Improving Natural Products Identification through Targeted LC-MS/MS in an Untargeted Secondary Metabolomics Workflow

Tandem mass spectrometry is a widely applied and highly sensitive technique for the discovery and characterization of microbial natural products such as secondary metabolites from myxobacteria. Here, a data mining workflow based on MS/MS precursor lists targeting only signals related to bacterial metabolism is established using LC-MS data of crude extracts from shaking flask fermentations. The devised method is not biased toward specific compound classes or structural features and is capable of increasing the information content of LC-MS/MS analyses by directing fragmentation events to signals of interest. The approach is thus contrary to typical auto-MS² setups where precursor ions are usually selected according to signal intensity, which is regarded as a drawback for metabolite discovery applications when samples contain many overlapping signals and the most intense signals do not necessarily represent compounds of interest. In line with this, the method described here achieves improved MS/MS scan coverage for low-abundance precursor ions not captured by auto-MS² experiments and thereby facilitates the search for new secondary metabolites in complex biological samples. To underpin the effectiveness of the approach, the identification and structure elucidation of two new myxobacterial secondary metabolite classes is reported

Predicting the Presence of Uncommon Elements in Unknown Biomolecules from Isotope Patterns

The determination of the molecular formula is one of the earliest and most important steps when investigating the chemical nature of an unknown compound. Common approaches use the isotopic pattern of a compound measured using mass spectrometry. Computational methods to determine the molecular formula from this isotopic pattern require a fixed set of elements. Considering all possible elements severely increases running times and more importantly the chance for false positive identifications as the number of candidate formulas for a given target mass rises significantly if the constituting elements are not prefiltered. This negative effect grows stronger for compounds of higher molecular mass as the effect of a single atom on the overall isotopic pattern grows smaller. On the other hand, hand-selected restrictions on this set of elements may prevent the identification of the correct molecular formula. Thus, it is a crucial step to determine the set of elements most likely comprising the compound prior to the assignment of an elemental formula to an exact mass. In this paper, we present a method to determine the presence of certain elements (sulfur, chlorine, bromine, boron, and selenium) in the compound from its (high mass accuracy) isotopic pattern. We limit ourselves to biomolecules, in the sense of products from nature or synthetic products with potential bioactivity. The classifiers developed here predict the presence of an element with a very high sensitivity and high specificity. We evaluate classifiers on three real-world data sets with 663 isotope patterns in total: 184 isotope patterns containing sulfur, 187 containing chlorine, 14 containing bromine, one containing boron, one containing selenium. In no case do we make a false negative prediction; for chlorine, bromine, boron, and selenium, we make ten false positive predictions in total. We also demonstrate the impact of our method on the identification of molecular formulas, in particular on the number of considered candidates and running time. The element prediction will be part of the next SIRIUS release, available from https://bio.informatik.uni-jena.de/software/sirius/

Two of a KindThe Biosynthetic Pathways of Chlorotonil and Anthracimycin

Chlorotonil A is a novel polyketide isolated from the myxobacterium <i>Sorangium cellulosum</i> So ce1525 that features a unique gem-dichloro-1,3-dione moiety. It exhibits potent bioactivity, most notably against the problematic malaria pathogen <i>Plasmodium falciparum</i> in the nanomolar range. In addition, strong antibacterial and moderate antifungal activity were determined. The outstanding biological activity of chlorotonil A as well as its unusual chemical structure triggered our interest in elucidating its biosynthesis, a prerequisite for alteration of the scaffold by synthetic biology approaches. This endeavor was facilitated by a recent report describing the strikingly similar structure of anthracimycin from a marine streptomycete, a compound of considerable interest due to its potent antibacterial activity. In this study, we report the identification and characterization of the chlorotonil A biosynthetic gene cluster from So ce1525 and compare it with that for anthracimycin biosynthesis. Access to both gene clusters allowed us to highlight commonalities between the two pathways and revealed striking differences, some of which can plausibly explain the structural differences observed between these intriguing natural products

Structural Insights into Anthranilate Priming during Type II Polyketide Biosynthesis

The incorporation of nonacetate starter units during type II polyketide biosynthesis helps diversify natural products. Currently, there are few enzymatic strategies for the incorporation of nonacetate starter units in type II polyketide synthase (PKS) pathways. Here we report the crystal structure of AuaEII, the anthranilate:CoA ligase responsible for the generation of anthraniloyl-CoA, which is used as a starter unit by a type II PKS in aurachin biosynthesis. We present structural and protein sequence comparisons to other aryl:CoA ligases. We also compare the AuaEII crystal structure to a model of a CoA ligase homologue, AuaE, which is present in the same gene cluster. AuaE is predicted to have the same fold as AuaEII, but instead of CoA ligation, AuaE catalyzes acyl transfer of anthranilate from anthraniloyl-CoA to the acyl carrier protein (ACP). Together, this work provides insight into the molecular basis for starter unit selection of anthranilate in type II PKS biosynthesis