Generative AI is more efficient than nature at designing proteins to edit the genome
After studying the diversity of mobile elements in the genome to feed and train generative AI tools, researchers have been able to design and lab validate new synthetic proteins that can edit the genome more efficiently than natural proteins.
2025-10-02
(Press-News.org)
Researchers at Integra Therapeutics, in collaboration with the Pompeu Fabra University (UPF) Department of Medicine and Life Sciences (MELIS) and the Center for Genomic Regulation (CRG), have designed and experimentally validated new synthetic proteins that can edit the human genome more efficiently than proteins provided by nature. This work, a global pioneer published today in the journal Nature Biotechnology, will be of great use in improving the current gene editing tools used in biotechnology research and personalized medicine by developing cellular (CAR-T) and gene therapies, especially to treat cancer and rare diseases.
The ability to insert large DNA sequences into genomes in a safe, targeted manner has been a revolution in the research and development of advanced therapies in recent years. Among the most promising systems are transposases, such as PiggyBac, which copies and pastes DNA to introduce therapeutic genes into patient cells. However, their potential has been limited by the scarce diversity of known transposases and their lack of precision.
Exploring biodiversity
The researchers used computational bioprospecting to screen more than 31,000 eukaryotic genomes and discovered more than 13,000 new, previously unknown PiggyBac sequences. After performing experimental validation in cultured human cells, 10 active transposases were identified, demonstrating that there is a large functional diversity that has not yet been explored. Two of these new transposases showed activity comparable to versions already optimized for laboratory and patient use, and one of them even exhibited high activity in human primary T cells, a crucial cell type for cancer therapies.
Designing with generative artificial intelligence
In the second phase, researchers went beyond nature and used a protein large language model (pLLM), a form of generative artificial intelligence. They trained the model with the 13,000 PiggyBac sequences discovered to generate completely new sequences with enhanced activity. This approach not only optimized one of the existing transposases, but also demonstrated that AI-engineered variants are compatible with advanced gene editing technologies such as the FiCAT platform.
“Publishing this paper in Nature Biotechnology opens the way to revolutionizing the field of gene editing and advanced therapies and cements Integra Therapeutics’ position at the forefront of gene therapies and the use of innovative tools like AI for protein design in our development,” notes Dr. Avencia Sánchez-Mejías, CEO and co-founder of Integra Therapeutics.
“For the first time, we have used generative AI to create synthetic parts and expand nature. Like the cognitive power of ChatGPT can be used to write a poem, we have used the protein-based large language models to generate new elements that comply with the physical and chemical principles of genes,” explains Dr. Marc Güell, scientific director at Integra Therapeutics and ICREA researcher at MELIS-UPF where he heads up the Translational Synthetic Biology Lab.
“These AI models are trained with all known protein sequences on earth and learn the internal language or ‘grammar’ of proteins. Using this grammar, they are able to speak this language perfectly, generating completely new proteins that maintain structural and functional meaning,” says Dr. Noelia Ferruz, who leads the Artificial Intelligence for Protein Design Group at the CRG.
To accelerate and expand its FiCAT technology and pipeline of therapeutic products, Integra Therapeutics forges strategic partnerships with leading companies and research centers like UPF and CRG.
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[Press-News.org] Generative AI is more efficient than nature at designing proteins to edit the genome
After studying the diversity of mobile elements in the genome to feed and train generative AI tools, researchers have been able to design and lab validate new synthetic proteins that can edit the genome more efficiently than natural proteins.