Computational methods for nonribosomal peptide discovery

非核糖体肽发现的计算方法

• 批准号: 2117640
• 负责人: Hosein Mohimani
• 金额: $ 66.85万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2117640&HistoricalAwards=false
• 关键词: Computational; methods; nonribosomal; peptide; discovery

Non-Ribosomal Peptides (NRPs) represent a diverse class of natural products that include antibiotics, immunosuppressants, anticancer agents, toxins, siderophores, pigments, and cytostatics. NRPs have been reported in various habitats, from marine environments to soil, and even human microbiome. However, the discovery of novel NRPs remains a slow and laborious process because NRPs are not directly encoded in the genome and are instead assembled by Non-Ribosomal Peptide Synthetases (NRPSs). This project will develop computational techniques for discovering novel bioactive NRPs through the integration of computational mass spectrometry and genome mining. A summer pre-college program in computational biology and outreaching to under-represented students in Pittsburgh area through STEM junction symposiums will be carried out.This project will develop algorithms for discovering novel bioactive NRPs through the following parts. First, the accuracy of prediction of adenylation domain (A-domain) substrate specificities will be improved through the incorporation of the 3-dimensional structure of domains. These features will be further used for identifying A-domains that encode novel chemistries in RefSeq microbial genomes. Second, algorithms will be developed for predicting the post-assembly modification of NRPs from the modification enzymes present in their gene cluster. Then, probabilistic models will be developed for matching predicted NRPs against mass spectra. These methods will be used to search the publicly available microbial genomes and mass spectra from RefSeq and global natural product social (GNPS) molecular networking infrastructure. The software and results of this project will be available to the scientific community through https://github.com/mohimanilab.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

非核糖体肽(NRP)是一类多种多样的天然产物，包括抗生素、免疫抑制剂、抗癌剂、毒素、铁载体、色素和细胞抑制剂。从海洋环境到土壤，甚至是人类微生物群，各种生境中都有NRPs的报道。然而，新的NRP的发现仍然是一个缓慢而费力的过程，因为NRP不是直接在基因组中编码的，而是由非核糖体多肽合成酶(NRPS)组装的。该项目将开发通过计算质谱学和基因组挖掘相结合的计算机技术来发现新的生物活性核糖核蛋白。将开展一个计算生物学的暑期大学预科项目，并通过STEM联合研讨会向匹兹堡地区代表性不足的学生提供外展服务。该项目将通过以下部分开发发现新的生物活性NRP的算法。首先，通过结合结构域的三维结构，预测腺化结构域(A-结构域)底物特异性的准确性将得到提高。这些特征将进一步用于识别RefSeq微生物基因组中编码新化学成分的A-结构域。其次，将开发算法，根据NRPs基因簇中存在的修饰酶预测NRPs的组装后修饰。然后，将开发概率模型，将预测的NRPS与质谱图进行匹配。这些方法将用于从RefSeq和全球天然产物社会(GNPS)分子网络基础设施中搜索公开可用的微生物基因组和质谱图。该项目的软件和成果将通过https://github.com/mohimanilab.This向科学界提供，该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。