ArticleBioMed research international2016
Sequence- and Structure-Based Functional Annotation and Assessment of Metabolic Transporters in Aspergillus oryzae: A Representative Case Study.
Article in BioMed research international, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Teasing out missing reactions in genome-scale metabolic networks through hypergraph learning.Nature communications · 2023Article
- Dynamics of microbial populations mediating biogeochemical cycling in a freshwater lake.Microbiome · 2018Article
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Authors and funding
4 authors.
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Abstract
Aspergillus oryzae is widely used for the industrial production of enzymes. In A. oryzae metabolism, transporters appear to play crucial roles in controlling the flux of molecules for energy generation, nutrients delivery, and waste elimination in the cell. While the A. oryzae genome sequence is available, transporter annotation remains limited and thus the connectivity of metabolic networks is incomplete. In this study, we developed a metabolic annotation strategy to understand the relationship between the sequence, structure, and function for annotation of A. oryzae metabolic transporters. Sequence-based analysis with manual curation showed that 58 genes of 12,096 total genes in the A. oryzae genome encoded metabolic transporters. Under consensus integrative databases, 55 unambiguous metabolic transporter genes were distributed into channels and pores (7 genes), electrochemical potential-driven transporters (33 genes), and primary active transporters (15 genes). To reveal the transporter functional role, a combination of homology modeling and molecular dynamics simulation was implemented to assess the relationship between sequence to structure and structure to function. As in the energy metabolism of A. oryzae, the H(+)-ATPase encoded by the AO090005000842 gene was selected as a representative case study of multilevel linkage annotation. Our developed strategy can be used for enhancing metabolic network reconstruction.
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