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 长期以来，来自细菌，真菌和植物的天然产物一直是有用分子的丰富来源。然而， 由于它们的复杂结构，很难筛选许多天然产品的类似物来真正了解 规则管理其结构与活动之间的关系。我们将通过发展来应对这一挑战 机器学习方法可以在功能上对自然的结构活性关系（SAR）建模 产品并有助于设计可以合成天然产物类似物的生物合成途径。所以， 我们将开发既优先考虑最有可能用作治疗方法的天然产品的方法 活动筛选和生物合成的自然产物。 机器学习是一种强大的计算技术，它使计算机能够从数据中推断。 有很多可用于生物分子的序列，结构和活动数据，我们可以用来使用这些数据 建立机器学习模型，以预测生化系统的行为。甚至机器 学习不是完全准确的算法对于药物发现工作非常有用。可能 要使用机器学习的数量级比在高通量屏幕中更多的化合物。 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品SARS。我们将采用两种方法，一个 遗传和化学结构方法。在遗传方法中，我们将验证相关性 我们先前观察到的生物合成基因和自然产物活性，并确认相关性 扩展到生物合成基因安装的化学子结构。在化学结构方法中，我们 将研究图形神经网络预测天然产物特性的能力。 我们的第二个项目将着重于开发机器学习和其他用于设计的计算工具 生物合成基因簇（BGC）以生物合成新型天然产物样分子。我们将首先关注 核糖体合成和翻译后修饰的肽（RIPP），并开发方法来预测 兼容修饰酶领导肽对。为此，我们将使用分子建模，统计 耦合分析（SCA）和机器学习。在验证了Ripps上的方法之后，我们将引起人们的注意 对于更困难的类BGC，例如非透射体肽合成酶（NRP）和聚酮化合物合酶 （PK）。 我们的第三个项目是开发设计基于RIPP的蛋白质 - 蛋白质相互作用（PPI）的方法 抑制剂。我们将开发分子建模和机器学习方法，以预测最佳RIPP 抑制感兴趣的PPI的序列。然后，我们将验证并收集这些数据 使用定向进化实验的预测。