Proteins are the fundamental building blocks of life, and understanding their structure and dynamics is crucial for developing effective drugs. Researchers at Brown University have developed a novel approach using machine learning to rapidly predict multiple protein configurations, paving the way for a deeper understanding of protein functions and accelerating drug discovery.
The study, published in Nature Communications, describes a technique that is reportedly accurate, fast, and cost-effective. The team found a way to go beyond the static 3D models of proteins and explore their dynamic 4D shapes, where the fourth dimension is time. Traditionally, computational methods for modeling protein structures have been time-consuming and expensive, limiting their practical application. However, the researchers have leveraged the power of the AI-powered computational method AlphaFold 2 to rapidly predict multiple protein conformations, allowing them to understand how proteins change shape during cellular processes.
“During most cellular processes, proteins will change shape dynamically,” first author Gabriel Monteiro da Silva said. “In order to match protein targets to drugs to treat cancer and other diseases, we need a more accurate understanding of these physiological changes. We need to go beyond 3D shapes to understanding 4D shapes, with the fourth dimension being time. That’s what we did with this approach.”
Using the analogy of a galloping horse, the researchers explain that previous methods could only predict a static model of a standing horse, whereas their new approach can generate multiple snapshots of the horse in motion, revealing the dynamic changes in its muscular structure. This understanding of protein dynamics is crucial for identifying potential drug targets and understanding why some drugs succeed or fail in treating diseases.
The researchers highlight the significant impact this breakthrough can have on drug discovery. By understanding the multiple conformations of proteins, researchers can explore various ways to target them with drugs, leading to more effective treatments for diseases such as cancer. Additionally, the researchers note that their approach can be applied to studying protein dynamics in poorly understood diseases, drug resistance, and emerging pathogens, accelerating the discovery process from years to hours.