Package: pathmodel
PathModel: inferring new biochemical reactions and metabolite structures to understand metabolic pathway driftPathModel is a prototype to infer new biochemical reactions and new metabolite structures. The biological motivation for developing it is described in thisarticle, published iniScience.  You can also watch theassociated presentationat theJOBIM2020 conference.There is no guarantee that this script will work, it is a Work In Progress in early state.Table of contentsDescriptionMetabolic Pathway Drift HypothesisProgramInstallationRequirementsUsing Singularity and Singularity HubUsing dockerUsing gitUsing pipUsing conda environment (to install all dependencies)PathModel presentationInputCommands and Python importOutputTutorialTutorial on fictitious dataInput dataCommandsResultsTutorial oniScienceArticle data (Chondrus crispussterol and Mycosporine-like Amino Acids pathways)Article dataSterol pathwayMAA pathwayArticle commandsArticle resultsSterol pathway resultsMAA pathway resultsDataCitationBibliographyDescriptionMetabolic Pathway Drift HypothesisMetabolic Pathway Drift hypothesizes that metabolic pathways can be conserved even if their biochemical reactions undergo variations. These variations can be non-orthologous displacement of genes or changes in enzyme order.Metabolic Pathway Drift HypothesisTo test the hypothesis on the enzyme order drift, we develop PathModel to infer possible enzyme order changes in metabolic pathways.Metabolic Pathway Drift HypothesisProgramPathModel is developed inASPusing theclingo grounder and solver. It is divided in three ASP scripts.The first one,ReactionSiteExtraction.lpcreates biochemical transformation from reactions. The biochemical transformation of a reaction corresponds to the atoms and bonds changes between the reactant and the product of the reaction.When a reaction occurred between two molecules, the script will compare atoms and bonds of the two molecules of the reaction and will extract a reaction site before the reaction (composed of atoms and bonds that are present in the reactant but absent in the product) and a reaction site after the reaction (composed of atoms and bonds present in the product but absent in the reactant).ReactionSiteExtraction produces two sites for each reaction (one before and one after the reaction). This corresponds to the biochemical transformation induced by the reaction.A second script,MZComputation.lpwill compute the MZ for each known molecule. It also computes the MZ changes between the reactant and the product of a reaction.These data will be used by the third script:PathModel.lp.PathModel uses the incremental mode from Clingo. Using a source molecule, it will apply two inference methods until it reaches a goal (another molecules).InstallationRequirementsPathModel is a Python3 package using Answer Set Programming (ASP) to infer new biochemical reactions and new metabolites structures. It is divided in two parts:a wrapper (pathmodel_wrapper.py) for the ASP programs (MZComputation.lp,ReactionSiteExtraction.lpandPathModel.lp).a plotting script (molecule_creation.py) to create pictures of molecules and pathways.PathModel requires:clingo: which must be installed with Lua compatibility (a good way to have it is withconda).clyngor package(can be installed with clingo withclyngor-with-clingo package).networkx(withgraphvizandpygraphviz).matplotlib package.rdkit package.Using Singularity and Singularity HubYou can use the container fromSingularity Hub.# Choose your preference to pull the container from Singularity Hub (once)singularitypullshub://pathmodel/pathmodel-singularity# Enter itsingularityrunpathmodel-singularity_latest.sifpathmodeltest-ooutput_folderpathmodel_plot-ioutput_folder/MAApathmodel_plot-ioutput_folder/sterol# Or use as a command linesingularityexecpathmodel-singularity_latest.sifpathmodeltest-ooutput_foldersingularityexecpathmodel-singularity_latest.sifpathmodel_plot-ioutput_folder/MAAsingularityexecpathmodel-singularity_latest.sifpathmodel_plot-ioutput_folder/sterolThis container is build from thisSingularity recipe. If you prefer, you can use this recipe:singularitybuildpathmodel.sifSingularityUsing dockerA docker image of pathmodel is available atdockerhub. This image is based on thepathmodel Dockerfile.dockerrun-ti-v/path/shared/container:/shared--name="mycontainer"pathmodel/pathmodelbashThis command will download the image and create a container with a shared path. It will launch a bash terminal where you can use the command pathmodel (seeCommands and Python importandTutorial).Using gitThe package can be installed either using python setup or pip install (see below)gitclonehttps://github.com/pathmodel/pathmodel.gitcdPathModelpythonsetup.pyinstallUsing pipIf you have all the dependencies on your system, you can just download Pathmodel usingpip.pipinstallpathmodelUsing conda environment (to install all dependencies)Due to all the dependencies required by the scripts of Pathmodel, we create a conda environment file that contains all dependencies.First you needConda.
