Versatility, thy name is G protein–coupled receptor (GPCR). Besides responding to diverse extracellular stimuli, GPCRs initiate diverse signaling reactions. Indeed, most GPCRs are multivalent. That is, they are capable of activating diverse G proteins. And yet the multivalency of GPCR–protein pairings is poorly understood, even though it is thought to account for the vast capacity of GPCRs to program complex cellular responses.
To attain a better understanding of GPCR–protein pairings, scientists based at UF Scripps Biomedical Research studied the enzymatic activity of GPCRs in living cells and developed a predictive algorithm of G protein selectivity. The scientists, who were led by neuroscientist Kirill Martemyanov, PhD, suggested that their work could enhance the development of GPCR-targeting drugs. This would be a significant contribution to drug development in general, given that GPCRs already constitute the largest family of proteins targeted by approved drugs.
Martemyanov’s group used a powerful molecular tracking technology to profile the action of more than 124 GPCRs, including their more common genetic variations. The scientists then used that data to develop and train an artificial intelligence (AI)-anchored platform.
Details about this work appeared in the journal Cell Reports, in an article titled, “Rules and mechanisms governing G protein coupling selectivity of GPCRs.” Martemyanov and colleagues indicated that their algorithm predicted with more than 80% accuracy how cell surface receptors would respond to drug-like molecules.
The scientists also noted that their long-range goal is to refine their algorithm so that it could facilitate the design of precision medications. That is, the scientists hope that their algorithm will help predict who could benefit from a given drug. All too often, it is unclear why a medicine that is ideal for one person may prove ineffective or harmful for someone else.
“We all think of ourselves as more or less normal, but we are not. We are all basically mutants. We have tremendous variability in our cell receptors,” Martemyanov said. “If doctors don’t know what exact genetic alteration you have, you just have this one-size-fits-all approach to prescribing, so you have to experiment to find what works for you.”
Besides developing an algorithm for predicting the G protein selectivity of GPCRs, Martemyanov and colleagues interrogated the structural basis of the selectivity, designed synthetic GPCRs with novel specificity, and analyzed the impact of genetic variants on GPCR-G protein selectivity. The scientists had to invent a new protocol to observe and document GPCRs. They found many surprises. Some GPCRs worked as expected, but others didn’t, notably those for neurotransmitters called glutamate.
“[We] establish a classification of GPCRs by functional selectivity, discover the existence of a G12/13-coupled receptor, G15-coupled receptors, and a variety of subclasses for Gi/o-, Gq-, and Gs-coupled receptors, culminating in development of the predictive algorithm of G protein selectivity,” the authors of the Cell Reports article wrote. “We further identify the structural determinants of G protein selectivity, allowing us to synthesize nonexistent GPCRs with de novo G protein selectivity and efficiently identify putative pathogenic variants.”
Martemyanov’s collaborators on the project included his postdoctoral researcher and later staff scientist, Ikuo Masuho, PhD, who now heads his own laboratory at Sanford Research in Sioux Falls, IA, as well as computational protein designer Bruno E. Correia, PhD, who is based at the Swiss Institute of Bioinformatics, in Lausanne, Switzerland, and was instrumental in creating the AI algorithm. Their collaboration grew from a lecture Correia gave at the Jupiter campus in Florida many years ago, Martemyanov said.
It was early days in GPCR research when they started, Martemyanov added, and they lacked that type of broad, sophisticated data on GPCR activity. “If you’ve only looked at one leg of the elephant you may not have the right idea of how to describe it,” he remarked. “You may not see that it’s actually an elephant.”
According to Martemyanov, classifying GPCRs solely by their best-known activity would be like seeing one leg of an elephant. It would be an oversimplification, too general to train AI.
To document the signaling in a comprehensive way, they turned to a useful technology called bioluminescence resonance energy transfer. It involved engineering a small bioluminescent tag into the cells’ proteins and documenting the change to the luminescence as the cell was exposed to molecules that activate GPCRs.
They gathered the data, attached ranks for binding preference, and saw patterns emerge. The data resembled something like an EKG, with measurements for the activation rate, amplitude, and selectivity. They added common genetic variants for the GPCRs that humans carry, and they documented significant differences in how these mutated receptors responded when activated.
When Correia’s group in Switzerland trained the algorithm to make predictions based on this more nuanced data, the researchers were excited by the results. They found it to be correct more than 80% of the time.
The scientists hope their results encourage drug developers to adopt a more sophisticated understanding of GPCRs, their G proteins, and their activities in a way that ultimately benefits patients with safer medicines, created more quickly and at lower cost. Going forward, they intend to explore more deeply how genetic variation affects the way GPCR-acting drug-like compounds work.
“Our ultimate goal,” Martemyanov stated, “is to be able to predict how individual variants that people carry respond to drugs, allowing for the custom tailoring of prescriptions and paving the way for precision medicine.”
