Equipment Supplement to R35GM146987: Purchase of LC-MS system for high throughput isolation of bioactive natural products

项目总结/摘要 来自细菌、真菌和植物的天然产物长期以来一直是具有迷人生物活性的分子的丰富来源。 化学结构和治疗相关的生物活性。然而，由于其复杂的结构， 很难筛选出许多天然产物的类似物，以真正了解管理关系的规则 它们的结构和活动之间的联系。在家长奖中，我们通过开发 机器学习方法，可以在功能上模拟天然药物的结构-活性关系（SAR）， 产品和援助的生物合成途径的设计，可以合成天然产物类似物。 家长奖的第一个项目应用机器学习研究天然产物SAR。一种方法， take是一种遗传方法，我们正在验证我们以前开发的机器学习模型， 基于天然产物的生物合成中生物合成基因的存在或不存在来预测生物活性 基因簇（BGC）。这种方法的一个主要挑战是细菌培养物的提取产生复合物， 这是代谢物的混合物，并且难以将单个代谢物与其BGC或观察到的活性联系起来。目前， 我们正在使用标准的生物活性导向分馏法将天然产物与观察到的活性联系起来。这是一个低- 生产量和费力的过程。有一些技术可以使这个过程更快，通常 包括进行色谱分析并平行收集级分和质谱， 特征可以与每个部分相关联。然后，在生物活性测定中观察到的活性可以与 特定的质量特征，以确定哪些分子负责活动。这个过程要高得多 通量比我们目前的方法，但它需要一个LC-MS与馏分收集，我们没有 使用最新仪器进行本试验。这一设备补充将使我们能够 购买所需的仪器，并最大限度地提高这个项目将取得成功的机会。 家长奖的第二个项目专注于开发机器学习和其他计算工具 用于设计BGC以生物合成新型天然产物样分子。在本项目中，我们将使用LC-MS来 确定我们的工程生物合成酶是否产生了预期的产物。虽然我们有机会 适用于此目的的核心设施工具，使用它们与每小时的费用有关。我们 也可以使用我们将购买的仪器与此补充执行这些实验，这将 节省核心设施成本，允许更多的母公司奖励用于人员和用品。

Project Summary/Abstract Natural products from bacteria, fungi, and plants have long been a rich source of molecules with fascinating chemical structures and therapeutically-relevant bioactivities. However, due to their complex structures, it is difficult to screen many analogs of natural products to truly understand the rules governing the relationship between their structure and activity. In the parent award, we are addressing this challenge by developing machine learning methods that can functionally model the structure-activity relationships (SAR) of natural products and aid in the design of biosynthetic pathways that can synthesize natural product analogs. The first project of the parent award applies machine learning to study natural product SARs. One approach we take is a genetic approach, where we are validating a machine learning model we previously developed that predicts bioactivity based on the presence or absence of biosynthetic genes in a natural product’s biosynthetic gene cluster (BGC). One major challenge of this approach is that extractions of bacterial cultures yield a complex mixture of metabolites and it is difficult to link individual metabolites to their BGCs or observed activity. Currently, we are using standard bioactivity-guided fractionation to link natural products to observed activity. This is a low- throughput and laborious process. There are technologies that can make this process faster, which generally involve performing chromatography and collecting fractions and mass spectra in parallel, so that specific mass features can be associated with each fraction. Then activity observed in bioactivity assays can be correlated with specific mass features to identify which molecules are responsible for activity. This process is much higher throughput than our current approach but it requires an LC-MS with fraction collection, and we do not have access to an up-to-date instrument for performing this assay. This equipment supplement will allow us to purchase the required instrument and maximize the chances that this project will be successful. The second project of the parent award focuses on developing machine learning and other computational tools for designing BGCs to biosynthesize novel natural product-like molecules. In this project, we will use LC-MS to determine if our engineered biosynthetic enzymes produced the expected product. While we do have access to core facility instruments that are appropriate for this purpose, using them is associated with an hourly fee. We could also use the instrument we will purchase with this supplement to perform these experiments, which would save on core facility costs allowing for more of the parent award to go to personnel and supplies.