To avoid conflict between the conda python and your system python, you could use a conda environment andMiniconda.If you want to test this, the first thing is to install miniconda:# Download Minicondawgethttps://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh# Give the permission to the installer.chmod+xMiniconda3-latest-Linux-x86_64.sh# Install it at the path that you choose../Miniconda3-latest-Linux-x86_64.sh-p/path/where/miniconda/will/be/installed/-b# Delete installer.rmMiniconda3-latest-Linux-x86_64.sh# Add conda path to you bash settings.echo'. /path/where/miniconda/is/installed/etc/profile.d/conda.sh'>>~/.bashrc# Will activate the environment.
# For more information: https://github.com/conda/conda/blob/master/CHANGELOG.md#440-2017-12-20echo'conda activate base'>>~/.bashrcAfter this you need to restart your terminal or use: source ~/.bashrcThen you will get our conda environment file:# Download our conda environment file from Pathmodel github page.wgethttps://raw.githubusercontent.com/pathmodel/pathmodel/master/conda/pathmodel_env.yaml# Use the file to create the environment and install all dependencies.condaenvcreate-fpathmodel.yamlIf no error occurs, you can now access a conda environment with pathmodel:# Activate the environment.condaactivatepathmodel# Launch the help of Pathmodel.(pathmodel)pathmodel-hYou can exit the environment with:# Deactivate the environment.condadeactivatePathModel presentationInputMolecules are modelled with atoms (hydrogen excluded) and bonds (single and double).atom("Molecule1",1,carb).atom("Molecule1",2,carb).bond("Molecule1",single,1,2).atom("Molecule2",1,carb).atom("Molecule2",2,carb).atom("Molecule2",3,carb).bond("Molecule2",single,1,2).bond("Molecule2",single,2,3).Reactions between molecules are represented as link between two molecules with a name:reaction(reaction1,"Molecule1","Molecule2").A common domain is needed to find which molecules share structure with others:atomDomain(commonDomainName,1,carb).atomDomain(commonDomainName,2,carb).bondDomain(commonDomainName,single,1,2).A molecule source is defined:source("Molecule1").Initiation and goal of the incremental grounding must be defined:init(pathway("Molecule1","Molecule2")).goal(pathway("Molecule1","Molecule3")).M/Z ratio can be added to check whether there is a metabolite that can be predict with this ratio. M/Z ratio must be multiplied by 10 000 because Clingo doesn’t use decimals. An example with a M/Z of 270,272:mzfiltering(2702720).Molecules absent in the organism of study can be specified. They will not be used by the inference method.absentmolecules("Molecule1").Commands and Python importRun PathModel prediction:pathmodelinfer-idata.lp-ooutput_folder-s100PathModel arguments:-i: input file-o: output folder-s: number of maximal steps before PathModel stops (to avoid endless run), by default at 100If PathModel does not find the goal molecules before it reaches the number of maximal steps, it will send an error message.Create picture representing the results (like new molecules inferred from M/Z ratio):pathmodel_plot-ioutput_folder_from_pathmodelIn python (pathmodel_plot is not available in import call):importpathmodelpathmodel.pathmodel_analysis('data.lp',output_folder,step_limit=100)OutputWith theinfer command, pathmodel will use the data file and try to create an output folder:output_folder
├── data_pathmodel.lp
├── pathmodel_data_transformations.tsv
├── pathmodel_incremental_inference.tsv
├── pathmodel_output.lpdata_pathmodel.lp contains intermediary files for PathModel. Specifically, it contains the input data and the results ofReactionSiteExtraction.lp(diffAtomBeforeReaction,diffAtomAfterReaction,diffBondBeforeReaction,diffBondAfterReaction,siteBeforeReaction,siteAfterReaction) and ofMZComputation.lp(domain,moleculeComposition,moleculeNbAtoms,numberTotalBonds,moleculeMZ,reactionMZ). The python wrapper gives this file toPathModel.lpas input.pathmodel_data_transformations.tsv contains all the transformation inferred from the input data and theReactionSiteExtraction.lpscript.pathmodel_incremental_inference.tsv shows the step of the incremental mode of clingo when a new reaction has been inferred using a known transformation. It does not show the step when passing through a known reaction, so the first step number in the file scan be superior to 1.pathmodel_output.lp is the output lp file ofPathModel.lp(newreaction,predictatom,predictbond,reaction,inferred).Then if you use thepathmodel_plot commandon the output_folder, pathmodel will create the following structure:output_folder
├── ...
