NAST User Profile: Li Niu, PhD

Li Niu of the University of Albany works with Simbios to understand an unusual RNA.

from http://biomedicalcomputationreview.org/content/simbios-bringing-biomedical-simulation-your-fingertips

 

Li Niu, PhD, associate professor of chemistry at the University of Albany, SUNY found an interesting RNA while selecting aptamers against glutamate ion channel receptors from a very large RNA library. “We only stumbled on this interesting problem which now has a life of its own,” he says. This RNA—which can inhibit glutamate ion channel receptors—was confounding: the same sequence can fold into three different stable, functional structures. Two of the forms, M1 and M2, are generated only by enzymatic transcription and must act together to inhibit the receptor. Yet they cannot convert from one to the other under any existing denaturing conditions. “Once they are made by the enzymes, they are what they are. There’s no way to convert them.” Niu says. “That is unprecedented. The structures are not conformations, because they cannot change. RNA is a whole lot smarter than we thought.”

 

That’s where Simbios comes in. Can physics-based simulation explain how a single RNA sequence can produce three stable RNA structures? A few months ago, Magda Jonikas, a graduate student in Russ Altman’s lab, decided to have a look using NAST, the Nucleic Acid Simulation Tool (see: Biomedical Computation Review, Spring 2009, p. 4) she developed with others on the Simbios team. NAST attempts to automatically predict possible 3-D RNA structures from the primary RNA sequence coupled with experimental evidence and known constraints based on the secondary structures Niu’s lab produced. “And she generated some interesting structures,” Niu says. “Hopefully, we’ll be able to collect more chemical and enzymatic probing data to provide better constraints for 3-D modeling. I’m eager to see what they can generate out of this.”

 

With NAST in the public domain, it will become possible for experimental researchers like Niu to generate possible RNA structures on their own—and these structures may also provide clues to guide further experiments. “I would be happier if NAST could make a complete prediction of a 3-D structure from a primary sequence, and then we could experimentally verify these structures,” Niu says. “The field is not there yet, but that’s the goal.”    
 



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