Artificial intelligence (AI) is transforming how scientists understand proteins—these are working molecules that drive nearly every process in the human body, from cell growth and immune defense to digestion and cell signaling. At NUS, researchers are harnessing AI to fast-track discoveries, offering fresh insights into life at the molecular level and new strategies against disease.
A protein's function is dictated by its three-dimensional (3D) shape, which determines how it interacts with other molecules and provides crucial clues to how diseases develop and could be treated. However, determining these structures experimentally is often time-consuming and costly.
A team led by Professor Zhang Yang, who is from NUS' Cancer Science Institute of Singapore, School of Computing and Yong Loo Lin School of Medicine, has developed D-I-TASSER, a new software tool that predicts the 3D shapes of complex proteins more accurately, supporting faster drug discovery, improved disease research and more precise design of targeted therapies.
"For most proteins, we still do not know their 3D structures, and that remains a major blind spot in biology," said Prof Zhang. "A protein's shape determines what it does in the body, but many large, multi-domain proteins are too complex for existing tools to model reliably."
The human body contains about 20,000 different proteins, many of which consist of several connected parts that move and interact with each other. This complexity makes accurate computer modeling difficult and slows progress in understanding disease mechanisms and developing new medicines.
Combining AI and physics to reveal protein structures
To address this challenge, the team developed D-I-TASSER by combining AI with physics-based simulations. The system breaks a complex protein into smaller sections, predicts the shape of each section first, and then uses physical modeling to assemble them into a complete three-dimensional structure, allowing more precise reconstruction of how the protein folds and fits together.
In tests, D-I-TASSER predicted complex protein structures about 13% more accurately than existing state-of-the-art methods. The researchers were also able to generate reliable structural models for most proteins in the human body, including many that were previously difficult to analyze.
"When we can see a protein's structure more clearly, we can better understand what goes wrong in disease and how potential drugs might interact with it," Prof Zhang added.
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