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

项目摘要/摘要 来自细菌、真菌和植物的天然产物长期以来一直是一种丰富的分子来源，具有迷人的 化学结构和与治疗相关的生物活性。然而，由于其复杂的结构，它是 很难筛选出许多天然产品的类似物来真正理解支配这种关系的规则 它们的结构和活动之间的关系。在家长奖中，我们正在通过制定 一种能够对天然化合物的结构-活性关系(SAR)进行功能建模的机器学习方法 并协助设计可合成天然产物类似物的生物合成途径。 家长奖的第一个项目是将机器学习应用于研究天然产物SARS。我们的一种方法是 Take是一种遗传方法，我们正在验证我们之前开发的机器学习模型 根据天然产物生物合成中生物合成基因的存在与否来预测生物活性 基因簇(BGC)。这种方法的一个主要挑战是提取细菌培养物产生一种复杂的 代谢物混合在一起，很难将单独的代谢物与其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级法将天然产物与观察到的活性联系起来。这是一个低谷- 生产量大，过程费力。有一些技术可以使这一过程更快，通常 包括并行进行层析和收集馏分和质谱图，从而使比质量 特征可以与每个分数相关联。那么在生物活性分析中观察到的活性可以关联到 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 吞吐量比我们目前的方法，但它需要LC-MS与部分收集，而我们没有 有权使用最新的仪器进行这项检测。这项设备补充将使我们能够 购买所需的工具，并最大限度地增加这个项目成功的机会。 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以生物合成新的天然产物类分子。在这个项目中，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品。虽然我们确实有权访问 适用于此目的的核心设施仪器，使用它们与按小时收费相关。我们 也可以使用我们将随本补充购买的仪器来执行这些实验，这将 节省核心设施成本，允许更多的父级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.