Cell morphological profiling enables high-throughput screening for PROteolysis TArgeting Chimera (PROTAC) phenotypic signature

Moreau, Kévin; Trapotsi, Maria-Anna; Mouchet, Elizabeth; Williams, Guy; Monteverde, Tiziana; Juhani, Karolina; Turkki, Riku; Miljković, Filip; Martinsson, Anton; Mervin, Lewis; Müllers, Erik; Barrett, Ian P.; Engkvist, Ola; Bender, Andreas

doi:10.1101/2022.01.17.476610

Cited by 5 publications

(5 citation statements)

References 37 publications

(40 reference statements)

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“…The pharmaceutical industry needs disruptive technologies to rapidly reduce the cost and failure rates for getting life-changing medicines to patients. The image-based microscopy profiling assay, Cell Painting 1 , has shown promise in several steps in the drug discovery pipeline 2 , including disease phenotype identification 3 , hit identification 4,5 assay activity prediction 6 , toxicity detection 7,8 and mechanism of action determination 9 , as well as in basic biological research such as functional genomics 10 . In this assay, eight cellular components are stained with six inexpensive dyes and imaged in five channels on a fluorescence microscope.…”

Section: Introductionmentioning

confidence: 99%

JUMP Cell Painting dataset: morphological impact of 136,000 chemical and genetic perturbations

Chandrasekaran

Ackerman

Alix

et al. 2023

Preprint

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Image-based profiling has emerged as a powerful technology for various steps in basic biological and pharmaceutical discovery, but the community has lacked a large, public reference set of data from chemical and genetic perturbations. Here we present data generated by the Joint Undertaking for Morphological Profiling (JUMP)-Cell Painting Consortium, a collaboration between 10 pharmaceutical companies, six supporting technology companies, and two non-profit partners. When completed, the dataset will contain images and profiles from the Cell Painting assay for over 116,750 unique compounds, over-expression of 12,602 genes, and knockout of 7,975 genes using CRISPR-Cas9, all in human osteosarcoma cells (U2OS). The dataset is estimated to be 115 TB in size and capturing 1.6 billion cells and their single-cell profiles. File quality control and upload is underway and will be completed over the coming months at the Cell Painting Gallery: https://registry.opendata.aws/cellpainting-gallery. A portal to visualize a subset of the data is available at https://phenaid.ardigen.com/jumpcpexplorer/.

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Section: Introductionmentioning

confidence: 99%

JUMP Cell Painting dataset: morphological impact of 136,000 chemical and genetic perturbations

Chandrasekaran

Ackerman

Alix

et al. 2023

Preprint

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“…Indeed, PROTAC phenotypic signatures have already been identified through cell morphological profiling. 186 3.4. Preparing Data for AI/ML-Enabled Analysis of Phenotypic Screening Data.…”

Section: Maximizing the Depth Of Downstream Analysismentioning

confidence: 99%

Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence

2023

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Only around 20% of the human proteome is considered to be druggable with small-molecule antagonists. This leaves some of the most compelling therapeutic targets outside the reach of ligand discovery. The concept of targeted protein degradation (TPD) promises to overcome some of these limitations. In brief, TPD is dependent on small molecules that induce the proximity between a protein of interest (POI) and an E3 ubiquitin ligase, causing ubiquitination and degradation of the POI. In this perspective, we want to reflect on current challenges in the field, and discuss how advances in multiomics profiling, artificial intelligence, and machine learning (AI/ML) will be vital in overcoming them. The presented roadmap is discussed in the context of small-molecule degraders but is equally applicable for other emerging proximity-inducing modalities.

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“…These numerical features representing morphological properties such as shape, size, area, intensity, granularity, and correlation, among many others, are considered versatile biological descriptors of a system. 7 Previous studies have shown Cell Painting data to be predictive of a wide range of bioactivity and drug safety-related endpoints such as the mechanism of action 14 , cytotoxicity 24 , microtubule-binding activity 15 , and mitochondrial toxicity 25 , and has been used to identify phenotypic signatures of PROteolysis TArgeting Chimeras (PRO-TACs) 16 as well as determining the impact of lung cancer variants 17 . Thus Cell Painting data can be expected to contain a signal about the biological activity of the compound perturbation, 7 and in this work, we explored how best to combine Cell Painting and chemical structural models for the prediction of a wide range of biological assay outcomes.…”

Section: Mainmentioning

confidence: 99%

Merging Bioactivity Predictions from Cell Morphology and Chemical Fingerprint Models Using Similarity to Training Data

Seal

Yang

Trapotsi

et al. 2022

Preprint

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The applicability domain of machine learning models trained on structural fingerprints for the prediction of biological endpoints is often limited by the diversity of chemical space of the training data. In this work, we developed similarity-based merger models which combined the output of individual models trained on cell morphology (based on Cell Painting) and chemical structure (based on chemical fingerprints). Using a combination of a decision tree and logistic regression models on the structural versus morphological feature space of the training data, which leveraged the similarity of test compounds to training compounds, the similarity-based merger models used logistic equations to weigh individual model outputs. We applied these models to predict assay hit calls of 92 assays from ChEMBL and PubChem and 89 anonymised assays released by the Broad Institute, where the required Cell Painting annotations were available. We found that for the 181 assays used in this study the similarity-based merger model improved AUC in relative terms by 16.3% compared to the models using chemical structure alone (mean AUC of 0.75 vs. 0.64), and by 21.3% compared to the models using Cell Painting data alone (mean AUC of 0.62). Our results demonstrate that similarity-based merger models combining structure and cell morphology models can more accurately predict a wide range of biological assay outcomes and expand the applicability domain by better extrapolating to new structural and morphology spaces.

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Cell morphological profiling enables high-throughput screening for PROteolysis TArgeting Chimera (PROTAC) phenotypic signature

Cited by 5 publications

References 37 publications

JUMP Cell Painting dataset: morphological impact of 136,000 chemical and genetic perturbations

JUMP Cell Painting dataset: morphological impact of 136,000 chemical and genetic perturbations

Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence

Merging Bioactivity Predictions from Cell Morphology and Chemical Fingerprint Models Using Similarity to Training Data

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