RNA Institute Researcher Working to Shine a Light on 3D RNA Structure Prediction to Advance Drug Discovery

Alan Chen, right, with Jia Sheng, a fellow UAlbany researcher in RNA structure prediction, who will test Chen's groundbreaking 3D models of RNA structure. (Photo by Mark Schmidt)

ALBANY, N.Y. (March 10, 2015) — University at Albany biophysicist Alan Chen is at work creating a new model to accurately predict structures of RNA. If effective, this methodology would alleviate a significant roadblock to RNA-based drug discovery, which holds great promise for various hard-to-treat diseases, better antiviral therapies against RNA viruses, and new classes of antibacterial compounds.

Funded by a grant from the Pharmaceutical Research and Manufacturers of American Foundation (PhRMA), Chen is advancing technology he initially developed in 2013 as a post-doc at Rensselaer Polytechnic Institute with RPI Professor Angel Garcia to predict structures that include potential drug targets in RNA viruses and small molecule binding RNA “aptamers.”

PhRMA supports early career development for scientists involved in research relevant to drug discovery and development. Though supported by manufacturers, PhRMA’s grant to Chen is for basic research. A member of UAlbany’s Department of Chemistry, Chen seeks to accomplish what traditional methods of modeling RNA have not.

Moving from Protein to RNA 3D Structural Views

Existing knowledge of drug design is almost entirely based on the assumption that the biological targets are proteins. Although modern computer simulations are able to accurately predict the three-dimensional (3D) folds of proteins, there have been comparatively few such successes for the 3D structure of RNAs. The advance Chen and Garcia made at RPI improved on protein-centric models, resulting in correct prediction of the structure of several small RNA tetraloops without relying on any knowledge of the final structure — a truly accurate “prediction.”

In a typical drug-discovery project, the first step is to establish a likely biological target that should have a therapeutic effect if drug-treated, then determine its structure to a high resolution (i.e. angstrom — 1/10 billionth of a meter) level. The next step is to find or design a 3D molecule that is complimentary to the biological target, hoping that a therapeutic effect will occur when they recognize each other.

“A drug binding to any old part of a target molecule doesn’t necessarily induce a therapeutic effect,” said Chen. “It has to be a special, functionally crucial part. Therefore you need a combination of high-resolution structural information as well as functional information in order to design a drug that results in a therapeutic effect upon administration.”

For proteins, there are a variety of well-established methods to determine the structure to angstrom-level resolution, but for RNA these methods are extremely difficult, with researchers often devoting years to determine a single RNA structure. Even then, many RNAs of biological interest cannot be predicted by these methods.

“So even though there is this recent explosion in RNA biology and many clever new ways to measure the biological function of RNAs, we are literally fumbling in the dark when it comes to understanding what they actually look like in 3D to the level of detail required to design a therapeutic drug that could bind to it and alter its function,” said Chen.

Work Enhanced by The RNA Institute Environment

His models represent every individual atom of the RNA as well as that of the surrounding biochemical environment, such as water and electrolytes. In principle, the accuracy of these models would require only the genetic sequence of the target RNA as input — easily obtainable by modern gene sequencing techniques. He stresses, however, that even if successful this will be but the first step in a long road toward actually developing RNA-based drugs.

“Even if we can develop a molecule that fits into an RNA’s specific shape, this does not guarantee it will have the desired biochemical/biological effect, that the hypothetical molecule can actually be synthesized efficiently in real life, that it can be efficiently delivered to the target in-vivo, or that it can be administered without adverse side effects to the patient, etc. These are all important concerns that would have to be addressed down the line by researchers once the technology is mature.”

Chen noted that a key component of his grant proposal that impressed reviewers was the quality and diversity of his experimental collaborators at The RNA Institute. “They are able to directly test the results of my computational predictions using state-of-the-art analytical methods, both in the test-tube and even in living cells and model organisms — all utilizing our in-house expertise represented by the cutting edge RNA research going on here.”

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