├── molecules
        ├── Molecule1
        ├── Molecule2
        ├── ...
├── newmolecules_from_mz
        ├── Prediction_...
        ├── Prediction_...
        ├── ...
├── pathmodel_output.svgmolecules contains the structures of each molecules in the input data file.newmolecules_from_mz contains the structures of inferred molecules using the MZ. It will be empty if no MZ were given or if no molecules were inferred.pathmodel_output.svg shows the pathway containing the molecules and the reactions (in green) from the input files and the newly inferred molecules and reactions (in blue).TutorialTutorial on fictitious dataInput dataFor this tutorial, we have created fictitious data available attest/pathmodel_test_data.lp.In this file there is 5 molecules:atom(“molecule_1”,1..4,carb).
bond(“molecule_1”,single,1,2).
bond(“molecule_1”,single,1,3).
bond(“molecule_1”,single,2,3).
bond(“molecule_1”,single,2,4).atom(“molecule_2”,1..4,carb).
bond(“molecule_2”,single,1,2).
bond(“molecule_2”,single,1,3).
bond(“molecule_2”,single,2,3).
bond(“molecule_2”,double,2,4).atom(“molecule_3”,1..6,carb).
bond(“molecule_3”,single,1,2).
bond(“molecule_3”,single,1,3).
bond(“molecule_3”,single,1,6).
bond(“molecule_3”,single,2,3).
bond(“molecule_3”,single,2,4).
bond(“molecule_3”,single,3,6).
bond(“molecule_3”,single,5,6).atom(“molecule_4”,1..6,carb).
bond(“molecule_4”,single,1,2).
bond(“molecule_4”,single,1,3).
bond(“molecule_4”,single,1,6).
bond(“molecule_4”,single,2,3).
bond(“molecule_4”,double,2,4).
bond(“molecule_4”,single,3,6).
bond(“molecule_4”,single,5,6).atom(“molecule_5”,1..7,carb).
bond(“molecule_5”,single,1,2).
bond(“molecule_5”,single,1,3).
bond(“molecule_5”,single,1,6).
bond(“molecule_5”,single,1,7).
bond(“molecule_5”,single,2,3).
bond(“molecule_5”,single,2,4).
bond(“molecule_5”,double,3,6).
bond(“molecule_5”,single,5,6).One reaction:reaction(reduction, “molecule_1”, “molecule_2”).One known MZ:92,1341 (so 921341 for Clingo)mzfiltering(921341).Commandspathmodelinfer-ipathmodel_test_data.lp-ooutput_folderpathmodel_plot-ioutput_folderResultsBy calling the command:pathmodelinfer-ipathmodel_test_data.lp-ooutput_folderPathmodel will create output files:output_folder
├── data_pathmodel.lp
├── pathmodel_data_transformations.tsv
├── pathmodel_incremental_inference.tsv
├── pathmodel_output.lpAs explained inOutput, data_pathmodel.lp is an intermediary file for Pathmodel.pathmodel_data_transformations.tsv contains the transformation inferred from the knonw reactions, here:reaction_idreactant_substructureproduct_substructurereduction[(‘single’, ‘2’, ‘4’)][(‘double’, ‘2’, ‘4’)]This means that the reduction transforms a single bond between atoms 2 and 4 into a double bond. These transformations are used by the deductive and analogical reasoning of PathModel.pathmodel_incremental_inference.tsv shows the new reactions inferred by PathModel and the step in Clingo incremental mode when the new reaction has been inferred.infer_turnnew_reactionreactantproduct2reduction“molecule_3”“molecule_4”2reduction“molecule_5”“Prediction_921341_reduction”Two new reduction variant reactions have been inferred at step two of incremenetal mode:one between Molecule3 and Molecule4 inferred from the reduction between Molecule1 and Molecule2. This is a demonstration of the deductive reasoning of PathModel:one between Molecule5 and a newly inferred metabolite with the MZ of 92,1341. To find this, PathModel computes the MZ of Molecule5 (94,1489). Then it applies each transformations from its knowledge database (here reduction) to each molecules from the knowledge database. With this, PathModel computes the MZ of hypothetical molecules and compared them to the MZ given by the user (here 92,1341). And if a match is found then the reaction and the molecule are inferred. This is an example of the analogical reasoning:Then it is possible to have access to graphic representations of molecules and reactions:pathmodel_plot-ioutput_folderoutput_folder
├── ...
