Researchers have used generative artificial intelligence (AI) to help them make completely new antibodies for the first time.
The proof-of-principle work, reported this week in a preprint on bioRxiv, raises the possibility of bringing AI-guided protein design to the therapeutic antibody market, which is worth hundreds of billions of dollars.
Antibodies — immune molecules that strongly attach to proteins implicated in disease — have conventionally been made using brute-force approaches that involve immunizing animals or screening vast numbers of molecules.
AI tools that can shortcut those costly efforts have the potential to “democratize the ability to design antibodies”, says study co-author Nathaniel Bennett, a computational biochemist at the University of Washington in Seattle. “Ten years from now, this is how we’re going to be designing antibodies.”
“It’s a really promising piece of research” that represents an important step in applying AI protein-design tools to making new antibodies, says Charlotte Deane, an immuno-informatician at the University of Oxford, UK. Learn more
Cancer Grand Challenges recently announced five winning teams for 2024, which included five researchers from MIT: Michael Birnbaum, Regina Barzilay, Brandon DeKosky, Seychelle Vos, and Ömer Yilmaz. Each team is made up of interdisciplinary cancer researchers from across the globe and will be awarded $25 million over five years.
Birnbaum, an associate professor in the Department of Biological Engineering, leads Team MATCHMAKERS and is joined by co-investigators Barzilay, the School of Engineering Distinguished Professor for AI and Health in the Department of Electrical Engineering and Computer Science and the AI faculty lead at the MIT Abdul Latif Jameel Clinic for Machine Learning in Health; and DeKosky, Phillip and Susan Ragon Career Development Professor of Chemical Engineering. All three are also affiliates of the Koch Institute for Integrative Cancer Research At MIT. Learn more
The MATCHMAKERS team will embark on an ambitious research program to revolutionize TCR-pMHC pair prediction. Central to their approach is the integration of sequence and structure datasets, leveraging the group’s unique experience to merge these approaches into a unified pipeline. The team will curate new sequence and structure datasets scaled for advanced machine learning algorithms, develop novel experimental methods for data acquisition, and devise computational strategies addressing the nuances of TCR-pMHC binding and structure. The international and interdisciplinary group of investigators will combine data collected from natural TCR repertoires, datasets generated through molecular engineering strategies, structural and biochemical analysis, and the latest advances in artificial intelligence-based predictions and machine learning. Learn more
Jim Collins is one of the leading biomedical engineers in the world. He’s been elected to all 3 National Academies (Engineering, Science, and Medicine) and is one of the founders of the field of synthetic biology. In this conversation, we reviewed the seminal discoveries that he and his colleagues are making at the Antibiotics-AI Project at MIT. Learn more
Casey is taking his newsletter Platformer off Substack, as criticism over the company’s handling of pro-Nazi content grows. Then, The Wall Street Journal spoke with witnesses who said that Elon Musk had used LSD, cocaine, ecstasy and psychedelic mushrooms, worrying some directors and board members of his companies. And finally, how researchers found a new class of antibiotics with the help of an artificial intelligence algorithm used to win the board game Go.
Today’s guests:
Kirsten Grind, enterprise reporter for The Wall Street Journal
Felix Wong, postdoctoral fellow at M.I.T. and co-founder of Integrated Biosciences
Additional Reading:
Why Platformer is leaving Substack.
Elon Musk has reportedly used illegal drugs, worrying leaders at Tesla and SpaceX.
Researchers have discovered a new class of antibiotics using A.I. Learn more
Using a type of artificial intelligence known as deep learning, MIT researchers have discovered a class of compounds that can kill a drug-resistant bacterium that causes more than 10,000 deaths in the United States every year. Learn more
Antibiotic resistance is among the biggest global threats to human health. It was directly responsible for an estimated 1.27 million deaths in 2019 and contributed to nearly five million more. The problem only got worse during the COVID pandemic. And no new classes of antibiotics have been developed for decades. Learn more
I’ll wager that the event of 2023 that will change our lives the most in coming years is not the sighting of a Chinese spy balloon, the failure of Silicon Valley Bank, the fall of Kevin McCarthy’s speakership or any of the other eruptions that transfixed us this year.
More likely, the event that’s judged most transformative will be some scientific or technological advance that only a handful of people know about right now — because that’s how things almost always go. The first time the word “transistor” appeared in print was in an article in The New York Times in 1948, on Page 46, following a report on two new radio shows, “Mr. Tutt” and “Our Miss Brooks.” I think we can agree that the transistor has had more impact on our daily lives in the 75 years since than either of those bits of entertainment. Learn more
Computational approaches are emerging as powerful tools for the discovery of antibiotics. A study now uses machine learning to discover abaucin, a potent antibiotic that targets the bacterial pathogen Acinetobacter baumannii. Learn more