Morphobase, an Encyclopedic Cell Morphology Database, and Its Use for Drug Target Identification

Futamura, Yushi; Kawatani, Masahito; Kazami, Sayaka; Tanaka, Kenichi; Muroi, Makoto; Shimizu, Takeshi; Tomita, Koji; Watanabe, Nobumoto; Osada, Hiroyuki

doi:10.1016/j.chembiol.2012.10.014

Cited by 89 publications

(82 citation statements)

References 33 publications

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“…Structural genomics and systems pharmacology greatly benefit from innovations in molecular informatics (50,51). We implemented and experimentally validated a unique method for ligand-based target prediction that extends the capabilities of related approaches.…”

Section: Resultsmentioning

confidence: 99%

Identifying the macromolecular targets of de novo-designed chemical entities through self-organizing map consensus

Reker

Rodrigues

Schneider

2014

Proc. Natl. Acad. Sci. U.S.A.

205

165

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De novo molecular design and in silico prediction of polypharmacological profiles are emerging research topics that will profoundly affect the future of drug discovery and chemical biology. The goal is to identify the macromolecular targets of new chemical agents. Although several computational tools for predicting such targets are publicly available, none of these methods was explicitly designed to predict target engagement by de novo-designed molecules. Here we present the development and practical application of a unique technique, self-organizing map-based prediction of drug equivalence relationships (SPiDER), that merges the concepts of self-organizing maps, consensus scoring, and statistical analysis to successfully identify targets for both known drugs and computer-generated molecular scaffolds. We discovered a potential off-target liability of fenofibraterelated compounds, and in a comprehensive prospective application, we identified a multitarget-modulating profile of de novo designed molecules. These results demonstrate that SPiDER may be used to identify innovative compounds in chemical biology and in the early stages of drug discovery, and help investigate the potential side effects of drugs and their repurposing options.drug design | target prediction | polypharmacology | machine learning | chemical similarity C omputer-assisted de novo molecular design has evolved as a popular source of ideas to combat the perceived lack of new chemical entities (NCEs) in chemical biology and drug discovery (1). We demonstrate that automated de novo design delivers readily synthesizable NCEs with desirable activity profiles. Although receptor-based design operates on a model of a macromolecular binding site, ligand-based methods are either explicitly or implicitly based on the chemical similarity principle (2) without requiring a receptor model (3). Instead, the latter typically uses some measure of chemical or pharmacophore feature similarity to a reference ligand as a fitness function, which aims to generate NCEs as template mimetics via scaffold hopping (4-8). We report the development, implementation, and successful prospective application of an innovative computational technique for the target profiling of de novo-designed molecules. The approach combines the concepts of self-organizing maps (SOMs) (9) for macromolecular target prediction (10), consensus scoring (11), and a statistical evaluation that provides confidence estimates for the predictions.Predicting polypharmacological activities is a topic relevant to chemical biology and drug discovery, not only to take advantage of inherent drug promiscuity but also to decrease lead compound attrition caused by unfavorable off-target modulation

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Section: Resultsmentioning

confidence: 99%

Identifying the macromolecular targets of de novo-designed chemical entities through self-organizing map consensus

Reker

Rodrigues

Schneider

2014

Proc. Natl. Acad. Sci. U.S.A.

205

165

View full text Add to dashboard Cite

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“…Discriminant analysis determines the distance from each point of the data set to the multivariate mean of each class and then assigns the point to the closest class (33). Essentially, discrimi- 12.57 (16) No antibiotic treatment (control) None None nant analysis is divided into two steps: (i) to build (i.e., "train") a model (i.e., classifier) using Raman spectra of cultures exposed to antibiotics of known class assignments, and (ii) to classify (i.e., predict the class of) a new Raman spectrum of an antibiotic-exposed culture based on the distance to the multivariate mean of the closest class. Two class assignment schemes were used with discriminant analysis to achieve the following two distinct objectives.…”

