- Scientists from the University of Sheffield and AstraZeneca have created an AI approach that can facilitate protein design for new drugs
- This new AI approach outweighs the current methods of “reverse protein folding” – an important part of the process of creating new proteins with specific functions
- Folding of reverse proteins is important but difficult, as small changes in protein sequence can cause unpredictable effects on protein structure
- For the drug to function properly, the proteins need to fold into a very specific 3D shape
- This new approach works like a guide to predict the most important folds of protein structures, making the design process more accurate
The AI approach, developed by researchers at the University of Sheffield and AstraZeneca, could facilitate the design of proteins needed for new treatments.
In a study published in the journal Nature Machine Intelligence, Sheffield Computer Scientists collaborated with Astrazeneca and the University of Southampton to develop a new machine learning framework that demonstrated the possibility that it could be 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: Addressing 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 computer scientists in Sheffield and AstraZeneca, who developed a drug bang AI that can predict whether candidates 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 in 2023.
The latest paper, mask-preferred induced removal diffusion, improves reverse protein folding and is published in Nature Machine Intelligence. Read the paper in full.
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