that quercetin decreased m6A level in a dose-dependent manner in MIA PaCa-2 pancreatic cancer cells. CCK-8 assay showed quercetin efficiently inhibited the proliferation of MIA PaCa-2 and Huh7 tumor cells, with IC50 values 73.51 ± 11.22 μM and 99.97 ± 7.03 μM, respectively. Molecular docking studies revealed that quercetin filled the pocket of the adenosine moiety of SAM but not the pocket of the SAM methionine in the METTL3 protein, and hydrogen bonds, hydrophobic interactions, and pi-stacking were formed. The values of the root mean square deviation (RMSD), the root mean square fluctuations (RMSF), and binding free energy suggested that quercetin can efficiently bind to the pocket of the METTL3 protein and form a stable protein-ligand complex. The present study is the first to identify METTL3 inhibitors from natural products, thus providing a basis for subsequent research and facilitating the development of METTL3-targeting drugs for diseases.

Dataset Information

Virtual screening for small-molecule pathway regulators by image-profile matching.

Publications

Virtual screening for small-molecule pathway regulators by image-profile matching.

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