UC San Diego researchers eliminate drug discovery bottleneck

In addition to screening for new drugs and studying natural compounds, the authors say this work may aid biosynthetic engineering efforts to reprogram E. coli strains in order to turn them into NRP assembly lines, now that researchers have a rapid method for characterizing the resulting NRPs.

NRPs such as penicillin, and other natural products, have an unparalleled track record in pharmacology: nine out of the top 20 best-selling drugs were either inspired by or derived from natural products, the authors say.

Nonribosomal peptides evolved over millions of years and often serve chemical defense and communication purposes for the organisms that manufacture them, explained first author Nuno Bandiera, a UCSD postdoctoral researcher and successful Ph.D. candidate from the computer science department at UCSD’s Jacobs School of Engineering.

It is notoriously difficult to determine the structure of NRPs because the usual peptide sequencing tools do not work. The cyclic structures of NRPs, the prevalence of non-standard amino acids that thwart database lookups, and the lack of structural information directly inscribed in the genomic DNA due to the nonribosomal nature of the peptides are all major contributors to the roadblock. Researchers have had to rely on slow, manual, expensive and not always reliable approaches to deciphering the structure of NRPs.

“This work removes a particularly troublesome bottleneck in the drug discovery pipeline for this class of therapeutics,” said Pieter Dorrestein, assistant professor in the Skaggs School of Pharmacy and Pharmaceutical Sciences and the Departments of Pharmacology, Chemistry and Biochemistry. “We have shown a way to quickly, structurally characterize nonribosomal peptides. Our next step is to replicate our findings with newly discovered, potentially therapeutic peptides.”

The UCSD researchers have shown that it is possible to break NRP rings apart and then break the resulting peptide strings into smaller and smaller subunits of the original ring using multiple passes with a mass spectrometer. This approach – called multistage mass spectrometry – allowed the UCSD Skaggs School researchers to collect data on the weights of ring fragments as these fragments got progressively shorter and more numerous with each pass of the mass spectrometer.

The UCSD Jacobs School computer scientists designed algorithms that literally pick up the pieces from here. The algorithms glue the overlapping pieces together until they have reassembled a series of possible original ring structures, explains Julio Ng, a graduate student in UCSD’s Interdisciplinary Bioinformatics Ph.D. program and RECOMB 2008 paper co-author.