├── molecules
        ├── molecule_1.svg
        ├── molecule_2.svg
        ├── molecule_3.svg
        ├── molecule_4.svg
        ├── molecule_5.svg
├── newmolecules_from_mz
        ├── Prediction_921341_reduction.svg
├── pathmodel_output.svgThere is a structure inferred by PathModel for the MZ 92.1341:PathModel creates also a picture showing all the reactions (known reactions in green, inferred reaction variant in blue and blue square for predicted molecules):Tutorial oniScienceArticle data (Chondrus crispussterol and Mycosporine-like Amino Acids pathways)PathModel contains script to reproduce the experience run in the article: analysis ofChondrus crispussterol and Mycosporine-like Amino Acids (MAA) pathways.Article dataSterol pathwayInput data for sterol pathway are inpathmodel/pathmodel/data/sterol_pwy.lp.For this pathway, known reactions were extracted from:MetaCyc cholesterol biosynthesis (plants) PWY18C3-1.MetaCyc cholesterol biosynthesis III (via desmosterol) PWY66-4.MetaCyc phytosterol biosynthesis (plants) PWY-2541.simplification of multistep C24-C29 demethylation fromSonawane et al. (2016).The source molecule is the cycloartenol and the goal molecules are: 22-dehydrocholesterol, brassicasterol and sitosterol.MAA pathwayInput data for MAA pathway are inpathmodel/pathmodel/data/MAA_pwy.lp.For this pathway, known reactions were extracted from:MetaCyc shinorine biosynthesis PWY-7751.Extended reaction from serine to threonine as proposed inBrawley et al. (2017).Reactions hypothesized byCarreto and Carignan (2011).Two unknown M/Z ratios were given as input for MAA pathway:270,2720302,3117The source molecule is the sedoheptulose-7-phosphate and the goal molecule is the palythine.Article commandsArticle data are stored in PathModel code. By calling the ‘test’ command, you can reproduce PathModel article experience. First run the inference on the sterol and MAA pathways:pathmodeltest-ooutput_folderThen, it is possible to create pictures representation of the results:pathmodel_plot-ioutput_folder/sterolpathmodel_plot-ioutput_folder/MAAArticle resultsThis prototype has been used to analyse to pathways from the red algaChondrus crispus, the sterol and the Mycopsorine-like Amino-Acids.pathmodeltest-ooutput_folderThis test command will create an output folder containing the inference results for the sterol and the MAA pathways:output_folder
├── MAA
    ├── data_pathmodel.lp
    ├── pathmodel_data_transformations.tsv
    ├── pathmodel_incremental_inference.tsv
    ├── pathmodel_output.lp
├── sterol
    ├── data_pathmodel.lp
    ├── pathmodel_data_transformations.tsv
    ├── pathmodel_incremental_inference.tsv
    ├── pathmodel_output.lpSterol pathway resultsThen you can create pictures representation of the results (pathways and molecules) for the sterol pathway:pathmodel_plot-ioutput_folder/steroloutput_folder
├── sterol
    ├── data_pathmodel.lp
    ├── pathmodel_data_transformations.tsv
    ├── pathmodel_incremental_inference.tsv
    ├── pathmodel_output.lp
    ├── pathmodel_output.svg
    ├── molecules
        ├── 22-dehydrocholesterol.svg
        ├── 24-epicampesterol.svg
        ├── 24-ethylidenelophenol.svg
        ├── 24-methyldesmosterol.svg
        ├── 24-methylenecholesterol.svg
        ├── 24-methylenecycloartanol.svg
        ├── 24-methylenelophenol.svg
        ├── 31-norcycloartanol.svg
        ├── 31-norcycloartenol.svg
        ├── 4α,14α-dimethyl-cholesta-8-enol.svg
        ├── 4α,14α-dimethylcholest-8,24-dien-3β-ol.svg
        ├── 4α-methyl-5α-cholest-7-en-3β-ol.svg
