Machine learning method improves accuracy of inverse protein folding for drug design

Sheffield computer scientists in collaboration with AstraZeneca and the University of Southampton have developed a new machine learning framework that has shown the potential to be more accurate at inverse protein folding than existing state-of-the-art methods.

Machine learning to more accurately predict which amino acid sequences will fold into stable, functional protein structures. These models are trained on large datasets of known protein sequences and structures to improve inverse folding predictions.

The results are a promising basis to develop the technology further, which, if successful, could accelerate the design of the key proteins needed to develop new vaccines and gene therapies, and other therapeutic modalities.