Washtenaw Community College

The antibiotic cerulenin is a fungal natural product identified as a covalent inhibitor of ketosynthases within fatty acid and polyketide biosynthesis. Due to its selective and potent inhibitory activity, cerulenin has found significant utility in multidisciplinary biochemical, biomedical, and clinical studies. Although its covalent inhibition profile has been confirmed, cerulenin's mechanism has not been fully determined at a molecular level, frustrating the drug development of related analogues. Herein, we describe the use of stable isotopic tracking with NMR and MS methods to unravel the covalent mechanism of cerulenin against type II fatty acid ketosynthases. We detail the discovery of a unique C2-C3 retro-aldol bond cleavage and a structural rearrangement upon covalent inhibition of cerulenin at the active cysteine residue in E. coli type II fatty acid ketosynthases FabB and FabF.

Machine learning (ML) applications in medical artificial intelligence (AI) systems have shifted from traditional and statistical methods to increasing application of deep learning models. This survey navigates the current landscape of multimodal ML, focusing on its profound impact on medical image analysis and clinical decision support systems. Emphasizing challenges and innovations in addressing multimodal representation, fusion, translation, alignment, and co-learning, the paper explores the transformative potential of multimodal models for clinical predictions. It also highlights the need for principled assessments and practical implementation of such models, bringing attention to the dynamics between decision support systems and healthcare providers and personnel. Despite advancements, challenges such as data biases and the scarcity of “big data” in many biomedical domains persist. We conclude with a discussion on principled innovation and collaborative efforts to further the mission of seamless integration of multimodal ML models into biomedical practice.

Short amphipathic peptides are capable of binding to transcriptional coactivators, often targeting the same binding surfaces as native transcriptional activation domains. However, they do so with modest affinity and generally poor selectivity, limiting their utility as synthetic modulators. Here we show that incorporation of a medium-chain, branched fatty acid to the N-terminus of one such heptameric lipopeptidomimetic (LPPM-8) increases the affinity for the coactivator Med25 >20-fold (Ki >100 μM to 4 μM), rendering it an effective inhibitor of Med25 protein-protein interactions (PPIs). The lipid structure, the peptide sequence, and the C-terminal functionalization of the lipopeptidomimetic each influence the structural propensity of LPPM-8 and its effectiveness as an inhibitor. LPPM-8 engages Med25 through interaction with the H2 face of its activator interaction domain and in doing so stabilizes full-length protein in the cellular proteome. Further, genes regulated by Med25-activator PPIs are inhibited in a cell model of triple-negative breast cancer. Thus, LPPM-8 is a useful tool for studying Med25 and mediator complex biology and the results indicate that lipopeptidomimetics may be a robust source of inhibitors for activator-coactivator complexes.

By DeAnna Gapp, PhD, RN, Nursing Department, Washtenaw Community College, Ann Arbor, Michigan, [email protected]. The author declares no conflict of interest.