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Cone Snail Venom May Hold Clues for Future Diabetes Treatment

Artificially designed cone snail insulin analogues reportedly bind better and more strongly to the human insulin receptor than the naturally occurring human insulin hormone.

Plaguing around 463 million people globally or 9.3 per cent of the global adult population, diabetes has become one of the leading causes of death worldwide. In 2019, the World Health Organization reported that around 1.5 million people died due to diabetes. While the condition can be improved through controlled diet, physical activity, regular screening, and synthetic insulin injections, there has yet to be an effective cure for diabetes.

Today, nearly 100 years after insulin was first discovered, researchers are tapping into nature’s resources for new treatment options. Scientists at the University of New Hampshire have begun their exploration of cone snails and their highly potent, paralytic insulin-like venom. According to their research, venom variants of a particular cone snail, dubbed as cone snail insulin (Con-Ins), could potentially be used to develop new fast-acting drugs to treat diabetes.

“Diabetes is rising at an alarming rate and it’s become increasingly important to find new alternatives for developing effective and budget-friendly drugs for patients suffering with the disease,” said Harish Vashisth, associate professor of chemical engineering. “Our work found that the modelled Con-Ins variants, or analogues, bind even better to receptors in the body than the human hormone and may work faster, which could make them a favourable option for stabilising blood sugar levels and a potential for new therapeutics.”

Typically found in tropical waters across the globe, cone snails such as C. geographus release venom containing fast-acting insulin to immobilise fish and other potential prey. The sting of certain species, particularly that of large cone snails, can lead to severe hypoglycaemic shocks and have occasionally resulted in fatalities in humans.

In their search for clues to designing potent, fast-acting therapeutic insulin, Gorai and Vashisth investigated the cone snail venom of C. geographus. Compared to the insulin made by the human body, the peptide sequence of the venom, which allows it to bind to human insulin receptors, is significantly shorter. To determine whether they could still effectively bind to human insulin receptors, the team used the insulin-like peptide sequence in the venom of C. geographus as a template to model six different Con-Ins analogues. The newly synthesised variants consisted of much shorter peptide chains than human insulin as they lacked the last eight residues of the B-chain of the human insulin.

After creating these variants, the team then performed multiple independent computer simulations of each Con-Ins variant complex with human insulin receptors in a near-physiological environment to examine the stability and variability of the Con-Ins structures. The scientists accounted for pressure, solution salinity, temperature, and water solvent when designing their testing environment.

Their results revealed that each insulin complex remained stable during the simulations. By comparing the interactions of the human insulin hormone and Con-Ins with the human insulin receptor, the researchers found that each Con-Ins variant exhibited few feasible residue substitutions in human insulin. The team also made a surprising discovery – the insulin-like peptides were able to bind better and more strongly to the receptor than the naturally occurring human insulin hormone, demonstrating its potential as therapeutic insulin.

“While more studies are needed, our research shows that despite the shorter peptide sequences, the cone snail venom could be a viable substitute and we are hopeful it will motivate future designs for new fast-acting drug options,” said Biswajit Gorai, postdoctoral research associate and lead author. [APBN]

Source: Gorai, B., & Vashisth, H. (2021). Structures and interactions of insulin-like peptides from cone snail venom. Proteins: Structure, Function, and Bioinformatics.