Hidden Bonds Uncovered: New Algorithm Reveals Unexpected Protein Chemistry.

Despite being some of the most extensively researched molecules in biology, proteins continue to reveal hidden layers of complexity. A groundbreaking study conducted by scientists at the University of Göttingen has uncovered previously undetected chemical bonds within archived protein structures, reshaping our understanding of protein chemistry.

Published in Communications Chemistry, the study highlights the discovery of nitrogen-oxygen-sulfur (NOS) linkages—unique chemical bonds that had gone unnoticed in the vast archive of protein data. These linkages are now understood to play a vital role in how proteins cope with oxidative stress, a condition characterized by an excess of reactive oxygen molecules that can damage vital cellular components such as DNA and proteins.

To make this discovery, the research team employed a new algorithm named SimplifiedBondfinder, which they developed in-house. This advanced pipeline integrates machine learning, quantum mechanical simulations, and structural refinement techniques to reanalyze over 86,000 high-resolution protein structures stored in the Protein Data Bank, a globally recognized repository of protein models.

The study revealed that NOS linkages are not limited to previously recognized amino acid pairings. Remarkably, new connections were identified between arginine-cysteine and glycine-cysteine pairs—expanding the known scope of such interactions. These findings build upon earlier work led by Professor Kai Tittmann, who was instrumental in the initial discovery of NOS bonds.

“Our research shows that even well-studied datasets like the Protein Data Bank can conceal hidden chemistry,” said Dr. Sophia Bazzi of the Institute of Physical Chemistry at Göttingen University, who led the investigation. “With the help of newly developed digital tools, we’ve been able to uncover interactions that have remained obscured for decades.”

Beyond their chemical novelty, NOS linkages function as molecular stabilizers under oxidative conditions, acting like switches that may regulate a wide array of biological mechanisms. Dr. Bazzi emphasized the broader significance of the findings: “Our approach doesn’t just refine protein models—it holds promise for advancements in protein engineering, drug discovery, and synthetic biology.”

The study underscores how innovative computational methods can unlock new insights from existing scientific data, signaling a new chapter in the exploration of protein chemistry.

Source:https://phys.org/news/2025-05-complexity-protein-chemistry-algorithm-uncovers.html

This is non-financial/medical advice and made using AI so could be wrong.