Bioinformatic Analysis Reveals both Oversampled and Underexplored Biosynthetic Diversity in Nonribosomal Peptides

The traditional natural product discovery approach has
accessed
only a fraction of the chemical diversity in nature. The use of bioinformatic
tools to interpret the instructions encoded in microbial biosynthetic
genes has the potential to circumvent the existing methodological
bottlenecks and greatly expand the scope of discovery. Structural
prediction algorithms for nonribosomal peptides (NRPs), the largest
family of microbial natural products, lie at the heart of this new
approach. To understand the scope and limitation of the existing prediction
algorithms, we evaluated their performances on NRP synthetase biosynthetic
gene clusters. Our systematic analysis shows that the NRP biosynthetic
landscape is uneven. Phenylglycine and its derivatives as a group
of NRP building blocks (BBs), for example, have been oversampled,
reflecting an extensive historical interest in the glycopeptide antibiotics
family. In contrast, the benzoyl BB, including 2,3-dihydroxybenzoate
(DHB), has been the most underexplored, hinting at the possibility
of a reservoir of as yet unknown DHB containing NRPs with functional
roles other than a siderophore. Our results also suggest that there
is still vast unexplored biosynthetic diversity in nature, and the
analysis presented herein shall help guide and strategize future natural
product discovery campaigns. We also discuss possible ways bioinformaticians
and biochemists could work together to improve the existing prediction
algorithms.