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. 2016 Aug;8(12):1399-412.
doi: 10.4155/fmc-2016-0079. Epub 2016 Aug 3.

Chemoinformatic expedition of the chemical space of fungal products

Mariana González-Medina  1 , Fernando D Prieto-Martínez  1 , J Jesús Naveja  1 , Oscar Méndez-Lucio  1 , Tamam El-Elimat  2 , Cedric J Pearce  3 , Nicholas H Oberlies  4 , Mario Figueroa  1 , José L Medina-Franco  1
Affiliations

Chemoinformatic expedition of the chemical space of fungal products

Mariana González-Medina et al. Future Med Chem. 2016 Aug.

Erratum in

  • Corrigendum.
    [No authors listed] [No authors listed] Future Med Chem. 2016 Nov;8(17):2167. doi: 10.4155/fmc-2016-0079c1. Future Med Chem. 2016. PMID: 27786551 Free PMC article. No abstract available.

Abstract

Aim: Fungi are valuable resources for bioactive secondary metabolites. However, the chemical space of fungal secondary metabolites has been studied only on a limited basis. Herein, we report a comprehensive chemoinformatic analysis of a unique set of 207 fungal metabolites isolated and characterized in a USA National Cancer Institute funded drug discovery project.

Results: Comparison of the molecular complexity of the 207 fungal metabolites with approved anticancer and nonanticancer drugs, compounds in clinical studies, general screening compounds and molecules Generally Recognized as Safe revealed that fungal metabolites have high degree of complexity. Molecular fingerprints showed that fungal metabolites are as structurally diverse as other natural products and have, in general, drug-like physicochemical properties.

Conclusion: Fungal products represent promising candidates to expand the medicinally relevant chemical space. This work is a significant expansion of an analysis reported years ago for a smaller set of compounds (less than half of the ones included in the present work) from filamentous fungi using different structural properties.

Keywords: chemical space; chemoinformatics; fungal metabolites; master key compound; molecular complexity; molecular fingerprint.

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Conflict of interest statement

Financial & competing interests disclosure

The authors thank the Universidad Nacional Autónoma de México (UNAM) for grant PAPIME No. PE200116, and the institutional program Nuevas Alternativas de Tratamiento para Enfermedades Infecciosas (NUATEI) of the Instituto de Investigaciones Biomédicas (IIB) UNAM for financial support, and the Consejo Nacional de Ciencia y Tecnología (CONACyT) for grant 236564. FDP-M is grateful to CONACyT for the fellowship 660465/576637. FD Prieto-Martínez is grateful to CONACyT for the fellowship No. 660465/576637. The isolation of fungal metabolites from the Mycosynthetix library via researchers at UNCG was funded by grant P01 CA125066 from the National Cancer Institute/National Institutes of Health, Bethesda, MD, USA. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

No writing assistance was utilized in the production of this manuscript.

Figures

<b>Figure 1.</b>
Figure 1.. Representative examples of fungal metabolites as drugs.
<b>Figure 2.</b>
Figure 2.. Molecular complexity of fungal isolates.
Box plots of the distributions of CCF, F-sp3 and globularity for the fungal isolates, molecules Generally Recognized as Safe, US FDA-approved drugs to treat cancer, FDA-approved drugs, drugs in clinical trials and compounds from a screening collection. Summary statistics are shown below each box plot. See text for details. 1st Q: First quartile; 3rd Q: Third quartile; CCF: Fraction of chiral centers; CLINIC: Drugs in clinical trials; FDA-NONC: FDA-approved drugs; FDA-ONC: FDA-approved drugs to treat cancer; FUNGI: Fungal isolates; F-sp3: Fraction of sp3 carbon atoms; GENERAL: Compounds from a screening collection; GLOB: Fraction of globularity; GRAS: Generally Recognized as Safe.
<b>Figure 3.</b>
Figure 3.. Box plot and summary statistics of the mean of F-sp3, fraction of chiral centers and globularity or mean complexity.
See text for details. 1st Q: First quartile; 3rd Q: Third quartile; CCF: Fraction of chiral centers; F-sp3: Fraction of sp3 carbon atoms; GLOB: Fraction of globularity.
<b>Figure 4.</b>
Figure 4.. Visualization of the molecular complexity mapped on a representation of the chemical space generated with principal component analysis of six physicochemical properties.
The first two principal components recover 85% of the variance. Data points are colored by the mean of Fsp3 and FCC using a continuous color scale from green (less complex) to red (more complex). Each panel corresponds to the visualization of single datasets. CLINIC: Drugs in clinical trials; FCC: Fraction of chiral centers; FDA-NONC: FDA-approved drugs; FDA-ONC: FDA-approved drugs to treat cancer; FUNGI: Fungal isolates; F-sp3: Fraction of sp3 carbon atoms; GENERAL: Compounds from a screening collection; GLOB: Fraction of globularity; GRAS: Generally Recognized as Safe; PC: Principal component.
<b>Figure 5.</b>
Figure 5.. Molecular diversity of the fungal metabolite dataset and reference collections.
This figure shows the cumulative distribution functions of all pair-wise similarity comparisons using the Tanimoto coefficient using Molecular ACCess System keys. The table summarizes the summary statistics of the cumulative distribution functions. The FUNGI set was less diverse than GRAS, US FDA, CLINIC and GENERAL but is as diverse as other natural product datasets reported in the literature [41,46]. 1st Q: First quartile; 3rd Q: Third quartile; CLINIC: Drugs in clinical trials; FDA-NONC: FDA-approved drugs; FDA-ONC: FDA-approved drugs to treat cancer; FUNGI: Fungal isolates; F-sp3: Fraction of sp3 carbon atoms; GENERAL: Compounds from a screening collection; GLOB: Fraction of globularity; GRAS: Generally Recognized as Safe; MACCS: Molecular ACCess System.
<b>Figure 6.</b>
Figure 6.. 3D and 2D visual representation of the chemical space of 207 fungal isolates considered in this work.
The visual representation was generated with a principal component analysis of six physicochemical properties: molecular weight, hydrogen bond donors, hydrogen bond acceptors, the octanol and/or water partition coefficient, topological polar surface area and number of rotatable bonds. The first two principal components capture 85.2% of the variance and the first three principal components capture 93.9% of the variance. FUNGI: purple; GRAS: yellow; FDA-ONC: green; FDA-NONC: red; CLINIC: blue; GENERAL: turquoise.
See this image and copyright information in PMC

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