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.

我们许多最重要的疗法都是受细菌小分子（天然产物，NP）的启发。 尽管微生物NP表现出广泛的生物活性，但他们提供了抗癌的最大效用 代理和抗生素。人们认为，NP作为治疗发展的铅结构的令人难以置信的成功。 是由于它们独特的结构和作用模式改进了进化选择性压力的影响。 由于许多药物发现计划由于不可接受的重新发现率而剥夺了NP，因此 基因组序列数据的分析，无论是来自培养细菌还是元基因组，都表明 传统单栽培发酵研究获得的生物合成多样性仅代表一小部分 实际上由全局微生物组编码的NP。解锁由这个大的代谢产物 以前无法访问的生物合成基因簇（BGC）的比例应在结构上和 机械新颖的分子可以作为新的抗癌剂的灵感。传统的NP 发现方法依赖于生物过程（即转录，翻译和酶）来转换通用 细菌基因组中包含的说明中，用于新型生物活性小分子。不幸的是，有这些 方法不可能哄骗实验室种植细菌来产生它们是所有不同的NP 能够制作。因此，我们已经开发了一种“无生物学”发现方法，而不是解码 使用生物学过程，生物信息学算法的遗传指令用于预测化学 细菌产生的结构，然后使用化学合成来构建这些结构，我们已经拥有 称为合成生物信息学NP（SYN-BNP）。该建议旨在将高级 生物信息学，总化学合成和下一代宏基因组方法鉴定Syn-BNP 受BGC启发的抗增殖剂，直到现在，这些剂仍隐藏在基因组中 培养的细菌和宏基因组。有趣的是，当今临床用途中几乎一半的药物受到启发 非透射体辣椒（NRP）或混合聚酮化合物-NRP。幸运的是，NRP生物合成是独一无二的 生物信息学算法已经发展到可以预测许多NRP结构的地步 仅主要数据序列。同时与这些生物信息学进步，合成的鲁棒方法 产生类似NRP的结构已变得简单且经济，使其成为未表征的NRP BGC模型 SYNB发现研究的目标，以及机械多样性和新颖的潜在丰富来源 抗增殖剂。考虑到这一点，在AIM 1对公开可用的NRP BGC的生物信息学分析 数据库将用于激发Syn-BNP 一组潜水癌品系。在AIM 2中，将对元基因组BGC进行测序并用来激发更多 用于抗增殖活性筛查的Syn-BNP。在AIM 3中，抗增殖的Syn-BNP命中将是机械的 研究并进行了合成优化，以准备将来，以便将来更详细的体外和体内研究。