Cell Death Discovery, Published online: 18 January 2025; doi:10.1038/s41420-025-02290-6

Deciphering the mechanisms of long non-coding RNAs in ferroptosis: insights into its clinical significance in cancer progression and immunology

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When you think of AI’s contributions to science, you probably think of AlphaFold, the Google DeepMind protein-folding program that earned its creator a Nobel Prize last year.

Now OpenAI says it’s getting into the science game too—with a model for engineering proteins.

The company says it has developed a language model that dreams up proteins capable of turning regular cells into stem cells—and that it has handily beat humans at the task.

The work represents OpenAI’s first model focused on biological data and its first public claim that its models can deliver unexpected scientific results. As such, it is a step toward determining whether or not AI can make true discoveries, which some argue is a major test on the pathway to “artificial general intelligence.”

Last week, OpenAI CEO Sam Altman said he was “confident” his company knows how to build an AGI, adding that “superintelligent tools could massively accelerate scientific discovery and innovation well beyond what we are capable of doing on our own.” 

The protein engineering project started a year ago when Retro Biosciences, a longevity research company based in San Francisco, approached OpenAI about working together.

That link-up did not happen by chance. Sam Altman, the CEO of OpenAI, personally funded Retro with $180 million, as MIT Technology Review first reported in 2023.

Retro has the goal of extending the normal human lifespan by 10 years. For that, it studies what are called Yamanaka factors. Those are a set of proteins that, when added to a human skin cell, will cause it to morph into a young-seeming stem cell, a type that can produce any other tissue in the body. 

It’s a phenomenon that researchers at Retro, and at richly funded companies like Altos Labs, see as the possible starting point for rejuvenating animals, building human organs, or providing supplies of replacement cells.

But such cell “reprogramming” is not very efficient. It takes several weeks, and less than 1% of cells treated in a lab dish will complete the rejuvenation journey.

OpenAI’s new model, called GPT-4b micro, was trained to suggest ways to re-engineer the protein factors to increase their function. According to OpenAI, researchers used the model’s suggestions to change two of the Yamanaka factors to be more than 50 times as effective—at least according to some preliminary measures. 

“Just across the board, the proteins seem better than what the scientists were able to produce by themselves,” says John Hallman, an OpenAI researcher.

Hallman and OpenAI’s Aaron Jaech, as well as Rico Meinl from Retro, were the model’s lead developers.

Outside scientists won’t be able to tell if the results are real until they’re published, something the companies say they are planning. Nor is the model available for wider use—it’s still a bespoke demonstration, not an official product launch.

“This project is meant to show that we’re serious about contributing to science,” says Jaech. “But whether those capabilities will come out to the world as a separate model or whether they’ll be rolled into our mainline reasoning models—that’s still to be determined.”

The model does not work the same way as Google’s AlphaFold, which predicts what shape proteins will take. Since the Yamanaka factors are unusually floppy and unstructured proteins, OpenAI said, they called for a different approach, which its large language models were suited to.

The model was trained on examples of protein sequences from many species, as well as information on which proteins tend to interact with one another. While that’s a lot of data, it’s just a fraction of what OpenAI’s flagship chatbots were trained on, making GPT-4b an example of a “small language model” that works with a focused data set.

Once Retro scientists were given the model, they tried to steer it to suggest possible redesigns of the Yamanaka proteins. The prompting tactic used is similar to the “few-shot” method, in which a user queries a chatbot by providing a series of examples with answers, followed by an example for the bot to respond to.

Although genetic engineers have ways to direct evolution of molecules in the lab, they can usually test only so many possibilities. And even a protein of typical length can be changed in nearly infinite ways (since they’re built from hundreds of amino acids, and each acid comes in 20 possible varieties).

OpenAI’s model, however, often spits out suggestions in which a third of the amino acids in the proteins were changed.

“We threw this model into the lab immediately and we got real-world results,” says Retro’s CEO, Joe Betts-Lacroix. He says the model’s ideas were unusually good, leading to improvements over the original Yamanaka factors in a substantial fraction of cases.

Vadim Gladyshev, a Harvard University aging researcher who consults with Retro, says better ways of making stem cells are needed. “For us, it would be extremely useful. [Skin cells] are easy to reprogram, but other cells are not,” he says. “And to do it in a new species—it’s often extremely different, and you don’t get anything.” 

