Bifurcate evolution of quinone synthetases in basidiomycetes

Abstract Background The terphenylquinones represent an ecologically remarkable class of basidiomycete natural products as they serve as central precursors of pigments and compounds that impact on microbial consortia by modulating bacterial biofilms and motility. This study addressed the phylogenetic origin of the quinone synthetases that assemble the key terphenylquinones polyporic acid and atromentin. Results The activity of the Hapalopilus rutilans synthetases HapA1, HapA2 and of Psilocybe cubensis PpaA1 were reconstituted in Aspergilli. Liquid chromatography and mass spectrometry of the culture extracts identified all three enzymes as polyporic acid synthetases. PpaA1 is unique in that it features a C-terminal, yet catalytically inactive dioxygenase domain. Combined with bioinformatics to reconstruct the phylogeny, our results demonstrate that basidiomycete polyporic acid and atromentin synthetases evolved independently, although they share an identical catalytic mechanism and release structurally very closely related products. A targeted amino acid replacement in the substrate binding pocket of the adenylation domains resulted in bifunctional synthetases producing both polyporic acid and atromentin. Conclusions Our results imply that quinone synthetases evolved twice independently in basidiomycetes, depending on the aromatic α-keto acid substrate. Furthermore, key amino acid residues for substrate specificity were identified and changed which led to a relaxed substrate profile. Therefore, our work lays the foundation for future targeted enzyme engineering

Machine Learning Uncovers Natural Product Modulators of the 5-Lipoxygenase Pathway and Facilitates the Elucidation of Their Biological Mechanisms.

Publication status: PublishedMachine learning (ML) models have made inroads into chemical sciences, with optimization of chemical reactions and prediction of biologically active molecules being prime examples thereof. These models excel where physical experiments are expensive or time-consuming, for example, due to large scales or the need for materials that are difficult to obtain. Studies of natural products suffer from these issues─this class of small molecules is known for its wealth of structural diversity and wide-ranging biological activities, but their investigation is hindered by poor synthetic accessibility and lack of scalability. To facilitate the evaluation of these molecules, we designed ML models that predict which natural products can interact with a particular target or a relevant pathway. Here, we focused on discovering natural products that are capable of modulating the 5-lipoxygenase (5-LO) pathway that plays key roles in lipid signaling and inflammation. These computational approaches led to the identification of nine natural products that either directly inhibit the activity of the 5-LO enzyme or affect the cellular 5-LO pathway. Further investigation of one of these molecules, deltonin, led us to discover a new cell-type-selective mechanism of action. Our ML approach helped deorphanize natural products as well as shed light on their mechanisms and can be broadly applied to other use cases in chemical biology

Machine Learning Uncovers Natural Product Modulators of the 5-Lipoxygenase Pathway and Facilitates the Elucidation of Their Biological Mechanisms.

Machine learning (ML) models have made inroads into chemical sciences, with optimization of chemical reactions and prediction of biologically active molecules being prime examples thereof. These models excel where physical experiments are expensive or time-consuming, for example, due to large scales or the need for materials that are difficult to obtain. Studies of natural products suffer from these issues─this class of small molecules is known for its wealth of structural diversity and wide-ranging biological activities, but their investigation is hindered by poor synthetic accessibility and lack of scalability. To facilitate the evaluation of these molecules, we designed ML models that predict which natural products can interact with a particular target or a relevant pathway. Here, we focused on discovering natural products that are capable of modulating the 5-lipoxygenase (5-LO) pathway that plays key roles in lipid signaling and inflammation. These computational approaches led to the identification of nine natural products that either directly inhibit the activity of the 5-LO enzyme or affect the cellular 5-LO pathway. Further investigation of one of these molecules, deltonin, led us to discover a new cell-type-selective mechanism of action. Our ML approach helped deorphanize natural products as well as shed light on their mechanisms and can be broadly applied to other use cases in chemical biology

Controlled masking and targeted release of redox-cycling ortho-quinones via a C-C bond-cleaving 1,6-elimination.

Natural products that contain ortho-quinones show great potential as anticancer agents but have been largely discarded from clinical development because their redox-cycling behaviour results in general systemic toxicity. Here we report conjugation of ortho-quinones to a carrier, which simultaneously masks their underlying redox activity. C-benzylation at a quinone carbonyl forms a redox-inactive benzyl ketol. Upon a specific enzymatic trigger, an acid-promoted, self-immolative C-C bond-cleaving 1,6-elimination mechanism releases the redox-active hydroquinone inside cells. By using a 5-lipoxygenase modulator, β-lapachone, we created cathepsin-B-cleavable quinone prodrugs. We applied the strategy for intracellular release of β-lapachone upon antibody-mediated delivery. Conjugation of protected β-lapachone to Gem-IgG1 antibodies, which contain the variable region of gemtuzumab, results in homogeneous, systemically non-toxic and conditionally stable CD33+-specific antibody-drug conjugates with in vivo efficacy against a xenograft murine model of acute myeloid leukaemia. This protection strategy could allow the use of previously overlooked natural products as anticancer agents, thus extending the range of drugs available for next-generation targeted therapeutics