Dataset Information

Recovery of PEX1-Gly843Asp peroxisome dysfunction by small-molecule compounds.

ABSTRACT: Zellweger spectrum disorder (ZSD) is a heterogeneous group of diseases with high morbidity and mortality caused by failure to assemble normal peroxisomes. There is no therapy for ZSD, but management is supportive. Nevertheless, one-half of the patients have a phenotype milder than classic Zellweger syndrome and exhibit a progressive disease course. Thus, patients would benefit if therapies became available and were instituted early. Recent reports indicate several interventions that result in partial peroxisome recovery in ZSD fibroblasts. To identify drugs that recover peroxisome functions, we expressed a GFP-peroxisome targeting signal 1 reporter in fibroblasts containing the common disease allele, PEX1-p.Gly843Asp. The GFP reporter remained cytosolic at baseline, and improvement in peroxisome functions was detected by the redistribution of the GFP reporter from the cytosol to the peroxisome. We established a high-content screening assay based on this phenotype assay and evaluated 2,080 small molecules. The cells were cultured in chemical for 2 days and then, were fixed and imaged by epifluorescent microscopy on a high-content imaging platform. We identified four compounds that partially recover matrix protein import, and we confirmed three using independent assays. Our results suggest that PEX1-p.G843D is a misfolded protein amenable to chaperone therapy.

SUBMITTER: Zhang R

PROVIDER: S-EPMC2851769 | biostudies-literature | 2010 Mar

REPOSITORIES: biostudies-literature

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Publications

Recovery of PEX1-Gly843Asp peroxisome dysfunction by small-molecule compounds.

Zhang Rui R Chen Li L Jiralerspong Sarn S Snowden Ann A Steinberg Steven S Braverman Nancy N

Proceedings of the National Academy of Sciences of the United States of America 20100308 12

Zellweger spectrum disorder (ZSD) is a heterogeneous group of diseases with high morbidity and mortality caused by failure to assemble normal peroxisomes. There is no therapy for ZSD, but management is supportive. Nevertheless, one-half of the patients have a phenotype milder than classic Zellweger syndrome and exhibit a progressive disease course. Thus, patients would benefit if therapies became available and were instituted early. Recent reports indicate several interventions that result in pa ...[more]

PMID: 20212125

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Project description:Scientists rely on high-throughput screening tools to identify promising small-molecule compounds for the development of biochemical probes and drugs. This study focuses on the identification of promiscuous bioactive compounds, which are compounds that appear active in many high-throughput screening experiments against diverse targets but are often false-positives which may not be easily developed into successful probes. These compounds can exhibit bioactivity due to nonspecific, intractable mechanisms of action and/or by interference with specific assay technology readouts. Such "frequent hitters" are now commonly identified using substructure filters, including pan assay interference compounds (PAINS). Herein, we show that mechanistic modeling of small-molecule reactivity using deep learning can improve upon PAINS filters when modeling promiscuous bioactivity in PubChem assays. Without training on high-throughput screening data, a deep learning model of small-molecule reactivity achieves a sensitivity and specificity of 18.5% and 95.5%, respectively, in identifying promiscuous bioactive compounds. This performance is similar to PAINS filters, which achieve a sensitivity of 20.3% at the same specificity. Importantly, such reactivity modeling is complementary to PAINS filters. When PAINS filters and reactivity models are combined, the resulting model outperforms either method alone, achieving a sensitivity of 24% at the same specificity. However, as a probabilistic model, the sensitivity and specificity of the deep learning model can be tuned by adjusting the threshold. Moreover, for a subset of PAINS filters, this reactivity model can help discriminate between promiscuous and nonpromiscuous bioactive compounds even among compounds matching those filters. Critically, the reactivity model provides mechanistic hypotheses for assay interference by predicting the precise atoms involved in compound reactivity. Overall, our analysis suggests that deep learning approaches to modeling promiscuous compound bioactivity may provide a complementary approach to current methods for identifying promiscuous compounds.

Dataset Information

Recovery of PEX1-Gly843Asp peroxisome dysfunction by small-molecule compounds.

Publications

Recovery of PEX1-Gly843Asp peroxisome dysfunction by small-molecule compounds.

Similar Datasets

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