The move could set public health back decades, experts say The move, first reported by CNN, would change how many vaccines to protect against various diseases children get and when they receive those immunizations. Recommendations for several vaccines that are currently given routinely to children in the U.S.—including shots for rotavirus, varicella (chickenpox), hepatitis A, meningococcal bacteria, influenza and respiratory syncytial virus (RSV)—could be scrapped entirely under the plans, according to CNN. Childhood vaccines collectively protect children and the U.S. population as a whole against diseases, such as measles and hepatitis B, that once sickened, hospitalized or killed hundreds or even thousands every year. If you're enjoying this article, consider supporting our award-winning journalism by subscribing. By purchasing a subscription you are helping to ensure the future of impactful stories about the discoveries and ideas shaping our world today. Changing what vaccines kids get would be “a terrible mistake,” says Jessica Malaty Rivera, an infectious disease epidemiologist at Defend Public Health, an all-volunteer organization sponsored by a nonprofit. More children could get sick and die from preventable illnesses as a result. The two available shots, which are not technically vaccines but antibody drugs that protect against RSV, were approved in 2023 and 2025 and are more than 90 percent effective at protecting against hospitalization. The Trump administration has previously stated that it wants to model the U.S.'s vaccine policy after other developed countries and specifically Denmark, which recommends fewer vaccines than the U.S. does and recommends them at different times of life. But it doesn't make sense to compare the U.S. to countries, such as Denmark, that have a vastly different health care system. “We can learn a lot from some studies that come from other countries, but we have to use a critical mind to figure out what is applicable to our context and what isn't,” says Jennifer Nuzzo, an epidemiologist and director of the Pandemic Center at Brown University. A key difference between the U.S. and Denmark that Kennedy and other U.S. health officials seem to avoid is that the European country has a national health care system that covers everyone for free while the U.S. does not. “Denmark or other places have universal health coverage where people don't fall into health care gaps like they do in the United States. Whatever the CDC recommends influences what private health insurers will cover and what federal programs, such as the Vaccines for Children program, will subsidize. “When changes are made to the schedule, it will have consequences for who is able to get vaccines, whether or not you want them,” Nuzzo says. “This isn't about allowing you to opt out. This is about making it harder for you to opt in.” The Department of Health and Human Services had scheduled a press conference about children's health on Friday but has since pushed the announcement back until next year. If these further changes come to pass, they will chip away at the collective protection against deadly infectious diseases, Nuzzo says. Individual medical providers and states may step up to preserve access to vaccines, but people could still slip through the cracks of an increasingly patchwork public health system. “We have to make public health recommendations that work for all. There are clearly people who can't spend a bulk of their time trying to find the credible sources of information,” Nuzzo says. “I'm worried about people who just won't get the lifesaving protection that they need.” Young is associate editor for health and medicine at Scientific American. She has edited and written stories that tackle a wide range of subjects, including the COVID pandemic, emerging diseases, evolutionary biology and health inequities. Young has nearly a decade of newsroom and science journalism experience. Before joining Scientific American in 2023, she was an associate editor at Popular Science and a digital producer at public radio's Science Friday. She has appeared as a guest on radio shows, podcasts and stage events. Young has also spoken on panels for the Asian American Journalists Association, American Library Association, NOVA Science Studio and the New York Botanical Garden. Her work has appeared in Scholastic MATH, School Library Journal, IEEE Spectrum, Atlas Obscura and Smithsonian Magazine. Young studied biology at California Polytechnic State University, San Luis Obispo, before pursuing a master's at New York University's Science, Health & Environmental Reporting Program. Tanya Lewis is senior desk editor for health and medicine at Scientific American. Previously, she has written for outlets that include Insider, Wired, Science News and others. She has a degree in biomedical engineering from Brown University and one in science communication from the University of California, Santa Cruz. If you enjoyed this article, I'd like to ask for your support. Scientific American has served as an advocate for science and industry for 180 years, and right now may be the most critical moment in that two-century history. If you subscribe to Scientific American, you help ensure that our coverage is centered on meaningful research and discovery; that we have the resources to report on the decisions that threaten labs across the U.S.; and that we support both budding and working scientists at a time when the value of science itself too often goes unrecognized. In return, you get essential news, captivating podcasts, brilliant infographics, can't-miss newsletters, must-watch videos, challenging games, and the science world's best writing and reporting. There has never been a more important time for us to stand up and show why science matters.

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. , Article number: (2025) Cite this article We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply. Predicting changes in protein thermostability caused by amino acid substitutions is essential for understanding human diseases and engineering proteins for practical applications. While recent protein generative models demonstrate impressive zero-shot performance in predicting various protein properties without task-specific training, their strong unsupervised prediction ability remains underexploited to improve protein stability prediction. We present SPURS, a deep learning framework that rewires and integrates two complementary protein generative models–a protein language model and an inverse folding model–and reprograms this unified framework for stability prediction through supervised fine-tuning on mega-scale thermostability data. SPURS delivers accurate, efficient, and scalable stability predictions and generalizes to unseen proteins and mutations. Beyond stability prediction, SPURS enables broad applications in protein informatics, including zero-shot identification of functional residues, improved low-N protein fitness prediction, and systematic dissection of stability-pathogenicity for human diseases. Together, these capabilities establish SPURS as a versatile tool for advancing protein stability prediction and protein engineering at scale. Unless otherwise stated, all data supporting the results of this study can be found in the article, supplementary, and source data files. We used esm2_t33_650M_UR50D checkpoint of ESM2, v_48_020 checkpoint of ProteinMPNN [https://github.com/dauparas/ProteinMPNN/blob/main/vanilla_model_weights_48_020.pt], and esm1v_t33_650M_UR90S_1 checkpoint of ESM1v [https://dl.fbaipublicfiles.com/fair-esm/models/esm1v_t33_650M_UR90S_1.pt]. The Megascale dataset was downloaded from [https://zenodo.org/records/7844779]. Megascale split, Fireprot(HF), S669, Ssym-direct, and Ssym-inverse were downloaded from [https://github.com/Kuhlman-Lab/ThermoMPNN]. S783, S2648, S461, S8754, S4346 and S571 were downloaded from [https://github.com/Gonglab-THU/GeoStab]. 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This work is supported in part by the National Institute of General Medical Sciences of the National Institutes of Health under award R35GM150890 (Y.L.). The authors acknowledge the computational resources provided by the Delta GPU Supercomputer at NCSA of UIUC through allocation CIS230097 (Y.L.) from the Advanced Cyberinfrastructure Coordination Ecosystem: Services & Support (ACCESS) program, which is supported by NSF grants #2138259, #2138286, #2138307, #2137603, and #2138296 (Y.L. ), and the cloud computational resources provided by Microsoft Azure through the Cloud Hub program at GaTech IDEaS and the Microsoft Accelerate Foundation Models Research (AFMR) program (Y.L. School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA, USA Ziang Li & Yunan Luo Search author on:PubMed Google Scholar Search author on:PubMed Google Scholar conceived and supervised the project. developed the computational framework and performed the evaluation analyses. wrote the manuscript. Correspondence to Yunan Luo. The authors declare no competing interests. Nature Communications thanks Emil Alexov, Arne Elofsson, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available. Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. 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To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. Reprints and permissions Li, Z., Luo, Y. Generalizable and scalable protein stability prediction with rewired protein generative models. Accepted: 03 December 2025 Anyone you share the following link with will be able to read this content: Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative ISSN 2041-1723 (online) © 2025 Springer Nature Limited Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.