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An integrated approach for identification and target validation of antifungal compounds active against Erg11p.

ABSTRACT: Systemic life-threatening fungal infections represent a significant unmet medical need. Cell-based, phenotypic screening can be an effective means of discovering potential novel antifungal compounds, but it does not address target identification, normally required for compound optimization by medicinal chemistry. Here, we demonstrate a combination of screening, genetic, and biochemical approaches to identify and characterize novel antifungal compounds. We isolated a set of novel non-azole antifungal compounds for which no target or mechanism of action is known, using a screen for inhibition of Saccharomyces cerevisiae proliferation. Haploinsufficiency profiling of these compounds in S. cerevisiae suggests that they target Erg11p, a cytochrome P450 family member, which is the target of azoles. Consistent with this, metabolic profiling in S. cerevisiae revealed a buildup of the metabolic intermediates prior to Erg11p activity, following compound treatment. Further, human cytochrome P450 is also inhibited in in vitro assays by these compounds. We modeled the Erg11p protein based on the human CYP51 crystal structure, and in silico docking of these compounds suggests that they interact with the heme center in a manner similar to that of azoles. Consistent with these docking observations, Candida strains carrying azole-resistant alleles of ERG11 are also resistant to the compounds in this study. Thus, we have identified non-azole Erg11p inhibitors, using a systematic approach for ligand and target characterization.

SUBMITTER: Hoepfner D

PROVIDER: S-EPMC3421618 | biostudies-literature | 2012 Aug

REPOSITORIES: biostudies-literature

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An integrated approach for identification and target validation of antifungal compounds active against Erg11p.

Hoepfner Dominic D Karkare Shantanu S Helliwell Stephen B SB Pfeifer Martin M Trunzer Markus M De Bonnechose Sophie S Zimmerlin Alfred A Tao Jianshi J Richie Daryl D Hofmann Andreas A Reinker Stefan S Frederiksen Mathias M Movva N Rao NR Porter Jeffrey A JA Ryder Neil S NS Parker Christian N CN

Antimicrobial agents and chemotherapy 20120521 8

Systemic life-threatening fungal infections represent a significant unmet medical need. Cell-based, phenotypic screening can be an effective means of discovering potential novel antifungal compounds, but it does not address target identification, normally required for compound optimization by medicinal chemistry. Here, we demonstrate a combination of screening, genetic, and biochemical approaches to identify and characterize novel antifungal compounds. We isolated a set of novel non-azole antifu ...[more]

PMID: 22615293

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Project description:BACKGROUND:Infections due to parasitic nematodes are common causes of morbidity and fatality around the world especially in developing nations. At present however, there are only three major classes of drugs for treating human nematode infections. Additionally the scientific knowledge on the mechanism of action and the reason for the resistance to these drugs is poorly understood. Commercial incentives to design drugs that are endemic to developing countries are limited therefore, virtual screening in academic settings can play a vital role is discovering novel drugs useful against neglected diseases. In this study we propose to build robust machine learning model to classify and screen compounds active against parasitic nematodes. RESULTS:A set of compounds active against parasitic nematodes were collated from various literature sources including PubChem while the inactive set was derived from DrugBank database. The support vector machine (SVM) algorithm was used for model development, and stratified ten-fold cross validation was used to evaluate the performance of each classifier. The best results were obtained using the radial basis function kernel. The SVM method achieved an accuracy of 81.79% on an independent test set. Using the model developed above, we were able to indentify novel compounds with potential anthelmintic activity. CONCLUSION:In this study, we successfully present the SVM approach for predicting compounds active against parasitic nematodes which suggests the effectiveness of computational approaches for antiparasitic drug discovery. Although, the accuracy obtained is lower than the previously reported in a similar study but we believe that our model is more robust because we intentionally employed stringent criteria to select inactive dataset thus making it difficult for the model to classify compounds. The method presents an alternative approach to the existing traditional methods and may be useful for predicting hitherto novel anthelmintic compounds.

Dataset Information

An integrated approach for identification and target validation of antifungal compounds active against Erg11p.

Publications

An integrated approach for identification and target validation of antifungal compounds active against Erg11p.

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