        ├── 4α-methyl-5α-cholesta-7,24-dienol.svg
        ├── 4α-methyl-5α-cholesta-8-en-3-ol.svg
        ├── 4α-methyl-cholesta-8,14-dienol.svg
        ├── 4α-methylcholest-8(9),14,24-trien-3β-ol.svg
        ├── 4α-methylzymosterol.svg
        ├── 5α-cholesta-7,24-dienol.svg
        ├── 7-dehydrocholesterol.svg
        ├── 7-dehydrodesmosterol.svg
        ├── brassicasterol.svg
        ├── campesterol.svg
        ├── cholesterol.svg
        ├── cycloartanol.svg
        ├── cycloartenol.svg
        ├── desmosterol.svg
        ├── lathosterol.svg
        ├── sitosterol.svg
        ├── stigmasterol.svg
    ├── newmolecules_from_mz
        (empty)In the molecules folder, each input molecules are represented as a svg file.No M/Z ratio were given as input for the sterol so there is no new molecules from M/Z.‘pathmodel_output.svg’ shows the predicted reactions in blue and the predicted molecules in blue (the picture form can change but it contains the same result):Inferred reactions are listed in ‘pathmodel_incremental_inference.tsv’, with the step of the incremental mode from the source molecule (cycloartenol) to the goal molecules:infer_stepnew_reactionreactantproduct2c24_c29_demethylation“cycloartenol”“31-norcycloartenol”2rxn66_28“cycloartenol”“cycloartanol”3rxn_4282“31-norcycloartenol”“31-norcycloartanol”3rxn_20436“31-norcycloartenol”“4α,14α-dimethylcholest-8,24-dien-3β-ol”4rxn_4282“4α,14α-dimethylcholest-8,24-dien-3β-ol”“4α,14α-dimethyl-cholesta-8-enol”4rxn_20438“4α,14α-dimethylcholest-8,24-dien-3β-ol”“4α-methylcholest-8(9),14,24-trien-3β-ol”5rxn_4282“4α-methylcholest-8(9),14,24-trien-3β-ol”“4α-methyl-cholesta-8,14-dienol”5rxn_20439“4α-methylcholest-8(9),14,24-trien-3β-ol”“4α-methylzymosterol”6rxn_4286“4α-methylzymosterol”“4α-methyl-5α-cholesta-7,24-dienol”6rxn_4282“4α-methylzymosterol”“4α-methyl-5α-cholesta-8-en-3-ol”7rxn_4282“4α-methyl-5α-cholesta-7,24-dienol”“4α-methyl-5α-cholest-7-en-3β-ol”7c24_c28_demethylation“4α-methyl-5α-cholesta-7,24-dienol”“5α-cholesta-7,24-dienol”8rxn_1_14_21_6“5α-cholesta-7,24-dienol”“7-dehydrodesmosterol”8rxn_4282“5α-cholesta-7,24-dienol”“lathosterol”9rxn_4282“7-dehydrodesmosterol”“7-dehydrocholesterol”9rxn66_323“7-dehydrodesmosterol”“desmosterol”10rxn_4021“desmosterol”“24-methylenecholesterol”10rxn_4282“desmosterol”“cholesterol”11c22_desaturation“cholesterol”“22-dehydrocholesterol”12rxn_2_1_1_143“campesterol”“sitosterol”MAA pathway resultsAnd the pictures for the MAA pathway are created with:pathmodel_plot-ioutput_folder/MAAoutput_folder
├── MAA
    ├── data_pathmodel.lp
    ├── pathmodel_data_transformations.tsv
    ├── pathmodel_incremental_inference.tsv
    ├── pathmodel_output.lp
    ├── pathmodel_output.svg
    ├── molecules
        ├── asterina-330.svg
        ├── mycosporin-glycine.svg
        ├── palythene.svg
        ├── palythine.svg
        ├── palythinol.svg
        ├── porphyra-334.svg
        ├── R-4-deoxygadusol.svg
        ├── R-demethyl-4-deoxygadusol.svg
        ├── S-4-deoxygadusol.svg
        ├── S-demethyl-4-deoxygadusol.svg
        ├── sedoheptulose-7-phosphate.svg
        ├── shinorine.svg
        ├── z-palythenic acid.svg
    ├── newmolecules_from_mz
        ├── Prediction_2702720_dehydration.svg
        ├── Prediction_3023117_decarboxylation_1.svg
