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Diffstat (limited to 'mutagenicity.md')
| -rw-r--r-- | mutagenicity.md | 36 |
1 files changed, 15 insertions, 21 deletions
diff --git a/mutagenicity.md b/mutagenicity.md index 1014cc1..eb0ce3c 100644 --- a/mutagenicity.md +++ b/mutagenicity.md @@ -107,13 +107,11 @@ tertiary base PAs and PA N-oxides. In mammals, PAs are mainly metabolized in the liver. There are three principal metabolic pathways for 1,2-unsaturated PAs (@Chen2010): -Detoxification by +- Detoxification by hydrolysis of the ester bond on positions C7 and C9 by non-specific esterases to release necine base and necic acid. -- hydrolysis of the ester bond on positions C7 and C9 by non-specific esterases to release necine base and necic acid +- N-oxidation of the necine base to form a PA N-oxides, which can be either conjugated by phase II enzymes and then excreted or converted back into the corresponding parent PA (@Wang2005). This detoxification pathway is not possible for otonecine-type PAs, as they are N-methylated (see @fig:pa-schema). -- N-oxidation of the necine base to form a PA N-oxides, which can be either conjugated by phase II enzymes and then excreted or converted back into the corresponding parent PA (following ref) This detoxification pathway is not possible for otonecine-type PAs, as they are N-methylated (see @fig:pa-schema, @Wang2005) - -- Metabolic activation or toxification by oxidation (for retronecine-type PAs) or oxidative N-demethylation (for otonecine-type Pas) by cytochromes P450 isoforms CYP2B and 3A (@Lin1998, @Ruan2014) +- Metabolic activation or toxification by oxidation (for retronecine-type PAs) or oxidative N-demethylation (for otonecine-type Pas) by cytochromes P450 isoforms CYP2B and 3A (@Lin1998, @Ruan2014). The latter reactions result in the formation of dehydropyrrolizidine (DHP) that is highly reactive and causes damage by building adducts with protein, lipids and DNA (@Chen2010). On the other hand, open diesters and macrocyclic PAs have a reduced detoxification due to steric hinderance of the respective esterases (@Ruan2014) @@ -229,13 +227,13 @@ instances. They can be obtained from the following locations: *Training data:* - - sparse representation (<https://git.in-silico.ch/mutagenicity-paper/tree/mutagenicity/mp2d/fingerprints.mp2d>) - - descriptor matrix (<https://git.in-silico.ch/mutagenicity-paper/tree/mutagenicity/mp2d/mutagenicity-fingerprints.csv.gz>) + - sparse representation (<https://git.in-silico.ch/mutagenicity-paper/tree/mutagenicity/mutagenicity-mp2d>) + - descriptor matrix (<https://git.in-silico.ch/mutagenicity-paper/tree/mutagenicity/mutagenicity-mp2d.csv.gz>) *Pyrrolizidine alkaloids:* - - sparse representation (<https://git.in-silico.ch/mutagenicity-paper/tree/pyrrolizidine-alkaloids/mp2d/fingerprints.mp2d>) - - descriptor matrix (<https://git.in-silico.ch/mutagenicity-paper/tree/pyrrolizidine-alkaloids/mp2d/pa-fingerprints.csv.gz>) + - sparse representation (<https://git.in-silico.ch/mutagenicity-paper/tree/pyrrolizidine-alkaloids/pa-mp2d>) + - descriptor matrix (<https://git.in-silico.ch/mutagenicity-paper/tree/pyrrolizidine-alkaloids/pa-mp2d.csv>) #### Chemistry Development Kit (*CDK*) descriptors @@ -250,11 +248,11 @@ all substances with contradictory experimental mutagenicity data were removed. T contained {{cv.cdk.n_descriptors}} descriptors for {{cv.cdk.n_compounds}} compounds. -CDK training data can be obtained from <https://git.in-silico.ch/mutagenicity-paper/tree/mutagenicity/cdk/mutagenicity-mod-2.new.csv>. +CDK training data can be obtained from <https://git.in-silico.ch/mutagenicity-paper/tree/mutagenicity/mutagenicity-cdk.csv>. The same procedure was applied for the pyrrolizidine dataset yielding {{pa.cdk.n_descriptors}} descriptors for {{pa.cdk.n_compounds}} -compounds. CDK features for pyrrolizidine alkaloids are available at <https://git.in-silico.ch/mutagenicity-paper/tree/pyrrolizidine-alkaloids/cdk/PA-Padel-2D_m2.csv>. +compounds. CDK features for pyrrolizidine alkaloids are available at <https://git.in-silico.ch/mutagenicity-paper/tree/pyrrolizidine-alkaloids/pa-cdk.csv>. Algorithms ---------- @@ -442,14 +440,13 @@ Jupyter notebooks for these experiments can be found at the following locations *Crossvalidation:* - - MolPrint2D fingerprints: <https://git.in-silico.ch/mutagenicity-paper/tree/crossvalidations/mp2d/tensorflow> - - CDK descriptors: <https://git.in-silico.ch/mutagenicity-paper/tree/crossvalidations/cdk/tensorflow> + - MolPrint2D fingerprints: <https://git.in-silico.ch/mutagenicity-paper/tree/crossvalidations/tensorflow/prediction-v5-norm.ipynb> + - CDK descriptors: <https://git.in-silico.ch/mutagenicity-paper/tree/crossvalidations/tensorflow/prediction-v5-ext.ipynb> *Pyrrolizidine alkaloids:* - - MolPrint2D fingerprints: <https://git.in-silico.ch/mutagenicity-paper/tree/pyrrolizidine-alkaloids/mp2d/tensorflow> - - CDK descriptors: <https://git.in-silico.ch/mutagenicity-paper/tree/pyrrolizidine-alkaloids/cdk/tensorflow> - - CDK desc + - MolPrint2D fingerprints: <https://git.in-silico.ch/mutagenicity-paper/tree/pyrrolizidine-alkaloids/tensorflow/prediction-v5-ext-ext-Padel-2D.ipynb> + - CDK descriptors: <https://git.in-silico.ch/mutagenicity-paper/tree/pyrrolizidine-alkaloids/tensorflow/prediction-v5-ext-Padel-2D.ipynb> Results ======= @@ -696,15 +693,12 @@ neighbor algorithms like `lazar` have the practical advantage that the rationales for individual predictions can be presented in a straightforward manner that is understandable without a background in statistics or machine learning (a screenshot of the mutagenicity prediction for -12,21-Dihydroxy-4-methyl-4,8-secosenecinonan-8,11,16-trione can be found at -https://git.in-silico.ch/mutagenicity-paper/tree/figures/lazar-screenshot.png). +12,21-Dihydroxy-4-methyl-4,8-secosenecinonan-8,11,16-trione is depicted in @fig:lazar). This allows a critical examination of individual predictions and prevents blind trust in models that are intransparent to users with a toxicological background. {#fig:lazar} -<!-- ---> Descriptors ----------- @@ -762,7 +756,7 @@ In order to investigate, if any of the investigated models show systematic errors in the vicinity of pyrrolizidine-alkaloids we have performed a detailled t-SNE analysis of all models (see @fig:tsne-mp2d-rf and @fig:tsne-cdk-lazar-all for two examples, all visualisations can be found at -<https://git.in-silico.ch/mutagenicity-paper/figures>). +<https://git.in-silico.ch/mutagenicity-paper/tree/figures>). None of the models showed obvious deviations from their expected behaviour, so the reason for the disagreement between some of the models |