Denisov, S. S. | bioRxiv Science

bioRxiv2025

Chemokine-binding all-D-CLIPS™ peptides identified using mirror-image phage display

Chemokines are secreted blood proteins, which steer leukocyte migration in the inflammatory response. Neutralization of chemokines is believed to be a beneficial therapeutic strategy for the treatment of inflammation-associated diseases. Proteolytically stable chemokine-binding peptides could be suitable candidates for the development of chemokine-neutralizing agents. Here, we report mirror-image phage display selection of cyclic all-D-peptides against the C-X-C motif chemokine ligand 8 (CXCL8). Selection yielded structurally diverse all-D-peptides with sub-micromolar affinity to the target CXCL8 chemokine and different selectivity to related chemokines. Binding of these all-D-peptides caused dissociation of the native CXCL8 dimer and disruption of its binding to GAGs, without effect on in vitro cell migration. This work demonstrates the example of mirror-image phage display selection of cyclized all-D-peptides and its utility for the development of chemokine-binding agents.

biochemistry↗

bioRxiv2024

Unravelling the functional diversity of type III polyketide synthases in fungi

Type III polyketide synthases (T3PKSs) are enzymes that produce diverse compounds of ecological and clinical importance. While well-studied in plants and bacteria, only a handful of T3PKSs from fungi have been characterised to date. Here, we developed a comprehensive workflow for kingdom-wide characterisation of T3PKSs. Using publicly available genomes, we mined more than 1000 putative enzymes and analysed their active site architecture and genomic neighbourhood. From there, we selected 37 representative PKS candidates for cell-free expression and prototyping with a diverse set of Coenzyme A activated substrates, revealing unique patterns in substrate and cyclisation specificity, as well as the preferred number of malonyl-Coenzyme A extensions. Using the 341 enzyme-substrate pairs generated in this study, we trained a machine learning model to predict T3PKS substrate specificity and experimentally validated it with an extended panel of non-natural substrates. We anticipate that the model will be useful for in silico screening of T3PKSs, while the insight into the product scope of these enzymes offers interesting starting points for further exploration.

biochemistry↗