Leptochelins A-C, Cytotoxic Metallophores Produced by Geographically Dispersed Strains of Marine Cyanobacteria.

Metals are important cofactors in the metabolic processes of cyanobacteria, including photosynthesis, cellular respiration, DNA replication, and the biosynthesis of primary and secondary metabolites. In adaptation to the marine environment, cyanobacteria use metallophores to acquire trace metals when necessary as well as to reduce potential toxicity from excessive metal concentrations. Leptochelins A-C were identified as structurally novel metallophores from three geographically dispersed cyanobacteria of the genus .

The Kavaratamides: Discovery of Linear Lipodepsipeptides from the Marine Cyanobacterium Using a Comparative Chemogeographic Analysis.

Kavaratamide A (), a new linear lipodepsipeptide possessing an unusual isopropyl--methylpyrrolinone moiety, was discovered from the tropical marine filamentous cyanobacterium collected from Kavaratti, India. A comparative chemogeographic analysis of . collected from six different geographical regions led to the prioritized isolation of this metabolite from India as distinctive among our data sets.

MHC-Fine: Fine-tuned AlphaFold for precise MHC-peptide complex prediction.

The precise prediction of major histocompatibility complex (MHC)-peptide complex structures is pivotal for understanding cellular immune responses and advancing vaccine design. In this study, we enhanced AlphaFold's capabilities by fine-tuning it with a specialized dataset consisting of exclusively high-resolution class I MHC-peptide crystal structures. This tailored approach aimed to address the generalist nature of AlphaFold's original training, which, while broad-ranging, lacked the granularity necessary for the high-precision demands of class I MHC-peptide interaction prediction.

PECAN Predicts Patterns of Cancer Cell Cytostatic Activity of Natural Products Using Deep Learning.

Many machine learning techniques are used as drug discovery tools with the intent to speed characterization by determining relationships between compound structure and biological function. However, particularly in anticancer drug discovery, these models often make only binary decisions about the biological activity for a narrow scope of drug targets. We present a feed-forward neural network, PECAN (Prediction Engine for the Cytostatic Activity of Natural product-like compounds), that simultaneously classifies the potential antiproliferative activity of compounds against 59 cancer cell lines.