        ├── Prediction_3023117_decarboxylation_2.svgpathmodel_output.svg contains the pathway with the known reactions (green), the reactions inferred by PathModel (blue) and the metabolites inferred (blue).Inferred reactions are listed in ‘pathmodel_incremental_inference.tsv’, with the step of the incremental mode from the source molecule (sedoheptulose-7-phosphate) to the goal molecule (palythine).Incremental step 2 is not showed because it is already known (between ‘sedoheptulose-7-phosphate’ and ‘R-demethyl-4-deoxygadusol’) and no new predictions have been inferred.infer_stepnew_reactionreactantproduct3rxn_17896“R-demethyl-4-deoxygadusol”“R-4-deoxygadusol”3rxn_17370“R-demethyl-4-deoxygadusol”“S-demethyl-4-deoxygadusol”4rxn_17895“R-4-deoxygadusol”“S-4-deoxygadusol”4rxn_17366“S-demethyl-4-deoxygadusol”“S-4-deoxygadusol”7dehydration“Prediction_3023117_decarboxylation_1”“palythene”7dehydration“Prediction_3023117_decarboxylation_2”“palythene”7decarboxylation_2“porphyra-334”“Prediction_3023117_decarboxylation_1”7decarboxylation_2“porphyra-334”“Prediction_3023117_decarboxylation_2”7decarboxylation_1“shinorine”“asterina-330”8dehydration“asterina-330”“Prediction_2702720_dehydration”The structures of the predicted molecules from M/Z can be found in newmolecules_from_mz:Prediction_2702720_dehydration corresponds to MAA1 of the article:Prediction_2702720_dehydrationfrom reaction(dehydration,”porphyra-334”,”z-palythenic acid”).predictatom(“Prediction_2702720_dehydration”,1,carb).
predictatom(“Prediction_2702720_dehydration”,2,carb).
predictatom(“Prediction_2702720_dehydration”,3,carb).
predictatom(“Prediction_2702720_dehydration”,4,carb).
predictatom(“Prediction_2702720_dehydration”,5,carb).
predictatom(“Prediction_2702720_dehydration”,6,carb).
predictatom(“Prediction_2702720_dehydration”,7,carb).
predictatom(“Prediction_2702720_dehydration”,8,nitr).
predictatom(“Prediction_2702720_dehydration”,9,oxyg).
predictatom(“Prediction_2702720_dehydration”,10,nitr).
predictatom(“Prediction_2702720_dehydration”,11,oxyg).
predictatom(“Prediction_2702720_dehydration”,12,oxyg).
predictatom(“Prediction_2702720_dehydration”,13,carb).
predictatom(“Prediction_2702720_dehydration”,14,carb).
predictatom(“Prediction_2702720_dehydration”,15,carb).
predictatom(“Prediction_2702720_dehydration”,16,oxyg).
predictatom(“Prediction_2702720_dehydration”,17,oxyg).
predictatom(“Prediction_2702720_dehydration”,18,carb).
predictatom(“Prediction_2702720_dehydration”,19,carb).predictbond(“Prediction_2702720_dehydration”,double,1,2).
predictbond(“Prediction_2702720_dehydration”,single,1,6).
predictbond(“Prediction_2702720_dehydration”,single,1,8).
predictbond(“Prediction_2702720_dehydration”,single,2,3).
predictbond(“Prediction_2702720_dehydration”,single,2,9).
predictbond(“Prediction_2702720_dehydration”,single,3,4).
predictbond(“Prediction_2702720_dehydration”,double,3,10).
predictbond(“Prediction_2702720_dehydration”,single,4,5).
predictbond(“Prediction_2702720_dehydration”,single,5,6).
predictbond(“Prediction_2702720_dehydration”,single,5,7).
predictbond(“Prediction_2702720_dehydration”,singleS,5,12).
predictbond(“Prediction_2702720_dehydration”,single,7,11).
predictbond(“Prediction_2702720_dehydration”,single,8,14).
predictbond(“Prediction_2702720_dehydration”,single,9,13).
predictbond(“Prediction_2702720_dehydration”,single,10,18).
predictbond(“Prediction_2702720_dehydration”,single,14,15).
predictbond(“Prediction_2702720_dehydration”,single,15,17).
predictbond(“Prediction_2702720_dehydration”,double,15,16).
predictbond(“Prediction_2702720_dehydration”,double,18,19).Prediction_3023117_decarboxylation_1 and Prediction_3023117_decarboxylation_2 (which are the same molecule) correspond to MAA2.This molecule has been identified as the Aplysiapalythine A found inAplysia californica[Kamio2011].
