SAN DIEGO A new method that searches an enormous database of protein structures could allow researchers to predict a drug’s potential side effects without researchers having to perform a single lab experiment, according to technologyreview.com.
Drugs work by latching onto very specific receptor sites on protein targets, like a key fitting into a lock. If a drug also happens to latch onto another, unintended target, it may produce unwanted side effects. The new method analyzes the shape of the intended lock and then combs through the structures of other proteins to look for similarly shaped locks. If such a lock is found, an algorithm rigorously determines whether the drug fits snugly into it. If it does, the technique has probably identified what’s called an “off-target.”
The researchers, led by University of California, San Diego pharmacologist Philip Bourne, didn’t stop there. Once they had identified an off-target, they worked backward, looking for other drugs known to bind to it and then using their technique to determine whether those drugs could also bind to the original target. If so, the researchers had further evidence that the two proteins—the intended target and the unintended target—had highly similar drug receptor sites. Then, by assessing the biological function of the off-target, they could suggest possible side effects of the drug being tested.
Bourne’s new technique reverses the approach ordinarily taken to computational drug design. Most methods focus on the drug rather than on the protein that it binds to. Pharmaceutical researchers routinely sift through databases of small molecules looking for drugs to match a particular protein. Bourne’s team, by contrast, is looking for proteins to match a particular drug.
The technique could also be used to identify ways to repurpose existing drugs, an application that Bourne’s group is currently exploring. Not all side effects are bad: just as the antidepressant bupropion, the active ingredient in Wellbutrin, is also used as an aid in smoking cessation, many drugs could have more than one beneficial use. Computational screening for off-targets could identify such alternative uses of known drugs.