High-Throughput Functional Annotation of Natural Products by Integrated Activity Profiling
Notice bibliographique
Résumé
Abstract Determining mechanism of action (MOA) is one of the biggest challenges in natural products discovery. Here, we report a comprehensive platform that uses Similarity Network Fusion (SNF) to improve MOA predictions by integrating data from the cytological profiling high-content imaging platform and the gene expression platform FUSION, and pairs these data with untargeted metabolomics analysis for de novo bioactive compound discovery. The predictive value of the integrative approach was assessed using a library of target-annotated small molecules as benchmarks. Using Kolmogorov–Smirnov (KS) tests to compare in-class to out-of-class similarity, we found that SNF retains the ability to identify significant in-class similarity across a diverse set of target classes, and could also find target classes that were not detectable in either platform alone. This confirmed that integration of expression-based and image-based phenotypes can accurately report on MOA. Furthermore, we integrated untargeted metabolomics of complex natural product fractions with the SNF network to map biological signatures to specific metabolites. Three examples are presented where SNF coupled with metabolomics was used to directly functionally characterize natural products and accelerate identification of bioactive metabolites, including the discovery of the novel azoxy-containing biaryl compounds parkamycins A and B. Our results support SNF integration of multiple phenotypic screening approaches along with untargeted metabolomics as a powerful approach for advancing natural products drug discovery. Significance statement New data-driven methods to aid in the discovery and biological characterization of natural products are necessary to advance the field. Assigning the mechanism of action (MOA) to novel bioactive compounds is an essential step in drug discovery and a major challenge in chemical biology. Despite technological advances in isolation, synthesis and screening strategies that make many bioactive substances readily available, in most cases their biological targets remain unknown. Additionally, a major bottleneck in natural products discovery efforts is de-replication of the large number of known compounds that predominate in crude extracts and fraction libraries. Advances in metabolomics has provided a better understanding of the constituents present in these libraries, but is not sufficient in itself to drive the discovery of novel biologically active metabolites. Here we describe an unbiased, data-driven strategy which integrates phenotypic screening with metabolomics into a single platform that provides rapid identification and functional annotation of natural products. This approach can be applied to any cohort of uncharacterized chemicals and represents a strategy that could significantly accelerate the process of drug discovery.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».