How exactly the GPT-4b arrives at its guesses is still not clear—as is often the case with AI models. “It’s like when AlphaGo crushed the best human at Go, but it took a long time to find out why,” says Betts-Lacroix. “We are still figuring out what it does, and we think the way we apply this is only scratching the surface.”

OpenAI says no money changed hands in the collaboration. But because the work could benefit Retro—whose biggest investor is Altman—the announcement may add to questions swirling around the OpenAI CEO’s side projects.

Last year, the Wall Street Journal said Altman’s wide-ranging investments in private tech startups amount to an “opaque investment empire” that is “creating a mounting list of potential conflicts,” since some of these companies also do business with OpenAI.

In Retro’s case, simply being associated with Altman, OpenAI, and the race toward AGI could boost its profile and increase its ability to hire staff and raise funds. Betts-Lacroix did not answer questions about whether the early-stage company is currently in fundraising mode. 

OpenAI says Altman was not directly involved in the work and that it never makes decisions based on Altman’s other investments. 

The tissue adjacent to a tumor behaves differently than areas farther away: The tumor's cancerous cells influence their surroundings, blocking the body's immune defenses and creating a sort of haven in which the tumor can grow. Treatments that target some of these pro-tumor actions are effective in a number of cancers, but only for some patients; in others, these treatments have little effect. Researchers have now developed a new approach that simultaneously targets several of these pro-tumor actions at once, which they have demonstrated can effectively reduce tumor growth across several types of cancers. Their new findings point to a potential new treatment that may benefit more patients than current therapeutic options.

The chemical composition of a material alone sometimes reveals little about its properties. The decisive factor is often the arrangement of the molecules in the atomic lattice structure or on the surface of the material. Materials science utilizes this factor to create certain properties by applying individual atoms and molecules to surfaces with the aid of high-performance microscopes. Using artificial intelligence, a new research group now wants to take the construction of nanostructures to a new level.

Scientists have used deep learning to design new proteins that bind to complexes involving other small molecules like hormones or drugs, opening up a world of possibilities in the computational design of molecular interactions for biomedicine.

The American Cancer Society (ACS) today released Cancer Statistics, 2025, the organization's annual report on cancer facts and trends. The new findings show the cancer mortality rate declined by 34% from 1991 to 2022 in the United States, averting approximately 4.5 million deaths. However, this steady progress is jeopardized by increasing incidence for many cancer types, especially among women and younger adults, shifting the burden of disease. For example, incidence rates in women 50-64 years of age have surpassed those in men, and rates in women under 50 are now 82% higher than their male counterparts, up from 51% in 2002.

Researchers have shown that the amount of a protein called CD74 can indicate which people with bowel cancer may respond best to immunotherapy.

Nature Biotechnology, Published online: 17 January 2025; doi:10.1038/s41587-024-02541-4

Therapeutic T cells with synthetic gene circuits act locally

Oncogene, Published online: 17 January 2025; doi:10.1038/s41388-024-03270-3

Evidence of DNA methylation heterogeneity and epipolymorphism in kidney cancer tissue samples

Nature, Published online: 15 January 2025; doi:10.1038/d41586-025-00134-y

Proteins made using machine learning successfully bind snake toxins — plus, Earth’s temperature breaks a significant climate threshold for the first time.

While most known types of DNA damage are fixed by our cells' in-house DNA repair mechanisms, some forms of DNA damage evade repair and can persist for many years, new research shows. This means that the damage has multiple chances to generate harmful mutations, which can lead to cancer.

Nature, Published online: 15 January 2025; doi:10.1038/d41586-025-00045-y

Machine-learning system can process words spoken in 101 languages, spitting out voice-synthesized translations in 36 target languages.

Nature, Published online: 15 January 2025; doi:10.1038/d41586-025-00052-z

The incoming US president is expected to gut support for research on the environment and infectious diseases, but could buoy work in artificial intelligence, quantum research and space exploration.

36氪获悉，中国中冶公告，公司2024年1-12月新签合同额为12,483.0亿元，较上年同期降低12.4%。其中，新签海外合同额为932.4亿元，较上年同期增长47.8%。

The new models could give antibiotics a laser-like precision to target only specific bacteria in specific parts of the body. This would represent a major step forward in the battle against the growing problem of antibiotic resistance.