Mar 27 2024Brown University To that end, a team of researchers at Brown University has developed a way of using machine learning to rapidly predict multiple protein configurations to advance understanding of protein dynamics and functions.The authors say the technique is accurate, fast, cost-effective and has the potential to revolutionize drug discovery by uncovering many more targets for new treatments.
While Monteiro da Silva said that the accuracy of AlphaFold 2 has revolutionized protein structure prediction, the method has limitations: It allows scientists to model proteins only in a static state at a specific point in time. Monteiro da Silva used the analogy of a horse to explain protein models. The arrangement of the horse's muscles and limbs create different shapes depending on whether the horse is standing or galloping; protein molecules conform into different shapes due to the bonding arrangements of their constituent atoms. Imagine that the protein is a horse, Monteiro da Silva said. Previous methods were used to predict a model of a standing horse.
Rubenstein explained that the protein on which the team focused in this study was one that had different drugs developed for it. Yet for many years, no one could understand why some of the drugs succeeded or failed, she said. "They're expensive in terms of materials, in terms of infrastructure; they take a lot of time, and you can't really do these computations in a high throughput kind of way -; I'm sure I was one of the top users of GPUs in Brown's computer cluster," Monteiro da Silva said.
Technology Technology Latest News, Technology Technology Headlines
Similar News:You can also read news stories similar to this one that we have collected from other news sources.
Source: medical_xpress - 🏆 101. / 51 Read more »