New Approach to Computationally Designing Drugs for GPCRs

By John Russell

September 8, 2016

Modeling protein interactions with drugs has long been computationally challenging. One obstacle is these interactions often take relatively long to occur and conventional molecular dynamics simulation is insufficient. This week a group of researchers, using several XSEDE supercomputers, report a hybrid in silico-experimental approach that shows promise as a drug design tool for use with G protein-coupled receptors (GPCRs), a class that includes the targets of about 40 percent of currently marketed drugs.

Their report is published in the Proceedings of the National Academy of Sciences (PNAS) early edition (Accelerated structure-based design of chemically diverse allosteric modulators of a muscarinic G protein-coupled receptor (GPCR)), and is focused on a GPCR associated with heart disease.

Using a unique computational approach to rapidly sample, in millisecond time intervals, proteins in their natural state of gyrating, bobbing, and weaving, the research team from UC San Diego and Monash University in Australia identified promising drug leads that may selectively combat heart disease, from arrhythmias to cardiac failure. An account of the work is posted on the San Diego Supercomputing Center web site.

The researchers used supercomputers Gordon and Comet, based at the San Diego Supercomputer Center (SDSC) at UC San Diego; and Stampede, at the Texas Advanced Computing Center at the University of Texas at Austin, to perform a survey of protein structures using accelerated molecular dynamics or aMD – a method that performs a more complete sampling of the myriad shapes and conformations that a target protein molecule may go through.

“Based on the hypothesis that incorporation of receptor flexibility is key to effective GPCR drug design, we used aMD (accelerated molecular dynamics) simulations to construct structural ensembles for molecular docking in the extracellular vestibule of the receptor. Ensemble docking of chemical compounds obtained from the National Cancer Institute (NCI) compound library was performed to identify new potential allosteric modulators. The computationally selected lead compounds were then tested experimentally to investigate their binding and functional properties. We report here a successful structure-based design approach,” write the researchers.

Shown below is an illustration of the workflow:

GPCR Research Workflow

Like most molecular dynamic modeling, aMD examines energy levels to determine the changing conformation of molecules, but shortcuts the process by restricting energy min/max levels explored. Use of supercomputers allowed “us to run hundreds-of-nanosecond aMD simulations, which are able to capture millisecond timescale events in complex biomolecules,” said the study’s first author Yinglong Miao, a research specialist with the Howard Hughes Medical Institute at UC San Diego and research scientist with the UC San Diego Department of Pharmacology.

Modern drug discovery targeting GPCRs, note the researchers, is characterized by an alarmingly high attrition rate. To a large degree this stems from the inability of most ligands to selectively target one receptor. “Many receptor subtypes of GPCR families often exhibit a highly conserved orthosteric binding pocket, such that a single ligand can interact with several receptors simultaneously, leading to the activation/inactivation of multiple receptors, sometimes with opposing of their signaling profiles, contributing to off-target side effects.”

Here’s a rough snapshot of the study workflow: aMD simulations were carried out to construct structural ensembles to account for receptor flexibility. Meanwhile, a compound library was prepared from the NCI Diversity Set (~1,600 compounds) by using LigPrep in the Schrödinger package. Docking known ligands against the receptor X-ray structures and aMD structural ensembles was carried out by using Glide virtual screening workflow (VSW, Schrödinger). Glide induced fit docking (IFD) calculations are very computationally expensive (`200 CPU hours for every 100 compounds per receptor structure).

Overall, retrospective docking of the antagonists and agonists using aMD structural ensembles provided significantly higher enrichment factors than using the X-ray structures alone, report the authors.

The next steps, say the researchers, will involve an investigation of the chemical properties of these novel molecules by the molecular chemists from Monash. More broadly, “This is just the beginning. We believe that it will be possible to apply our combined cutting-edge in silico and in vitro techniques to a wide array of receptor targets that are involved in some of the most devastating diseases,” said Celine Valant, the study’s co-lead investigator from Monash.

In addition to XSEDE, supercomputing time was also provided by the Hopper and Edison supercomputers through the National Energy Research Scientific Computing Center (NERSC).

Link to paper on PNAS: http://www.pnas.org/content/early/2016/09/01/1612353113.abstract

Link to article on SDSC: http://www.sdsc.edu/News%20Items/PR20160905_heart_disease.html

Link to article on TACC: https://www.tacc.utexas.edu/-/promising-drug-leads-identified-to-combat-heart-disease

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