Computational methods for 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.