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The AI approach, developed by researchers at the University of Sheffield and AstraZeneca, could facilitate the design of proteins needed for new treatments.
Research published in the journal Nature Machine IntelligenceSheffield computer scientists have worked with AstraZeneca and the University of Southampton to develop a new machine learning framework that demonstrates the possibility of being more accurate with inverse protein folding than existing cutting edge methods.
Reverse protein folding is an important process for creating novel proteins. It is the process of identifying amino acid sequences, which are components of a protein, folded into the 3D protein structure of interest, allowing the protein to perform certain functions. Protein engineering plays an important role in drug development by designing proteins that can bind to specific targets in the body. However, this process is difficult due to the complexity of protein folding and the difficulty in predicting how amino acid sequences interact to form functional structures.
Scientists have turned to machine learning to more accurately predict which amino acid sequences fold into stable functional protein structures. These models are trained on a large dataset of known protein sequences and structures to improve inverse folding prediction.
A new machine learning framework called MapDiff from the University of Sheffield, AstraZeneca and the University of Southampton surpasses the most cutting-edge AI in successfully making predictions in simulated tests. The results are promising grounds for further development of the technology, and if successful, can accelerate the design of key proteins needed to develop new vaccines and gene therapy, as well as other therapeutic modalities. It also complements other recent advances, such as Alphafold, which predicts the 3D structure of a protein by inverting the approach by starting with protein folding and then obtaining potential amino acid sequences.
Haiping Lu, professor of machine learning at the University of Sheffield and corresponding author of the research, said: “This work represents an important advancement in using AI to design proteins with desired structures. It addresses these fundamental challenges in biology.”
“During my PhD, I was motivated by the possibilities of AI, and MapDiff becomes the protein of design,” said Paise Hempbai, a senior machine learning scientist at AstraZeneca, who developed AI as part of her PhD at the University of Sheffield's School of Computer Science.
The study is the result of a lackluster collaboration that combines industry expertise and is based on previous research between Sheffield computer scientists and AstraZeneca, who developed an AI that can predict whether candidate drugs can bind to intended target protein molecules in the human body and accelerate the discovery of new drugs. This paper became one of the most cited papers from the journal Nature Machine Intelligence 2023.
reference: Bai P, MiljkovićF, Liu X, and other mask-preferentially induced removal diffusion improves reverse protein folding. Nat Mach Intel. 2025. doi:10.1038/s42256-025-01042-6
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