The taste of toxicity: a quantitative analysis of bitter and toxic molecules

Ido Nissim, Ayana Dagan-Wiener, Masha Y. Niv

Institute of Biochemistry, Food Science and Nutrition, Robert H Smith Faculty of Agriculture

Food and Environment, The Hebrew University, Rehovot 76100, Israel

and The Fritz Haber Center for Molecular Dynamics, The Hebrew University, Jerusalem 91904,

Israel

First published: 11 November 2017 by IUBMB Life

DOI: 10.1002/iub.1694

Supplementary Data

A SMARTS Patterns

Flavonoid 1) [#6]1~[#6]~[#6]~[#6]~2~[#6](~[#6]~[#6]~[#6](~[#6]3~[#6]~[#6]~[#6]~[#6]~[#6]~3)~[#8]2)~[#6]1 2) [#6]1~[#6]~[#6]~[#6]2~[#6](~[#6]1)~[#6]~[#6](~[#6]~[#8]2)~[#6]1~[#6]~[#6]~[#6]~[#6]~[#6]~1 3) [#6]1~[#6]~[#6]~[#6]2~[#6](~[#6]1)~[#6](~[#6]~[#6]~[#8]2)~[#6]1~[#6]~[#6]~[#6]~[#6]~[#6]~1

6-Atom ring Alkaloid 1) [#6]1~[#6]~[#6]~[#6]~[#6]~[#7]~1 2) [#6]1~[#6]~[#6]~[#7]~[#6]~[#7]~1

5-Atom ring Alkaloid 1) [#6]1~[#6]~[#6]~[#6]~[#7]~1 2) [#6]1~[#6]~[#7]~[#6]~[#7]~1

Glucosinolate [#6]1([#6]([#8][#6]([#6]([#6]1~[#8])~[#8])[#16][#6]=[#7][#8]~[#16](~[#8])(~[#8])~[#8])[#6]~[#8])~[#8]

Thiocyanate or Isothiocyanate

1) [#16][#6]#[#7] 2) [#7]=[#6]=[#16]

Sulfonamide [#16](=[#8])(=[#8])[#7]

Terpenes [#6]~[#6](~[#6])~[#6]~[#6]

Alpha Acids [#6]1(~[#6](~[#6](~[#6]~[#6](~[#6]~1(~[#8])[#6][#6]=[#6][#6])~[#8])~[#8])[#6][#6]=[#6]([#6])[#6])~[#8]

B BitterDB FocTox CombiTox

Flavonoid 75 0 1,183

6-Atom ring Alkaloid 145 14 29,669

5-Atom ring Alkaloid 56 10 17,897

Glucosinolate 4 0 1

Thiocyanate or Isothiocyanate

3 4 476

Sulfonamide 31 0 4,251

Terpenes 531 88 89,225

Alpha Acids 4 0 0

Suppl. Table 1: A) Functional groups and chemical moieties associated with bitter taste and the corresponding SMARTS pattern used to search for them in the various datasets. B) The frequencies in the bitter and toxic datasets for each moiety.

http://onlinelibrary.wiley.com/doi/10.1002/iub.1694/full

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Suppl. Figure 1: Relative frequency of dataset similarity between A) FocTox and CombiTox, B)

BitterDB and FocTox. Similarity to nearest neighbor is calculated using the Tanimoto Coefficient

based on MOLPRINT2D fingerprints. Colors represent datasets – green for BitterDB, red for

CombiTox, purple for FocTox. The X-axis is the range of similarity between compounds and the Y-

axis represents the percentage of the datasets.

Suppl. Figure 2: Number of compounds in each toxicity category from A) Predicted bitter

compounds in CombiTox, B) Predicted bitter compounds in FocTox, C) Predicted non-bitter

compounds in CombiTox, D) Predicted non-bitter compounds in FocTox. Categories 1 and 2 were

marked as ‘Fatal’, category 3 was marked as ‘Toxic’, category 4 was marked as ‘Harmful’,

categories 5 and non-toxic were marked as ‘May be harmful or non-toxic’. All predicted

compounds in these panels were predicted with the 0.6 bitter cutoff.

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Fatal Toxic Harmful May be harmful

or non-toxic Category 1

5,000 mg/kg bw

BitterDB 2 3 28 67 50 25

Non-Bitter 2 1 19 146 284 85

FocTox 34 70 47 24 7 5

FocTox Predicted Bitter 17 39 14 9 3 4

FocTox Predicted Non-Bitter 4 8 1 1 2 0

CombiTox 133 362 787 2031 1472 812

CombiTox Predicted Bitter 37 129 227 507 300 177

CombiTox Predicted Non-Bitter 31 41 146 402 423 204

Suppl. Table 2: Reported LD50 values (mg/kg bw) via the Acute Oral Toxicity Database for all

datasets. ‘Predicted Bitter’ are subsets of the toxic datasets which were predicted as bitter with

the 0.6 bitter cutoff.

Suppl. Figure 3: Number of mouse bitter taste receptors (TAS2Rs) activated for each compound

and the respective LD50 value.

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Suppl. Figure 4: Number of bitter taste receptors (TAS2Rs) activated for each compound and the respective log (1/LD50) value for all compounds. This is based on the book QSAR: Hansch Analysis and

Related Approaches by Kubinyi, H. which recommends on using log(1/c) for QSAR studies to directly

connect the distribution to free energy (ΔG).