Scientists from Leeds' School of Food Science and Nutrition have developed an advanced AI process that can rapidly identify plant proteins capable of acting as emulsifiers from tens of millions of initial candidates. To date, it has already identified nearly 800. The discovery will cut years of costly trial-and-error research and bring the next generation of plant-based foods and cosmetics closer.
Emulsifiers are essential for binding oil and water into stable, homogenous mixtures in food, cosmetics, pharmaceuticals and industrial applications. Common uses include lotions, medicinal creams, sauces, ice creams, mayonnaise and paints. There is increasing interest in creating natural, sustainable alternatives to high carbon-footprint synthetic or animal-derived emulsifiers.
The research, published today in Communications Chemistry, was led by postdoctoral researcher Dr Simha Sridharan and supervised by Professor Anwesha Sarkar, both of the University's Sarkar Lab . They worked alongside AI researchers at Leeds' School of Food Science and Nutrition and in close collaboration with Dr Rik Sarkar, a machine learning expert at the University of Edinburgh.
Dr Sridharan said: "As we want to shift towards more sustainable, plant‑based ingredients, scientists face a major challenge: There are millions of potential plant proteins but testing them all to identify the right emulsifier is expensive and involves a time‑consuming trial-and-error approach. Until now, there has been no reliable way to predict which plant proteins are likely to behave as emulsifiers like animal proteins."
There is growing interest among consumers for natural emulsifiers, in place of the commonly used animal-based emulsifiers such as milk proteins like caseins or whey. This new tool could help identify new possibilities, reduce years of testing, and accelerate the transition towards sustainable, plant-based food systems.
Dr Sridharan and Professor Anwesha Sarkar used a simulation model to understand how proteins attach between oil and water mixtures, which is crucial for them to act as an emulsifier. In collaboration with Dr Rik Sarkar, they applied machine learning to fingerprint specific segments of the protein that dictate their attachment behaviour. By combining machine learning and statistical physics, the team were able to screen plant proteins to identify those that will resemble the emulsification performance of animal proteins in a fraction of the time needed by conventional experiments.
Dr Rik Sarkar said: "Emulsfiers often have a characteristic chemical structure called di-blocks. We were able to model this structure mathematically for plant proteins. Using machine learning based on features obtained from statistical physics simulations, we can predict which plant proteins are most likely to work best as natural emulsifiers."
The team believe their discovery could be of interest to food and cosmetics companies developing plant‑based and sustainable products.
Professor Anwesha Sarkar, who is also co-director of the National Alternative Protein Innovation Centre (NAPIC) based at Leeds, added: "The model identified nearly 800 plant proteins that could potentially act as emulsifiers, many of which had never previously been considered for this purpose. We then tested several commercially available proteins and found the results matched the model's predictions, with proteins from peas and potatoes proving effective. This shows how AI could help researchers find promising new ingredients much faster than before."
Further information
"Data-driven pipeline enables discovery of plant protein surfactants" will be published in Communications Chemistry at 10:00am GMT on Thursday 3rd September.
