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10.3 Bioinformatics Analysis in Natural Product Discovery
Including Non-ribosomal Peptides
Natural products (NPs) have always been a subject of interest for research and
development of bioactive molecules. Genome sequencing projects of fungi at large
scales uncovered a huge realm of NPs useful for human use (Schueffler and Anke
2014). The continuously increasing load of fungal genomic data has incited the
discovery of new NPs through identification of biosynthetic gene clusters (BGCs)
that act as the source of these NPs. Such genes from which the fungal NPs are
sourced include polyketide synthases and peptide synthetases. Fungal siderophores
are secondary metabolites constructed from non-ribosomal peptide (NRP) biosynthetic machinery with the help of enzymes called non-ribosomal peptide synthetases (NRPS). These NRPSs are huge enzymes, generally multi-modular in nature
that catalyse the peptide bond formation without the involvement of ribosomes
(Haas et al. 2008).
Still, the number of such genes that produce them surpasses the number of known
NP compounds so far, thus revealing a disproportion even in the current genomic
era. Hence, it makes sense to further exploit the genomic data for mining the probable pathways to characterize novel NPs. (Stadler and Hoffmeister 2015). One of
the major reasons for the disparity between the available genomic data and uncharacterized NPs including siderophores is attributed to the fact that the BGCs are
either silenced at the transcriptional level or expressed only at low levels under laboratory conditions. The regulatory systems of BGCs function in such a way that they
require specific environmental cues to be activated. Surpassing these challenges of
the pre-genomic era, synthetic biology strategies are currently used for the activation of target BGCs followed by structural characterization of the NPs. However, it
is imperative that the silent BGCs are correctly identified before these strategies. A
typical workflow of modern NP discovery is initiated with the identification of silent
BGCs using bioinformatics tools (Ren et al. 2017; Rutledge and Challis 2015;
Zarin-Tutt et al. 2016). The major techniques devised and the recent developments
in research for the in silico–based omics analyses of fungal siderophores are
described below.
10.3.1 Genome Mining: Identification of BGCs
The catalytic domains of BGCs are highly conserved like the operons among bacteria. This supports the mining of genome sequences for identification of putative
BGCs using homology searches using alignment techniques or Hidden Markov
Models (HMMs) (Weber and Kim 2016). Once a matching domain is identified by
similarity search, bioinformatic analysis of the neighbouring genes can then predict
putative gene clusters (Rondon et al. 2004). Table 10.1 depicts the different web
servers/tools and databases for BGC/NP discovery.
10 Bioinformatics Applications in Fungal Siderophores: Omics Implications
10.3 Bioinformatics Analysis in Natural Product Discovery
Including Non-ribosomal Peptides
Natural products (NPs) have always been a subject of interest for research and
development of bioactive molecules. Genome sequencing projects of fungi at large
scales uncovered a huge realm of NPs useful for human use (Schueffler and Anke
2014). The continuously increasing load of fungal genomic data has incited the
discovery of new NPs through identification of biosynthetic gene clusters (BGCs)
that act as the source of these NPs. Such genes from which the fungal NPs are
sourced include polyketide synthases and peptide synthetases. Fungal siderophores
are secondary metabolites constructed from non-ribosomal peptide (NRP) biosynthetic machinery with the help of enzymes called non-ribosomal peptide synthetases (NRPS). These NRPSs are huge enzymes, generally multi-modular in nature
that catalyse the peptide bond formation without the involvement of ribosomes
(Haas et al. 2008).
Still, the number of such genes that produce them surpasses the number of known
NP compounds so far, thus revealing a disproportion even in the current genomic
era. Hence, it makes sense to further exploit the genomic data for mining the probable pathways to characterize novel NPs. (Stadler and Hoffmeister 2015). One of
the major reasons for the disparity between the available genomic data and uncharacterized NPs including siderophores is attributed to the fact that the BGCs are
either silenced at the transcriptional level or expressed only at low levels under laboratory conditions. The regulatory systems of BGCs function in such a way that they
require specific environmental cues to be activated. Surpassing these challenges of
the pre-genomic era, synthetic biology strategies are currently used for the activation of target BGCs followed by structural characterization of the NPs. However, it
is imperative that the silent BGCs are correctly identified before these strategies. A
typical workflow of modern NP discovery is initiated with the identification of silent
BGCs using bioinformatics tools (Ren et al. 2017; Rutledge and Challis 2015;
Zarin-Tutt et al. 2016). The major techniques devised and the recent developments
in research for the in silico–based omics analyses of fungal siderophores are
described below.
10.3.1 Genome Mining: Identification of BGCs
The catalytic domains of BGCs are highly conserved like the operons among bacteria. This supports the mining of genome sequences for identification of putative
BGCs using homology searches using alignment techniques or Hidden Markov
Models (HMMs) (Weber and Kim 2016). Once a matching domain is identified by
similarity search, bioinformatic analysis of the neighbouring genes can then predict
putative gene clusters (Rondon et al. 2004). Table 10.1 depicts the different web
servers/tools and databases for BGC/NP discovery.
10 Bioinformatics Applications in Fungal Siderophores: Omics Implications