Journal News

JBC: Simulations might speed drug target discovery

Sasha Mushegian
May 1, 2018

Researchers at the California Institute of Technology have developed an approach to overcome a major stumbling block in testing new drug targets. The work was reported in in the Journal of Biological Chemistry.

A new method improves protein yield by predicting membrane insertion efficiency,
a key step in the expression of membrane protein.
courtesy of Thomas Miller III and William Clemons Jr./caltech

Proteins embedded in cell membranes are potential targets for drugs to treat a number of diseases, from infectious diseases to cancers. Membrane proteins (which include transporters, channels and receptors) are the targets of almost 70 percent of FDA-approved drugs.

However, it is notoriously difficult for researchers to produce membrane proteins in the lab in sufficient quantities to be able to purify them and conduct experiments with potential drugs. Thomas F. Miller III and William M. Clemons Jr. of the department of chemistry and chemical engineering at Caltech wondered whether there was a way to help researchers experiencing this problem.

“Our motivation for this project was really born out of frustration with this general problem, which is that membrane proteins are very hard to produce at scale for experimental purposes,” Clemons said.

To produce proteins of interest, researchers typically insert the gene encoding the protein into a laboratory workhorse cell line, such as Escherichia coli; this process is called heterologous overexpression of a protein. But membrane proteins typically are overexpressed in only very small amounts for reasons that have been poorly understood until now. Individual researchers sometimes spend years trying to modify their proteins of interest in ways that will make them more efficiently expressed in the lab.

“People just hunt around in the dark to hopefully find something that works better so that they can get enough protein to perform their studies,” Miller said. “New tools are needed to rationally enhance that, to do it in a more purposeful way.”

To see whether there were any general principles that could guide attempts to improve membrane protein expression, Clemons and Miller and their graduate students Michiel J.M. Niesen and Stephen S. Marshall focused on a specific step in the process: the point when a cell actually inserts a newly synthesized protein into the membrane.

The efficiency of insertion — that is, the fraction of the time that a protein is inserted into the membrane correctly — depends on the protein’s amino acid sequence. The team developed a computational simulation method to predict how a change in the sequence would affect insertion efficiency.

In the new study, the team tested how this predicted efficiency related to protein expression in the lab. The team systematically produced many variants of a particular protein and used the algorithm to predict each variant’s membrane insertion efficiency. Then the researchers quantified how much protein was produced. As they had hypothesized, improved insertion efficiency correlated with improved protein yield.

Now researchers interested in studying a particular membrane protein can use these simulation tools to predict what changes they should make to their protein sequence in order to produce the membrane protein in the lab. There are caveats: If a particular protein in a particular cell type is subject to inefficiencies at steps in its synthesis other than membrane insertion, then the new method may not help. But the researchers are confident that the method offers a way forward for many membrane protein researchers struggling to express their proteins.

“We believe that the tools we’ve developed here have the potential to really revolutionize membrane protein expression,” Clemons said. “There are still things we have to do to fully realize that, but this paper demonstrates that the potential is there.”

The researchers are teaming up with others to put these tools to work.

“There are many membrane protein targets that are of real importance and real value for pharmaceutical and drug design purposes,” Miller said. “If we can help people by bringing an elusive target within grasp, it would be a big victory.”

Sasha Mushegian

Sasha Mushegian is a postdoctoral fellow at Georgetown University. Follow her on Twitter.

Join the ASBMB Today mailing list

Sign up to get updates on articles, interviews and events.

Latest in Science

Science highlights or most popular articles

Meet Stephanie Moon
Observance

Meet Stephanie Moon

Aug. 2, 2021

She studies messenger RNA regulation at the University of Michigan in Ann Arbor.

Researchers find a cell surface decorated with sugar-coated RNAs
News

Researchers find a cell surface decorated with sugar-coated RNAs

Aug. 1, 2021

Finding not just glycoproteins and glycolipids but also glycoRNA means “now there are three hands, and we don’t know what that third hand is doing.”

Unraveling the mysterious mutations that make delta the most transmissible COVID virus yet
News

Unraveling the mysterious mutations that make delta the most transmissible COVID virus yet

July 31, 2021

As of this week, the delta variant had caused at least 92% of the new infections in the United States, according to a research firm in Switzerland.

Cats communicate with the help of bacteria living in their butts
News

Cats communicate with the help of bacteria living in their butts

July 31, 2021

KittyBiome researchers want to study the cat microbiome to improve health and understand scent-based communication.

From the journals: JLR
Journal News

From the journals: JLR

July 29, 2021

Reversing alcoholism’s effects on lipid droplets. How HDL cholesterol might reduce COVID-19 risk. Shining light on the cholesterol–GPCR relationship. Read about recent papers on these topics in the Journal of Lipid Research.

Starved to death: Can dietary methionine combat cancer?
Journal News

Starved to death: Can dietary methionine combat cancer?

July 27, 2021

Scientists draw a connection between this essential amino acid and cancer lipid metabolism.