Machine learning approaches for the discovery, repurposing, and optimization of natural products with therapeutic potential

用于发现、重新利用和优化具有治疗潜力的天然产物的机器学习方法

• 批准号: 10693375
• 负责人: Allison Sara Walker
• 金额: $ 39.63万
• 依托单位: VANDERBILT UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10693375
• 关键词: Anabolism; Attention; Bacteria; Behavior; Biochemical; Biological; Chemical Structure; Chemicals; Complex; Computational Technique; Computers; Coupling; Data; Directed Molecular Evolution; Enzymes; Gene Cluster; Genes; Genetic Structures; Machine Learning; Methods; Modeling; Molecular Machines; Natural Products; Pathway interactions; Peptide Leader Sequences; Peptides; Plants; Property; Ribosomes; Source; Structure; Structure-Activity Relationship; System; Therapeutic; Therapeutic Uses; Training; Work; analog; computerized tools; design; drug discovery; experimental study; fungus; gene product; genetic approach; graph neural network; high throughput screening; improved; inhibitor; interest; machine learning algorithm; machine learning method; machine learning model; method development; molecular modeling; natural product inspired; novel; peptide synthase; polyketide synthase; protein aminoacid sequence; protein protein interaction; trend

Project Summary Natural products from bacteria, fungi, and plants have long been a rich source of useful molecules. 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. We will address 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. Therefore, we will develop methods both for prioritizing natural products that are most likely to be useful as therapeutics for activity screens and for biosynthesizing natural products of interest. Machine learning is a powerful computational technique that enables computers to make inferences from data. There is a wealth of sequence, structure, and activity data available for biological molecules that we can use to build machine learning models to make predictions about the behavior of biochemical systems. Even machine learning algorithms that are not perfectly accurate can be extremely useful for drug discovery efforts. It is possible to screen orders of magnitude more compounds using machine learning than in high-throughput screens. Machine learning can therefore be used as an initial filter to increase hit rates in screens. Our first project will apply machine learning to study natural product SARs. We will take two approaches, a genetic and chemical structure approach. In the genetic approach we will validate correlations between biosynthetic genes and natural product activity that we have previously observed and confirm that the correlation extends to chemical substructures installed by the biosynthetic genes. In the chemical structure approach, we will investigate the ability of graph neural networks to predict natural product properties. Our second project will focus on developing machine learning and other computational tools for designing biosynthetic gene clusters (BGCs) to biosynthesize novel natural product-like molecules. We will first focus on Ribosomally Synthesized and Posttranslationally modified Peptides (RiPPs) and develop methods to predict compatible modifying enzyme-leader peptide pairs. To do this we will use molecular modeling, Statistical Coupling Analysis (SCA), and machine learning. After validating our methods on RiPPs, we will turn our attention to more difficult classes of BGCs, such as nonribosomal peptide synthetases (NRPS) and polyketide synthases (PKS). Our third project is the development of methods for designing RiPP-based protein-protein interaction (PPI) inhibitors. We will develop both molecular modeling and machine learning methods for predicting optimal RiPP sequences for inhibiting a PPI of interest. We will then validate and collect additional training data for these predictions using directed evolution experiments.

项目摘要 来自细菌、真菌和植物的天然产物长期以来一直是有用分子的丰富来源。然而，在这方面， 由于天然产物的结构复杂，很难筛选出许多天然产物的类似物，以真正了解它们的结构。 控制其结构和活动之间关系的规则。我们将通过发展 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。因此，我们认为， 我们将开发方法，优先考虑最有可能作为治疗药物的天然产品， 活性筛选和生物合成感兴趣的天然产物。 机器学习是一种强大的计算技术，使计算机能够从数据中进行推断。 生物分子有丰富的序列、结构和活性数据可供我们使用， 建立机器学习模型来预测生化系统的行为。连机器 不完全准确的学习算法对于药物发现工作是非常有用的。可以 使用机器学习筛选比高通量筛选多几个数量级的化合物。 因此，机器学习可以用作初始过滤器，以提高屏幕中的命中率。 我们的第一个项目将应用机器学习来研究天然产物SAR。我们将采取两种方法， 遗传和化学结构方法。在遗传方法中，我们将验证 生物合成基因和天然产物的活性，我们以前已经观察到，并确认， 延伸到由生物合成基因安装的化学亚结构。在化学结构方法中，我们 将研究图神经网络预测天然产品特性的能力。 我们的第二个项目将专注于开发机器学习和其他计算工具， 生物合成基因簇（BGC）来生物合成新的天然产物样分子。我们将首先关注 核糖体合成和后修饰肽（RIPPs），并开发预测方法 相容的修饰酶-前导肽对。为此，我们将使用分子建模，统计 耦合分析（SCA）和机器学习。在RIPP上验证了我们的方法之后，我们将把注意力转向 更困难的BGC类别，如非核糖体肽合成酶（NRPS）和聚酮酶 （PKS）. 我们的第三个项目是开发基于RiPP的蛋白质-蛋白质相互作用（PPI）的设计方法 抑制剂的我们将开发分子建模和机器学习方法来预测最佳RiPP 用于抑制感兴趣的PPI的序列。然后，我们将验证并收集这些额外的训练数据。 使用定向进化实验进行预测。