Substrate Promiscuity of a Paralytic Shellfish Toxin Amidinotransferase

Lukowski, April L.; Mallik, Leena; Hinze, Meagan E.; Carlson, Brian M.; Ellinwood, Duncan C.; Pyser, Joshua B.; Koutmos, Markos; Narayan, Alison R. H.

doi:10.1021/acschembio.9b00964

Cited by 21 publications

(19 citation statements)

References 28 publications

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“…Although the genes involved in sxt biosynthesis have been found in many cyanobacteria and dinoflagellates, there are few three-dimensional protein models. The crystallographic structure of an amidinotransferase annotated as sxtG in the cyanobacterium Microseira wollei has been recently published [ 83 ], highly different in amino acid number and sequence from the sxtG found in the present work. To our knowledge, these are the first in silico predictions of three-dimensional structures of sxt gene products in dinoflagellates.…”

Section: Resultsmentioning

confidence: 82%

De novo Transcriptome of the Non-saxitoxin Producing Alexandrium tamutum Reveals New Insights on Harmful Dinoflagellates

et al. 2020

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Many dinoflagellates species, especially of the Alexandrium genus, produce a series of toxins with tremendous impacts on human and environmental health, and tourism economies. Alexandrium tamutum was discovered for the first time in the Gulf of Naples, and it is not known to produce saxitoxins. However, a clone of A. tamutum from the same Gulf showed copepod reproduction impairment and antiproliferative activity. In this study, the full transcriptome of the dinoflagellate A. tamutum is presented in both control and phosphate starvation conditions. RNA-seq approach was used for in silico identification of transcripts that can be involved in the synthesis of toxic compounds. Phosphate starvation was selected because it is known to induce toxin production for other Alexandrium spp. Results showed the presence of three transcripts related to saxitoxin synthesis (sxtA, sxtG and sxtU), and others potentially related to the synthesis of additional toxic compounds (e.g., 44 transcripts annotated as “polyketide synthase”). These data suggest that even if this A. tamutum clone does not produce saxitoxins, it has the potential to produce toxic metabolites, in line with the previously observed activity. These data give new insights into toxic microalgae, toxin production and their potential applications for the treatment of human pathologies.

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Section: Resultsmentioning

confidence: 82%

De novo Transcriptome of the Non-saxitoxin Producing Alexandrium tamutum Reveals New Insights on Harmful Dinoflagellates

et al. 2020

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“…Nine recent data sets from the literature were chosen toward this aim, for which both reactants and products were known (opposed to the more prevalent manner of only reporting reactants). Eight studies comprised enzymatic transformations, reporting the activity of different nitrilases, 45 aminedehydrogenases, 46 alcoholdehydrogenases, 47 carboxyl-methyltransferases, 48 transaminases, 49 tryptophansynthases, 50 amidinotransferases, 51 and dehalogenases 52 on diverse sets of substrates. An additional study on seven organic C(sp 2 )–C(sp 3 ) couplings 53 was utilized to showcase the performance of EHreact on nonenzymatic transformations.…”

Section: Results and Discussionmentioning

confidence: 99%

EHreact: Extended Hasse Diagrams for the Extraction and Scoring of Enzymatic Reaction Templates

Heid

Goldman

Sankaranarayanan

et al. 2021

J. Chem. Inf. Model.

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Data-driven computer-aided synthesis planning utilizing organic or biocatalyzed reactions from large databases has gained increasing interest in the last decade, sparking the development of numerous tools to extract, apply, and score general reaction templates. The generation of reaction rules for enzymatic reactions is especially challenging since substrate promiscuity varies between enzymes, causing the optimal levels of rule specificity and optimal number of included atoms to differ between enzymes. This complicates an automated extraction from databases and has promoted the creation of manually curated reaction rule sets. Here, we present EHreact, a purely data-driven open-source software tool, to extract and score reaction rules from sets of reactions known to be catalyzed by an enzyme at appropriate levels of specificity without expert knowledge. EHreact extracts and groups reaction rules into tree-like structures, Hasse diagrams, based on common substructures in the imaginary transition structures. Each diagram can be utilized to output a single or a set of reaction rules, as well as calculate the probability of a new substrate to be processed by the given enzyme by inferring information about the reactive site of the enzyme from the known reactions and their grouping in the template tree. EHreact heuristically predicts the activity of a given enzyme on a new substrate, outperforming current approaches in accuracy and functionality.

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“…For validation of the scoring function, a set of experimental studies on the substrate ranges of various enzymes was extracted from literature manually, [44][45][46][47][48][49][50][51] as well as a study on organic coupling reactions to test the performance of EHreact on organic, non-enzymatic reactions. 52 Each study reported either the yield or activity of an enzyme/catalyst on a specified substrate under reaction conditions consistent throughout each study.…”

Section: E Data Preparationmentioning

confidence: 99%

EHreact: Extended Hasse Diagrams for the Extraction and Scoring of Enzymatic Reaction Templates

Heid

Goldman²,

Sankaranarayanan³

et al. 2021

Preprint

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Data-driven computer-aided synthesis planning utilizing organic or biocatalyzed reactions from large databases has gained increasing interest in the last decade, sparking the development of numerous tools to extract, apply and score general reaction templates. The generation of reaction rules for enzymatic reactions is especially challenging, since substrate promiscuity varies between enzymes, causing the optimal levels of rule specificity and optimal number of included atoms to differ between enzymes. This complicates an automated extraction from databases and has promoted the creation of manually curated reaction rule sets. Here we present EHreact, a purely data-driven open-source software tool to extract and score reaction rules from sets of reactions known to be catalyzed by an enzyme at appropriate levels of specificity without expert knowledge. EHreact extracts and groups reaction rules into tree-like structures, Hasse diagrams, based on common substructures in the imaginary transition structures. Each diagram can be utilized to output a single or a set of reaction rules, as well as calculate the probability of a new substrate to be processed by the given enzyme by inferring information about the reactive site of the enzyme from the known reactions and their grouping in the template tree. EHreact heuristically predicts the activity of a given enzyme on a new substrate, outperforming current approaches in accuracy and functionality.

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Substrate Promiscuity of a Paralytic Shellfish Toxin Amidinotransferase

Cited by 21 publications

References 28 publications

De novo Transcriptome of the Non-saxitoxin Producing Alexandrium tamutum Reveals New Insights on Harmful Dinoflagellates

De novo Transcriptome of the Non-saxitoxin Producing Alexandrium tamutum Reveals New Insights on Harmful Dinoflagellates

EHreact: Extended Hasse Diagrams for the Extraction and Scoring of Enzymatic Reaction Templates

EHreact: Extended Hasse Diagrams for the Extraction and Scoring of Enzymatic Reaction Templates

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