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Nature Papers Present Approach to Find Natural Products, Method to ID Cancer Driver Mutations, More

Combining cryogenic electron microscopy with genome mining, researchers from the University of California, Los Angeles, have developed a new method for discovering natural products. Chemicals derived from living organisms form the basis for more than 60 percent of pharmaceuticals, yet the discovery rate for these natural products has been slowing in recent decades, largely due to difficulties around structural characterization. To address this, the researchers used microcrystal electron diffraction, which provides unambiguous structures from submicron-sized crystals of chemical compounds not suitable for X-ray analysis, with genome mining to accelerate natural product discovery and structural elucidation. As they reported in Nature Chemical Biology this week, the researchers demonstrate their approach by rapidly determining the structure of a new 2-pyridone natural product and revising the structure of fischerin, a natural product isolated more than 25 years ago that has shown potent cytotoxicity but has ambiguous structural assignment. The work represents "a powerful approach for discovery and structural determination of novel and elusive NPs of high structural complexity," the team writes.

A computational approach for identifying the specific genetic mutations that drive tumorigenesis across tumor types is reported in Nature this week. Despite the existence of good catalogs of cancer genes, distinguishing driver from passenger mutations in these genes is still largely an unsolved problem. Aiming to address this issue, a group of investigators from the Barcelona Institute of Science and Technology used publicly available mutations observed in thousands of tumors across dozens of tumor types to build boostDM, a methodology for creating machine learning models that encapsulate the features of driver mutations. They used their solution to build and validate 185 gene/tissue-specific machine learning models that outperform experimental saturation mutagenesis in the identification of driver and passenger mutations. "Using these models, we outline the blueprints of potential driver mutations in cancer genes and demonstrate the role of mutation probability in shaping the landscape of observed driver mutations," the researchers write. "These blueprints will support the interpretation of newly sequenced tumors in patients and the study of the mechanisms of tumorigenesis of cancer genes across tissues."

A four-dimensional single-cell protein atlas of transcription factors in nearly all lineage-mapped cells during Caenorhabditis elegans embryogenesis is presented in Nature Methods this week. A team from the Chinese Academy of Sciences combined protein-fusion reporters, 4D imaging, and direct lineaging to build the resource, which they state can be used to study metazoan embryogenesis at both the molecular and systems levels.