Computational methods for nonribosomal peptide discovery

非核糖体肽（nrp）代表了多种天然产物，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。据报道，NRPs存在于各种栖息地，从海洋环境到土壤，甚至是人类微生物群。然而，新型nrp的发现仍然是一个缓慢而艰苦的过程，因为nrp不直接在基因组中编码，而是由非核糖体肽合成酶（NRPSs）组装。该项目将开发计算技术，通过计算质谱法和基因组挖掘的整合来发现新的生物活性nrp。将开展计算生物学的夏季大学预科课程，并通过STEM连接研讨会向匹兹堡地区代表性不足的学生伸出援助之手。该项目将通过以下部分开发发现新型生物活性nrp的算法。首先，通过结合结构域的三维结构，预测腺苷化结构域（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物质的a结构域。其次，将开发算法来预测基因簇中存在的修饰酶对NRPs的组装后修饰。然后，建立概率模型，将预测的nrp与质谱进行匹配。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。该项目的软件和结果将通过https://github.com/mohimanilab.This向科学界提供，该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.