Discovery and characterization of synthetic bioinformatic natural product anticancer agents

合成生物信息天然产物抗癌剂的发现和表征

• 批准号: 10639302
• 负责人: SEAN F BRADY
• 金额: $ 42.31万
• 依托单位: ROCKEFELLER UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-01 至 2028-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10639302
• 关键词: Algorithms; Animal Model; Antibiotics; Antineoplastic Agents; Bacteria; Bacterial Genome; Bioinformatics; Biological Process; Biology; Breathing; Cancer cell line; Chemical Structure; Clinical; Cloning; Collection; DNA; Data; Development; Enzymes; FDA approved; Family; Fermentation; Funding; Future; Gene Cluster; Genetic; Genetic Transcription; Genome; Genomics; Goals; Human; In Vitro; Instruction; Laboratories; Large-Scale Sequencing; Lead; Libraries; Malignant Neoplasms; Metagenomics; Methodology; Methods; Modeling; Natural History; Natural Products; Nature; Peptide Biosynthesis; Peptides; Pharmaceutical Preparations; Resistance; Rewards; Sampling; Soil; Source; Structure; Study models; Therapeutic; Translations; antimicrobial; antiproliferative agents; bioactive natural products; chemical synthesis; cytotoxic; design; drug discovery; in vivo; laboratory experiment; metagenome; microbial; microbiome; mutant; next generation; novel; novel anticancer drug; peptide structure; peptide synthase; polyketides; pressure; programs; public database; screening; small molecule; success; therapeutic development

Many of our most important therapeutics were inspired by bacterial small molecules (natural products, NPs). Although microbial NPs display a wide range of bioactivities, they have offered their greatest utility as anticancer agents and antibiotics. The incredible success of NPs as lead structures for therapeutic development is thought to be due to their unique structural and mode of action refinement from eons of evolutionary selective pressures. Since many drug discovery programs deprioritized NPs due to unacceptably high rediscovery rates, bioinformatic analyses of genomic sequence data, whether from cultured bacteria or metagenomes, has revealed that the biosynthetic diversity accessed by traditional monoculture fermentation studies represents only a small fraction of the NPs that are actually encoded by the global microbiome. Unlocking the metabolites encoded by this large fraction of previously inaccessible biosynthetic gene clusters (BGCs) should provide structurally and mechanistically novel molecules that can serve as inspirations for new anticancer agents. Traditional NPs discovery methods rely on biological processes (i.e., transcription, translation and enzymes) to convert genetic instructions contained in bacterial genomes into novel bioactive small molecules. Unfortunately, with these methods it has not been possible to coax laboratory grown bacteria into producing all the different NPs they are capable of making. We have therefore developed a “biology free” discovery approach where, instead of decoding genetic instructions using biological processes, bioinformatic algorithms are used to predict the chemical structures produced by bacteria and then chemical synthesis is used to build these structures, which we have called Synthetic Bioinformatic NPs (syn-BNPs). This proposal is designed to bring together advanced bioinformatics, total chemical synthesis, and next-generation metagenomic methods to identify syn-BNP antiproliferative agents that are inspired by BGCs which, until now, have remained hidden in the genomes of cultured bacteria and metagenomes. Interestingly, nearly half of all drugs in clinical use today are inspired by nonribosomal peptides (NRPs) or mixed polyketide-NRPs. Fortuitously, NRP biosynthesis is unique in that bioinformatic algorithms have developed to the point where it is possible to predict many NRP structures from primary data sequence alone. Concurrently with these bioinformatic advances, robust methods for synthetically producing NRP-like structure have become simple and economical, making uncharacterized NRP BGCs model targets for syn-BNP discovery studies and a potentially rich source of mechanistically diverse and novel antiproliferative agents. With this in mind, in Aim 1 bioinformatic analysis of NRP BGCs found in publicly available data bases will be used to inspire syn-BNPs that will be screened for differential antiproliferative activity across a panel of diverse cancer lines. In Aim 2, metagenomic BGCs will be sequenced and used to inspire additional syn-BNPs for antiproliferative activity screening. In Aim 3, antiproliferative syn-BNP hits will be mechanistically studied and synthetically optimized to ready them for future more detailed in vitro and in vivo studies.

我们许多最重要的治疗方法都受到细菌小分子（天然产物，NP）的启发。 虽然微生物纳米颗粒显示出广泛的生物活性，但它们在抗癌方面提供了最大的效用 药剂和抗生素。纳米粒子作为治疗开发的主要结构的令人难以置信的成功被认为是 这是由于它们独特的结构和作用方式在进化选择压力的作用下不断完善而形成的。 由于许多药物发现计划由于不可接受的高再发现率而降低了NP的优先级，生物信息学 对基因组序列数据的分析，无论是来自培养的细菌还是宏基因组，都揭示了 通过传统的单一培养发酵研究获得的生物合成多样性仅代表一小部分 由全球微生物组编码的纳米颗粒。解开这个巨大的 一部分以前无法获得的生物合成基因簇（BGC）应该提供结构和 这些分子是机械上新颖的分子，可以作为新抗癌剂的灵感来源。传统NP 发现方法依赖于生物过程（即，转录、翻译和酶）来转化基因 将细菌基因组中包含的指令转化为新的生物活性小分子。不幸的是，有了这些 目前还不可能诱导实验室培养的细菌产生它们所需要的所有不同的NP。 有能力做。因此，我们开发了一种“无生物学”的发现方法， 遗传指令使用生物过程，生物信息学算法用于预测化学 细菌产生的结构，然后用化学合成来构建这些结构， 称为合成生物信息学纳米粒子（syn-BNPs）。该提案旨在汇集先进的 生物信息学、全化学合成和下一代宏基因组学方法来鉴定syn-BNP 抗增殖剂的灵感来自BGC，到目前为止，BGC仍然隐藏在基因组中， 培养的细菌和宏基因组。有趣的是，今天临床使用的所有药物中有近一半是受 非核糖体肽（NRP）或混合聚酮-NRP。幸运的是，NRP生物合成是独特的， 生物信息学算法已经发展到可以预测许多NRP结构的程度， 原始数据序列。与这些生物信息学进展同时，用于合成 产生NRP样结构变得简单和经济，使未表征的NRP BGC模型 syn-BNP发现研究的靶点和机制多样和新颖的潜在丰富来源， 抗增殖剂。考虑到这一点，在目标1中，对在公开可用的文献中发现的NRP BGC进行生物信息学分析。 数据库将用于激发syn-BNP，其将被筛选用于不同的抗增殖活性， 一组不同的癌症细胞系。在目标2中，将对宏基因组BGC进行测序，并用于激发额外的基因组学研究。 syn-BNP用于抗增殖活性筛选。在目标3中，抗增殖性syn-BNP命中将在机制上被抑制。 研究和综合优化，以准备他们在未来更详细的体外和体内研究。