Literature DB >> 30304379

PrecursorFinder: a customized biosynthetic precursor explorer.

Le Yuan1,2,3, Yu Tian1,3, Shaozhen Ding2, Yanfang Liu1,3, Fu Chen1,4, Tong Zhang1,4, Weizhong Tu5, Junni Chen5, Qian-Nan Hu2.   

Abstract

SUMMARY: Synthetic biology has a great potential to produce high value pharmaceuticals, commodities or bulk chemicals. However, many biosynthetic target molecules have no defined or predicted biosynthetic pathways. Biosynthetic precursors are crucial to create biosynthetic pathways. Thus computer-assisted tools for precursor identification are urgently needed to develop novel metabolic pathways. To this end, we present PrecursorFinder, a computational tool that explores biosynthetic precursors for the query target molecules using chemical structure, similarity as well as MCS (maximum common substructure). This platform comprises more than 60 000 compounds biosynthesized for being promising precursors, which are extracted from >500 000 scientific literatures and manually curated by more than 100 people over the past 8 years. The PrecursorFinder could speed up the process of biosynthesis research and make synthetic biology or metabolic engineering more efficient.
AVAILABILITY AND IMPLEMENTATION: PrecursorFinder is available at: http://www.rxnfinder.org/precursorfinder/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
© The Author(s) 2018. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

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Year:  2019        PMID: 30304379     DOI: 10.1093/bioinformatics/bty838

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  3 in total

1.  novoPathFinder: a webserver of designing novel-pathway with integrating GEM-model.

Authors:  Shaozhen Ding; Yu Tian; Pengli Cai; Dachuan Zhang; Xingxiang Cheng; Dandan Sun; Le Yuan; Junni Chen; Weizhong Tu; Dong-Qing Wei; Qian-Nan Hu
Journal:  Nucleic Acids Res       Date:  2020-07-02       Impact factor: 16.971

2.  Deep learning driven biosynthetic pathways navigation for natural products with BioNavi-NP.

Authors:  Shuangjia Zheng; Tao Zeng; Chengtao Li; Binghong Chen; Connor W Coley; Yuedong Yang; Ruibo Wu
Journal:  Nat Commun       Date:  2022-06-10       Impact factor: 17.694

3.  Metabolic disassembler for understanding and predicting the biosynthetic units of natural products.

Authors:  Kohei Amano; Tsubasa Matsumoto; Kenichi Tanaka; Kimito Funatsu; Masaaki Kotera
Journal:  BMC Bioinformatics       Date:  2019-12-23       Impact factor: 3.169

  3 in total

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