In collaboration with scientists from the University of Basel and ETH Zurich, researchers at Auburn University have made an advance in the fight against cancer. The team, led by Dr. Rafael Bernardi, Associate Professor of Biophysics in the Department of Physics, has developed a novel approach integrating artificial intelligence (AI) with molecular dynamics simulations and network analysis to enhance the prediction of binding sites on the PD-L1 protein. This breakthrough promises to accelerate the development of personalized cancer treatments by identifying critical interaction points in cancer-related proteins.

 Their work, published in the Journal of the American Chemical Society, focuses on understanding how therapeutic proteins interact with PD-L1, a protein known to help cancer cells evade detection by the immune system. Their findings could be instrumental in improving immunotherapies, such as pembrolizumab (Keytruda), that are already revolutionizing cancer treatment.

"Utilizing computational tools to engineer proteins represents the next frontier in cancer therapeutics," said Dr. Bernardi. "Our integrated approach combining AI, molecular dynamics, and network analysis holds immense potential for developing personalized therapies for cancer patients."

One of the greatest challenges in cancer therapeutics is accurately predicting where a drug can bind to its target protein. In this case, the researchers focused on PD-L1, a checkpoint protein that cancers exploit to suppress the immune system. By blocking PD-L1, some modern drugs unleash the immune system to attack tumours. However, understanding where to target PD-L1 with new treatments has been a longstanding problem.

Dr. Bernardi and his team have developed a sophisticated method that combines AlphaFold2-based AI tools with molecular dynamics simulations and dynamic network analysis. Their approach allowed them to predict and confirm key binding regions in the PD-L1 protein critical for drug interaction.

The implications of this study go far beyond PD-L1. The methods developed can be applied to many other proteins, potentially leading to the discovery of new drug targets for various diseases, including different types of cancer and autoimmune conditions. Additionally, this research paves the way for more cost-effective and rapid development of therapeutics, an area where traditional experimental methods can be slow and expensive.

Source:

Phys Org