Web Exclusives: Chemistry
New Drugs: Getting More Out of Nature
Nature is a prolific source of new medicines. In fact, it inspired more than half of the 877 small molecules introduced as drugs during the 1980s and 1990s. But such natural products chemistry is also labor-intensive and time-consuming, making it a less desirable path to discovering new therapeutic compounds.
Computational tools recently developed by researchers at the University of California, San Diego could make it easier to study and rapidly determine whether natural compounds collected in oceans and forests are potential new candidates for drug development.
The work focuses on a class of natural compounds called nonribosomal peptides (NRPs). These compounds often serve as chemical defenses for the bacteria that manufacture them. Starting from penicillin, NPRs have an unparalleled track record in pharmacology: Most anti-cancer and anti-microbial agents stem from natural products.
"NRPs are one of the last bastions of pharmacologically important biological compounds that remain virtually untouched by computational research. As a result, it is currently one of the most painfully slow processes, it is a real bottleneck that we have now removed," says Pavel Pevzner , a UCSD computer science professor and lead scientist on the new work.
The researchers developed algorithms that computers use to make sense of the flood of the tiny peptide fragments generated by machines called mass spectrometers that blast NPRs apart and determine their sizes. The new algorithms do two key things: They can piece these peptide fragments back together to get NRP chemical structures, and they can take information about known NRPs and determine what the data signature would look like if a mass spectrometer had blown the compound apart.
By using these two approaches, the researchers have created tools that enable others to both characterize the compound they have isolated and check to see if it, or something similar, has been previously described.
"If I collect 1,000 ocean compounds, why waste time with compounds that are already known or patented?" says UCSD computational biologist Nuno Bandeira .
Plus, scientists can use the algorithms to leverage existing information and not start from scratch each time a new compound needs to be identified.
The ultimate outcome: faster drug discovery.
William Gerwick , a UCSD natural products chemist focused on marine organisms, says: "These advances will speed the process by which we discover and describe new and biologically active molecules from organisms such as marine cyanobacteria, also known as blue-green algae. This, in turn, will accelerate the timeline for bringing new experimental therapies into clinical application."