Section: Determination Of Mics E Coli Mach1 Cells (Invitrogen Lifementioning

confidence: 99%

“…Principal component analysis (PCA) of metabolomic profiles, obtained using high-performance liquid chromatography-mass spectrometry (HPLC-MS) data, revealed that the mechanism of action of Radix Tinosporae was similar to that of rifampin and norfloxacin (14). Techniques for multiparameter imaging-based phenotypic profiling rely on high-throughput fluorescence microscopy (15) or morphological analysis (16) and have shown great promise in drug discovery research (2,7). For example, bright-field cell imaging coupled with nuclear staining was used to construct a library of morphological profiles for cancer cells exposed to 54 anticancer drugs with known molecular targets (16).…”

mentioning

confidence: 99%

“…Techniques for multiparameter imaging-based phenotypic profiling rely on high-throughput fluorescence microscopy (15) or morphological analysis (16) and have shown great promise in drug discovery research (2,7). For example, bright-field cell imaging coupled with nuclear staining was used to construct a library of morphological profiles for cancer cells exposed to 54 anticancer drugs with known molecular targets (16). The library was then used with multivariate statistical analysis to identify the mechanistically unknown drugs NPD6689, NPD8617, and NPD8969 as novel tubulin inhibitors.…”

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confidence: 99%

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Phenotypic Profiling of Antibiotic Response Signatures in Escherichia coli Using Raman Spectroscopy

Athamneh

Alajlouni

Wallace

et al. 2014

Antimicrob Agents Chemother

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bIdentifying the mechanism of action of new potential antibiotics is a necessary but time-consuming and costly process. Phenotypic profiling has been utilized effectively to facilitate the discovery of the mechanism of action and molecular targets of uncharacterized drugs. In this research, Raman spectroscopy was used to profile the phenotypic response of Escherichia coli to applied antibiotics. The use of Raman spectroscopy is advantageous because it is noninvasive, label free, and prone to automation, and its results can be obtained in real time. In this research, E. coli cultures were subjected to three times the MICs of 15 different antibiotics (representing five functional antibiotic classes) with known mechanisms of action for 30 min before being analyzed by Raman spectroscopy (using a 532-nm excitation wavelength). The resulting Raman spectra contained sufficient biochemical information to distinguish between profiles induced by individual antibiotics belonging to the same class. The collected spectral data were used to build a discriminant analysis model that identified the effects of unknown antibiotic compounds on the phenotype of E. coli cultures. Chemometric analysis showed the ability of Raman spectroscopy to predict the functional class of an unknown antibiotic and to identify individual antibiotics that elicit similar phenotypic responses. Results of this research demonstrate the power of Raman spectroscopy as a cellular phenotypic profiling methodology and its potential impact on antibiotic drug development research.

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“…The high-content cell morphology database Morphobase utilizes similarity in chemical-genetic phenotype of cancer cell lines treated with small molecules for rapid target identification. 26 …”

Section: Methods In Target Identification and Elucidation Of Mechanmentioning

confidence: 99%

Biological targets and mechanisms of action of natural products from marine cyanobacteria

2015

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Marine cyanobacteria are an ancient group of organisms and prolific producers of bioactive secondary metabolites. These compounds are presumably optimized by evolution over billions of years to exert high affinity for their intended biological target in the ecologically relevant organism but likely also possess activity in different biological contexts such as human cells. Screening of marine cyanobacterial extracts for bioactive natural products has largely focused on cancer cell viability; however, diversification of the screening platform led to the characterization of many new bioactive compounds. Targets of compounds have oftentimes been elusive if the compounds were discovered through phenotypic assays. Over the past few years, technology has advanced to determine mechanism of action (MOA) and targets through reverse chemical genetic and proteomic approaches, which has been applied to certain cyanobacterial compounds and will be discussed in this review. Some cyanobacterial molecules are the most-potent-in-class inhibitors and therefore may become valuable tools for chemical biology to probe protein function but also be templates for novel drugs, assuming in vitro potency translates into cellular and in vivo activity. Our review will focus on compounds for which the direct targets have been deciphered or which were found to target a novel pathway, and link them to disease states where target modulation may be beneficial.

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Morphobase, an Encyclopedic Cell Morphology Database, and Its Use for Drug Target Identification

Cited by 89 publications

References 33 publications

Identifying the macromolecular targets of de novo-designed chemical entities through self-organizing map consensus

Identifying the macromolecular targets of de novo-designed chemical entities through self-organizing map consensus

Phenotypic Profiling of Antibiotic Response Signatures in Escherichia coli Using Raman Spectroscopy

Biological targets and mechanisms of action of natural products from marine cyanobacteria

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