Computational analyses of mechanism of action (MoA): data, methods and integration

Trapotsi, Maria-Anna; Hosseini-Gerami, Layla; Bender, Andreas

doi:10.1039/d1cb00069a

Cited by 46 publications

(40 citation statements)

References 246 publications

(319 reference statements)

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“…The prediction of bioactivity, mechanism of action (MOA) of compounds 1 as well as safety and toxicity 2 using only chemical structure data is challenging given that such models are limited in the diversity of the chemical space of the training data. 3 The chemical space of this data on which the model is trained is used to define the applicability domain of the model.…”

Section: Mainmentioning

confidence: 99%

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

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Yang

Trapotsi

et al. 2022

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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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Section: Mainmentioning

confidence: 99%

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

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Yang

Trapotsi

et al. 2022

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“… 7 , 9 , 13 , 17 Phenotypes from this assay are not obtained with any particular biological point of interest in mind and can be considered as image-based fingerprints of a compound covering a wide range of information. 7 , 18 , 19 …”

Section: Introductionmentioning

confidence: 99%

Cell Morphological Profiling Enables High-Throughput Screening for PROteolysis TArgeting Chimera (PROTAC) Phenotypic Signature

et al. 2022

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PROteolysis TArgeting Chimeras (PROTACs) use the ubiquitin–proteasome system to degrade a protein of interest for therapeutic benefit. Advances made in targeted protein degradation technology have been remarkable, with several molecules having moved into clinical studies. However, robust routes to assess and better understand the safety risks of PROTACs need to be identified, which is an essential step toward delivering efficacious and safe compounds to patients. In this work, we used Cell Painting, an unbiased high-content imaging method, to identify phenotypic signatures of PROTACs. Chemical clustering and model prediction allowed the identification of a mitotoxicity signature that could not be expected by screening the individual PROTAC components. The data highlighted the benefit of unbiased phenotypic methods for identifying toxic signatures and the potential to impact drug design.

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“…Finally, hybrid approaches, as demonstrated by Kitsak et al [ 16 ], have leveraged gene expression data from 64 different tissues and mapped genes expressed in specific tissues to a protein–protein interactome, revealing that these disease context-specific genes tend to be located in close proximity within the interactome. It is important to note that while transcriptomic experiments are often used as a proxy to reflect protein expression, the correlation between the two is often below 0.5 on average [ 26 , 40 ]. Nevertheless, correlations between genes whose mRNA is differentially expressed and their protein products have been shown to be significantly higher than genes whose mRNA is not differentially expressed, lending support to the use of differential mRNA expression to infer changes at the protein level [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

Elucidating gene expression patterns across multiple biological contexts through a large-scale investigation of transcriptomic datasets

et al. 2022

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Distinct gene expression patterns within cells are foundational for the diversity of functions and unique characteristics observed in specific contexts, such as human tissues and cell types. Though some biological processes commonly occur across contexts, by harnessing the vast amounts of available gene expression data, we can decipher the processes that are unique to a specific context. Therefore, with the goal of developing a portrait of context-specific patterns to better elucidate how they govern distinct biological processes, this work presents a large-scale exploration of transcriptomic signatures across three different contexts (i.e., tissues, cell types, and cell lines) by leveraging over 600 gene expression datasets categorized into 98 subcontexts. The strongest pairwise correlations between genes from these subcontexts are used for the construction of co-expression networks. Using a network-based approach, we then pinpoint patterns that are unique and common across these subcontexts. First, we focused on patterns at the level of individual nodes and evaluated their functional roles using a human protein–protein interactome as a referential network. Next, within each context, we systematically overlaid the co-expression networks to identify specific and shared correlations as well as relations already described in scientific literature. Additionally, in a pathway-level analysis, we overlaid node and edge sets from co-expression networks against pathway knowledge to identify biological processes that are related to specific subcontexts or groups of them. Finally, we have released our data and scripts at https://zenodo.org/record/5831786 and https://github.com/ContNeXt/, respectively and developed ContNeXt (https://contnext.scai.fraunhofer.de/), a web application to explore the networks generated in this work.

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Computational analyses of mechanism of action (MoA): data, methods and integration

Abstract: This review summarises different data, data resources and methods for computational mechanism of action (MoA) analysis, and highlights some case studies where integration of data types and methods enabled MoA elucidation on the systems-level.

Cited by 46 publications

References 246 publications

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

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

Cell Morphological Profiling Enables High-Throughput Screening for PROteolysis TArgeting Chimera (PROTAC) Phenotypic Signature

Elucidating gene expression patterns across multiple biological contexts through a large-scale investigation of transcriptomic datasets

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