Nature is pretty good at designing proteins. Scientists are even better. But artificial intelligence holds the promise of improving proteins many times over. Medical applications for such "designer proteins" range from creating more precise antibodies for treating autoimmune conditions or cancers to more effective vaccines against viruses. Applications may extend beyond medicine to, for example, growing better crops that could be more nutritious or absorb more carbon dioxide from the atmosphere. Investigators from Mass General Brigham and Beth Israel Deaconess Medical Center (BIDMC) have developed an artificial intelligence (AI) tool known as EVOLVEpro that may represent a leap forward in protein engineering. In a paper published in Science , the research team demonstrates the tool's ability to make proteins more stable, precise, and efficient by applying the model to engineer six proteins with different applications.
"The power of a tool like this is that we're not restricted by evolution. Using AI, we can choose to optimize a protein to be better in whatever way is needed," said co-senior author Omar Abudayyeh, PhD, an investigator at the Gene and Cell Therapy Institute at Mass General Brigham and Engineering in Medicine Division in the Department of Medicine at Brigham and Women's Hospital. Abudayyeh also has a secondary appointment in the Center for Virology and Vaccine Research at BIDMC and is an assistant professor at Harvard Medical School. "We can make a protein that's better, faster, stronger. We can design it to be more efficient at binding to a target to improve a therapy or improve its function. If we can measure it, we can improve upon it."
The concept of protein engineering is not new, but the emergence of AI and large language models is beginning to revolutionize the field. Protein language models (PLMs) can learn the "grammar" of proteins, reading protein sequences from across large genomic databases and offering suggestions that can improve proteins in ways that a scientist specifies. Much like new LLMs, EVOLVEpro acts as a layer on top of previous models, which can reason and provide more thought before responding.
"Protein modeling has advanced in recent years, and we wondered if we could now use large language models to essentially predict better protein sequences," said co-senior author Jonathan S. Gootenberg, PhD, of the Center for Virology and Vaccine Research at BIDMC, member of the Gene and Cell Therapy Institute at Mass General Brigham, and member of the faculty at Harvard Medical School. "Our results consistently show how well this tool can work. We picked two clinically relevant antibodies—either already in use or close to human use—and found that with EVOLVEpro, we could engineer an antibody that could bind better and had better expression. Usually, you can do well on one of those outcomes, but here we saw improvements to both."
Studies like this one show the promise of advances in gene and cell therapy technologies for transforming medicine. The Mass General Brigham Gene and Cell Therapy Institute (GCTI) was established in 2022 to fuel the discovery and development of targeted, transformative treatments that have the potential to cure diseases or halt their progression. The Institute unites more than 500 researchers and clinicians dedicated to advancing gene and cell therapy for first-in-human clinical trials, and ultimately, life-saving treatments for patients.
The research team for the Science paper, led by first authors Kaiyi Jiang and Zhaoqing Yan, of the Gene and Cell Therapy Institute at Mass General Brigham, and Matteo Di Bernardo at the Massachusetts Institute of Technology, used EVOLVEpro to engineer six proteins. The researchers found that the two monoclonal antibodies EVOLVEpro engineered were up to 30-fold better at sticking to their target. A miniature CRISPR nuclease was five times more effective at making genetic changes. A protein used for n gene editing, called a prime editor, was twice as good at inserting sequences into different parts of the genome. A protein called Bxb1 integrase was four times more efficient at inserting DNA into cells for programmable gene integration applications, and a protein for RNA production, a T7 RNA polymerase, was 100 times better at making accurate copies of RNA, which is important for manufacturing mRNA for mRNA therapies or vaccines.
"We anticipate that this is just the beginning for EVOLVEpro, which will continue to improve with time and could be used for a wide variety of protein engineering applications," said Jiang. "This technology marks the beginning of a new era where we can design proteins not just to match nature's designs, but to solve challenges nature never had to face—from creating more precise medicines to developing proteins that could help address global challenges like climate change and food security."
Authorship: In addition to Gootenberg, Abudayyeh. Jiang, and Yan, Mass General Brigham authors include Samantha R. Sgrizzi, Lukas Villiger, Alisan Kayabolen, and Josephine K. Carscadden. Additional authors include Matteo Di Bernardo, Byungji Kim, Masahiro Hiraizumi, and Hiroshi Nishimasu.
Disclosures: Gootenberg, Abudayyeh, Jiang, Di Bernardo, Villiger, and Yan have filed patents related to this work with provisional patent (#63/509,139). Gootenberg and Abudayyeh are co-founders of Sherlock Biosciences, Tome Biosciences, Doppler Biosciences, and Circle Labs. No companies are currently involved in commercializing this technology.
Funding: National Institutes of Health (1R21-AI149694, R01-EB031957, 1R01GM148745, R56-HG011857, and R01AG074932) the K. Lisa Yang and Hock E. Tan Center for Molecular Therapeutics in Neuroscience; Impetus Grants; the Cystic Fibrosis Foundation Pioneer Grant; Google Ventures; Pivotal Life Sciences; MGB Gene and Cell Therapy Institute; the Yosemite Fund; JSPS KAKENHI (21H05281 and 22H00403), the Takeda Medical Research Foundation, the Inamori Research Institute for Science, and JST, CREST (JPMJCR19H5 and JPMJCR23B6).
Paper cited: Jiang K et al. "Rapid in silico directed evolution by a protein language model with EVOLVEpro" Science DOI: 10.1126/science.adr6006