Furthermore, Aplysiapalythine A has been detected in red algae (the group in whichChondrus crispusis classified)[Orfanoudaki2019].Prediction_3023117_decarboxylation_1Prediction_3023117_decarboxylation_2from reaction(decarboxylation_1,”z-palythenic acid”,”palythene”).from reaction(decarboxylation_2,”shinorine”,”asterina-330”).predictatom(“Prediction_3023117_decarboxylation_1”,1,carb).
predictatom(“Prediction_3023117_decarboxylation_1”,2,carb).
predictatom(“Prediction_3023117_decarboxylation_1”,3,carb).
predictatom(“Prediction_3023117_decarboxylation_1”,4,carb).
predictatom(“Prediction_3023117_decarboxylation_1”,5,carb).
predictatom(“Prediction_3023117_decarboxylation_1”,6,carb).
predictatom(“Prediction_3023117_decarboxylation_1”,7,carb).
predictatom(“Prediction_3023117_decarboxylation_1”,8,nitr).
predictatom(“Prediction_3023117_decarboxylation_1”,9,oxyg).
predictatom(“Prediction_3023117_decarboxylation_1”,10,nitr).
predictatom(“Prediction_3023117_decarboxylation_1”,11,oxyg).
predictatom(“Prediction_3023117_decarboxylation_1”,12,oxyg).
predictatom(“Prediction_3023117_decarboxylation_1”,13,carb).
predictatom(“Prediction_3023117_decarboxylation_1”,14,carb).
predictatom(“Prediction_3023117_decarboxylation_1”,15,carb).
predictatom(“Prediction_3023117_decarboxylation_1”,16,oxyg).
predictatom(“Prediction_3023117_decarboxylation_1”,17,oxyg).
predictatom(“Prediction_3023117_decarboxylation_1”,18,carb).
predictatom(“Prediction_3023117_decarboxylation_1”,19,carb).
predictatom(“Prediction_3023117_decarboxylation_1”,20,oxyg).
predictatom(“Prediction_3023117_decarboxylation_1”,24,carb).predictbond(“Prediction_3023117_decarboxylation_1”,double,1,2).
predictbond(“Prediction_3023117_decarboxylation_1”,single,1,6).
predictbond(“Prediction_3023117_decarboxylation_1”,single,1,8).
predictbond(“Prediction_3023117_decarboxylation_1”,single,2,3).
predictbond(“Prediction_3023117_decarboxylation_1”,single,2,9).
predictbond(“Prediction_3023117_decarboxylation_1”,single,3,4).
predictbond(“Prediction_3023117_decarboxylation_1”,double,3,10).
predictbond(“Prediction_3023117_decarboxylation_1”,single,4,5).
predictbond(“Prediction_3023117_decarboxylation_1”,single,5,6).
predictbond(“Prediction_3023117_decarboxylation_1”,single,5,7).
predictbond(“Prediction_3023117_decarboxylation_1”,singleS,5,12).
predictbond(“Prediction_3023117_decarboxylation_1”,single,7,11).
predictbond(“Prediction_3023117_decarboxylation_1”,single,8,14).
predictbond(“Prediction_3023117_decarboxylation_1”,single,9,13).
predictbond(“Prediction_3023117_decarboxylation_1”,single,10,18).
predictbond(“Prediction_3023117_decarboxylation_1”,single,14,15).
predictbond(“Prediction_3023117_decarboxylation_1”,double,15,16).
predictbond(“Prediction_3023117_decarboxylation_1”,single,15,17).
predictbond(“Prediction_3023117_decarboxylation_1”,single,18,19).
predictbond(“Prediction_3023117_decarboxylation_1”,single,19,20).
predictbond(“Prediction_3023117_decarboxylation_1”,single,19,24).predictatom(“Prediction_3023117_decarboxylation_2”,1,carb).
predictatom(“Prediction_3023117_decarboxylation_2”,2,carb).
predictatom(“Prediction_3023117_decarboxylation_2”,3,carb).
predictatom(“Prediction_3023117_decarboxylation_2”,4,carb).
predictatom(“Prediction_3023117_decarboxylation_2”,5,carb).
predictatom(“Prediction_3023117_decarboxylation_2”,6,carb).
predictatom(“Prediction_3023117_decarboxylation_2”,7,carb).
predictatom(“Prediction_3023117_decarboxylation_2”,8,nitr).
predictatom(“Prediction_3023117_decarboxylation_2”,9,oxyg).
predictatom(“Prediction_3023117_decarboxylation_2”,10,nitr).
