AI method speeds up protein generation for drug development

Researchers devise AI method to speed up protein generation for drug development

AI method speeds up protein generation for drug development

Researchers from Chalmers University of Technology, Sweden have developed Artificial Intelligence that is capable of generating novel, functionally active proteins. The research represents a significant breakthrough in the field of synthetic protein development.

“What we are now able to demonstrate offers fantastic potential for a number of future applications, such as faster and more cost-efficient development of protein-based drugs,” said Aleksej Zelezniak, Associate Professor at the Department of Biology and Biological Engineering at Chalmers.

Martin Engqvist, Assistant Professor, also of the Department of Biology and Biological Engineering, who was involved in designing the experiments to test the AI synthesised proteins, commented on the development, saying that “accelerating the rate at which we engineer proteins is very important for driving down development costs for enzyme catalysts. This is the key for realising environmentally sustainable industrial processes and consumer products, and our AI model, as well as future models, will enable that. Our work is a vital contribution in that context.”

Protein-based drugs are very common – the diabetes drug insulin is one of the most prescribed. Some of the most expensive and effective cancer medicines are also protein-based, as well as the antibody formulas currently being used to treat COVID-19.

From digital design to working proteins in just a few weeks

Current methods used for protein engineering rely on introducing random mutations to protein sequences. However, with each additional random mutation introduced, the protein activity declines.

“Consequently, one must perform multiple rounds of very expensive and time-consuming experiments, screening millions of variants, to engineer proteins and enzymes that end up being significantly different from those found in nature,” explained Zelezniak, who lead the research.

“This engineering process is very slow, but now we have an AI-based method where we can go from computer design to working protein in just a few weeks.”

The researchers recently published their findings in the journal Nature Machine Intelligence.


The AI-based approach is called ProteinGAN and uses a generative deep learning approach.

In essence, the AI is provided with a large amount of data from well-studied proteins; it studies this data and attempts to create new proteins based on it. At the same time, another part of the AI tries to figure out if the synthetic proteins are fake or not. The proteins are sent back and forth in the system until the AI can no longer not tell apart natural and synthetic proteins. This method is well known for creating photos and videos of fictitious people, but in this study, it was used for producing highly diverse protein variants with naturalistic-like physical properties that could be tested for their functions.

The proteins widely used in everyday products are not always entirely natural but are made through synthetic biology and protein engineering techniques. Using these techniques, the original protein sequences are modified in the hope of creating synthetic novel protein variants that are more efficient, stable, and tailored towards particular applications. The new AI-based approach is of importance for developing efficient industrial enzymes as well as new protein-based therapies, such as antibodies and vaccines.

The next step for the researchers is to explore how the technology could be used for specific improvements to protein properties, such as increased stability, something which could have great benefit for proteins used in industrial technology.

“Expanding functional protein sequence spaces using generative adversarial networks” in Nature Machine Intelligence.