Youngs, R. | Search bioRxiv

bioRxiv2026

Expanding CyanoHAB Monitoring: New Micropeptins and Generalizable MS/MS Workflows for the Annotation of Cyanopeptide Classes

Cyanobacterial harmful algal blooms (cyanoHABs) are a major ecological and public health concern, commonly monitored for hepatotoxic microcystins and cylindrospermopsins and neurotoxic anatoxins and saxitoxins. However, the broader suite of bioactive metabolites produced during blooms remains under characterized. Here, we interrogated a chromatography fraction library generated from a cyanoHAB in Muskegon, Michigan. From this library, we isolated two new micropeptins (1 and 2), including an analog bearing a bishomologated tyrosine residue, and we confirmed the structure of ferintoic acid C (3). Structures were established using complementary spectrometric and spectroscopic methods. To expand chemical space coverage beyond isolated compounds, we analyzed LC-MS/MS data using the GNPS2 Analysis Hub query language for product ion searching, enabling annotation of cyanopeptide classes and class-specific modifications across the fraction set, which provided a practical and user-friendly approach for identifying cyanopeptide classes. One of the new micropeptins (1) exhibited moderate inhibition of neutrophil elastase, consistent with roles in ecological interactions and potential relevance to human exposure. Analysis of field samples from ongoing Lake Erie blooms showed recurring micropeptins but no evidence of microcystins. Together, these results challenge microcystin-centric assessments of bloom hazard and support expanded monitoring of non-microcystin cyanopeptides. SYNOPSISRoutine cyanoHAB monitoring targets few regulated toxins; we reveal abundant, under characterized cyanopeptides and enable rapid class-level annotation across datasets with a new LC-MS/MS analysis pipeline. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=114 SRC="FIGDIR/small/704577v1_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@1849d1eorg.highwire.dtl.DTLVardef@16729a8org.highwire.dtl.DTLVardef@1dffe58org.highwire.dtl.DTLVardef@b36a52_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2025

Reversing Erooms Law- Finitude and Dilution in Small-Molecule Discovery

Drug discovery productivity--measured as clinically viable new molecules per research has declined for decades, a trend known as Erooms Law. This paradox persists despite advances in screening, computing, and artificial intelligence. Here we show that it stems from two forces: the finite diversity of natures biosynthetic rules, leading to saturation of novel natural scaffolds, and the dilution of viable candidates in expanding synthetic chemical spaces, where success rates fall without improved specificity. We propose an optimized hybrid strategy that allocates effort to mining natural products and using their validated patterns to guide synthetic exploration, maximizing novelty per cost. This approach yields testable rules, such as optimal allocation thresholds and benchmarks for artificial intelligence tools. By embedding natural principles in modern discovery, this framework offers a path to reverse Erooms Law and boost pharmaceutical efficiency.

pharmacology and toxicology↗