predictatom(“Prediction_3023117_decarboxylation_2”,11,oxyg).
predictatom(“Prediction_3023117_decarboxylation_2”,12,oxyg).
predictatom(“Prediction_3023117_decarboxylation_2”,13,carb).
predictatom(“Prediction_3023117_decarboxylation_2”,14,carb).
predictatom(“Prediction_3023117_decarboxylation_2”,15,carb).
predictatom(“Prediction_3023117_decarboxylation_2”,16,oxyg).
predictatom(“Prediction_3023117_decarboxylation_2”,17,oxyg).
predictatom(“Prediction_3023117_decarboxylation_2”,18,carb).
predictatom(“Prediction_3023117_decarboxylation_2”,19,carb).
predictatom(“Prediction_3023117_decarboxylation_2”,20,oxyg).
predictatom(“Prediction_3023117_decarboxylation_2”,24,carb).predictbond(“Prediction_3023117_decarboxylation_2”,double,1,2).
predictbond(“Prediction_3023117_decarboxylation_2”,single,1,6).
predictbond(“Prediction_3023117_decarboxylation_2”,single,1,8).
predictbond(“Prediction_3023117_decarboxylation_2”,single,2,3).
predictbond(“Prediction_3023117_decarboxylation_2”,single,2,9).
predictbond(“Prediction_3023117_decarboxylation_2”,single,3,4).
predictbond(“Prediction_3023117_decarboxylation_2”,double,3,10).
predictbond(“Prediction_3023117_decarboxylation_2”,single,4,5).
predictbond(“Prediction_3023117_decarboxylation_2”,single,5,6).
predictbond(“Prediction_3023117_decarboxylation_2”,single,5,7).
predictbond(“Prediction_3023117_decarboxylation_2”,singleS,5,12).
predictbond(“Prediction_3023117_decarboxylation_2”,single,7,11).
predictbond(“Prediction_3023117_decarboxylation_2”,single,8,14).
predictbond(“Prediction_3023117_decarboxylation_2”,single,9,13).
predictbond(“Prediction_3023117_decarboxylation_2”,single,10,18).
predictbond(“Prediction_3023117_decarboxylation_2”,single,14,15).
predictbond(“Prediction_3023117_decarboxylation_2”,double,15,16).
predictbond(“Prediction_3023117_decarboxylation_2”,single,15,17).
predictbond(“Prediction_3023117_decarboxylation_2”,single,18,19).
predictbond(“Prediction_3023117_decarboxylation_2”,single,19,20).
predictbond(“Prediction_3023117_decarboxylation_2”,single,19,24).DataIn the GitHub repository (pathmodel/pathmodel/data/), there are 4 data files:MAA_pwy.lp: Mycosporine Amino-Acids Like pathways according to data fromChondrus crispus(Belcour et al, 2020).sterol_pwy.lp: Sterol pathways according to data fromChondrus crispus(Belcour et al, 2020).brown_sterols_pwy.lp: Sterol pathways in Brown algae (Girard et al., 2021).mozukulins_pwy.lp: Mozukulins and sterol pathway in the brown algaCladosiphon okamuranus(Girard et al., 2021).CitationArnaud Belcour, Jean Girard, Méziane Aite, Ludovic Delage, Camille Trottier, Charlotte Marteau, Cédric Leroux, Simon M. Dittami, Pierre Sauleau, Erwan Corre, Jacques Nicolas, Catherine Boyen, Catherine Leblanc, Jonas Collén, Anne Siegel, Gabriel V. Markov. (2020). Inferring biochemical reactions and metabolite structures to understand metabolic pathway drift,iScience, 2020, 23(2): 100849,https://doi.org/10.1016/j.isci.2020.100849.Citation in BibTeX format.Bibliography[Kamio2011]Kamio, M., Kicklighter, C.E., Nguyen, L., Germann, M.W. and Derby, C.D. (2011). Isolation and Structural Elucidation of Novel Mycosporine‐Like Amino Acids as Alarm Cues in the Defensive Ink Secretion of the Sea HareAplysia californica.Helvetica Chimica Acta, 94: 1012-1018.doi:10.1002/hlca.201100117.[Orfanoudaki2019]Orfanoudaki, M., Hartmann, A., Karsten, U. and Ganzera, M. (2019). Chemical profiling of mycosporine‐like amino acids in twenty‐three red algal species.Journal of Phycology, 55: 393-403.doi:10.1111/jpy.12827.