Computational discovery of specificity-conferring sites in non-ribosomal peptide synthetases

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Computational discovery of specificity-conferring sites in non-ribosomal peptide synthetases. / Knudsen, Michael; Søndergaard, Dan Ariel; Tofting-Olesen, Claus; Hansen, Frederik Teilfeldt; Brodersen, Ditlev Egeskov; Pedersen, Christian Storm.

In: Bioinformatics, Vol. 32, No. 3, 2016, p. 325-329.

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@article{ebc7bc91365948b0b12034c78536b958,
title = "Computational discovery of specificity-conferring sites in non-ribosomal peptide synthetases",
abstract = "Motivation: By using a class of large modular enzymes known as Non-Ribosomal Peptide Synthetases (NRPS), bacteria and fungi are capable of synthesizing a large variety of secondary metabolites, many of which are bioactive and have potential, pharmaceutical applications as e.g.~antibiotics. There is thus an interest in predicting the compound synthesized by an NRPS from its primary structure (amino acid sequence) alone, as this would enable an in silico search of whole genomes for NRPS enzymes capable of synthesizing potentially useful compounds.Results: NRPS synthesis happens in a conveyor belt like fashion where each individual NRPS module is responsible for incorporating a specific substrate (typically an amino acid) into the final product. Here, we present a new method for predicting substrate specificities of individual NRPS modules based on occurrences of motifs in their primary strucutres. We compare our classifier to existing methods and discuss possible biological explanations of how the motifs might relate to substrate specificity.Availability: SEQL-NRPS is available as a web service implemented in Python with Flask at http://services.birc.au.dk/seql-nrps and source code available at https://bitbucket.org/dansondergaard/seql-nrps/.",
author = "Michael Knudsen and S{\o}ndergaard, {Dan Ariel} and Claus Tofting-Olesen and Hansen, {Frederik Teilfeldt} and Brodersen, {Ditlev Egeskov} and Pedersen, {Christian Storm}",
year = "2016",
doi = "10.1093/bioinformatics/btv600",
language = "English",
volume = "32",
pages = "325--329",
journal = "Bioinformatics",
issn = "1367-4803",
publisher = "Oxford University Press",
number = "3",

}

RIS

TY - JOUR

T1 - Computational discovery of specificity-conferring sites in non-ribosomal peptide synthetases

AU - Knudsen, Michael

AU - Søndergaard, Dan Ariel

AU - Tofting-Olesen, Claus

AU - Hansen, Frederik Teilfeldt

AU - Brodersen, Ditlev Egeskov

AU - Pedersen, Christian Storm

PY - 2016

Y1 - 2016

N2 - Motivation: By using a class of large modular enzymes known as Non-Ribosomal Peptide Synthetases (NRPS), bacteria and fungi are capable of synthesizing a large variety of secondary metabolites, many of which are bioactive and have potential, pharmaceutical applications as e.g.~antibiotics. There is thus an interest in predicting the compound synthesized by an NRPS from its primary structure (amino acid sequence) alone, as this would enable an in silico search of whole genomes for NRPS enzymes capable of synthesizing potentially useful compounds.Results: NRPS synthesis happens in a conveyor belt like fashion where each individual NRPS module is responsible for incorporating a specific substrate (typically an amino acid) into the final product. Here, we present a new method for predicting substrate specificities of individual NRPS modules based on occurrences of motifs in their primary strucutres. We compare our classifier to existing methods and discuss possible biological explanations of how the motifs might relate to substrate specificity.Availability: SEQL-NRPS is available as a web service implemented in Python with Flask at http://services.birc.au.dk/seql-nrps and source code available at https://bitbucket.org/dansondergaard/seql-nrps/.

AB - Motivation: By using a class of large modular enzymes known as Non-Ribosomal Peptide Synthetases (NRPS), bacteria and fungi are capable of synthesizing a large variety of secondary metabolites, many of which are bioactive and have potential, pharmaceutical applications as e.g.~antibiotics. There is thus an interest in predicting the compound synthesized by an NRPS from its primary structure (amino acid sequence) alone, as this would enable an in silico search of whole genomes for NRPS enzymes capable of synthesizing potentially useful compounds.Results: NRPS synthesis happens in a conveyor belt like fashion where each individual NRPS module is responsible for incorporating a specific substrate (typically an amino acid) into the final product. Here, we present a new method for predicting substrate specificities of individual NRPS modules based on occurrences of motifs in their primary strucutres. We compare our classifier to existing methods and discuss possible biological explanations of how the motifs might relate to substrate specificity.Availability: SEQL-NRPS is available as a web service implemented in Python with Flask at http://services.birc.au.dk/seql-nrps and source code available at https://bitbucket.org/dansondergaard/seql-nrps/.

U2 - 10.1093/bioinformatics/btv600

DO - 10.1093/bioinformatics/btv600

M3 - Journal article

C2 - 26471456

VL - 32

SP - 325

EP - 329

JO - Bioinformatics

JF - Bioinformatics

SN - 1367-4803

IS - 3

ER -