Machine learning streamlines the complexities of making better proteins

Machine learning streamlines the complexities of making better proteins Skip to content Subscribe today Every print subscription comes with full digital access Subscribe Now Menu All Topics Health Humans Anthropology Health & Medicine Archaeology Psychology View All Life Animals Plants Ecosystems Paleontology Neuroscience Genetics Microbes View All Earth Agriculture Climate Oceans Environment View All Physics Materials Science Quantum Physics Particle Physics View All Space Astronomy Planetary Science Cosmology View All Magazine Menu All Stories Multimedia Reviews Puzzles Collections Educator Portal Century of Science Unsung characters Coronavirus Outbreak Newsletters Investors Lab About SN Explores Our Store SIGN IN Donate Home INDEPENDENT JOURNALISM SINCE 1921 SIGN IN Search Open search Close search Home INDEPENDENT JOURNALISM SINCE 1921 All Topics Earth Agriculture Climate Oceans Environment Humans Anthropology Health & Medicine Archaeology Psychology Life Animals Plants Ecosystems Paleontology Neuroscience Genetics Microbes Physics Materials Science Quantum Physics Particle Physics Space Astronomy Planetary Science Cosmology Tech Computing Artificial Intelligence Chemistry Math Science & Society All Topics Health Humans Humans Anthropology Health & Medicine Archaeology Psychology Recent posts in Humans Chemistry Machine learning streamlines the complexities of making better proteins By Skyler Ware5 hours ago Health & Medicine Home HPV tests won’t replace the ob-gyn By Jamie Ducharme10 hours ago Artificial Intelligence Real-world medical questions stump AI chatbots By Tina Hesman SaeyFebruary 17, 2026 Life Life Animals Plants Ecosystems Paleontology Neuroscience Genetics Microbes Recent posts in Life Paleontology A mouth built for efficiency may have helped the earliest bird fly By Jay Bennett8 hours ago Animals Some dog breeds carry a higher risk of breathing problems By Jake BuehlerFebruary 18, 2026 Animals Regeneration of fins and limbs relies on a shared cellular playbook By Elizabeth PennisiFebruary 18, 2026 Earth Earth Agriculture Climate Oceans Environment Recent posts in Earth Climate Snowball Earth might have had a dynamic climate and open seas By Michael Marshall7 hours ago Oceans Evolution didn’t wait long after the dinosaurs died By Elie DolginFebruary 13, 2026 Earth Earth’s core may hide dozens of oceans of hydrogen By Nikk OgasaFebruary 10, 2026 Physics Physics Materials Science Quantum Physics Particle Physics Recent posts in Physics Physics Physicists dream up ‘spacetime quasicrystals’ that could underpin the universe By Emily ConoverFebruary 17, 2026 Physics A precise proton measurement helps put a core theory of physics to the test By Emily ConoverFebruary 11, 2026 Physics The only U.S. particle collider shuts down – so a new one may rise By Emily ConoverFebruary 6, 2026 Space Space Astronomy Planetary Science Cosmology Recent posts in Space Astronomy This inside-out planetary system has astronomers scratching their heads By Adam MannFebruary 12, 2026 Space Artemis II is returning humans to the moon with science riding shotgun By Lisa GrossmanFebruary 4, 2026 Physics A Greek star catalog from the dawn of astronomy, revealed By Adam MannJanuary 30, 2026 News Chemistry Machine learning streamlines the complexities of making better proteins The AI framework predicts how proteins will function with several interacting mutations A new machine learning framework called MULTI-evolve dramatically condenses the protein engineering process. Eugene Mymrin/Moment/Getty Images By Skyler Ware 5 hours ago Share this:Share Share via email (Opens in new window) Email Share on Facebook (Opens in new window) Facebook Share on Reddit (Opens in new window) Reddit Share on X (Opens in new window) X Print (Opens in new window) Print Making high-performance proteins for medicines or consumer products can take trial after trial of tweaks, experiments and fine-tuning. A new machine learning framework squeezes all that into a single round of testing. The technique, called MULTI-evolve, predicts how proteins will behave when several of their amino acids are swapped for others. MULTI-evolve blends laboratory experiments with machine learning to find these upgraded proteins, researchers report February 19 in Science. Specially-crafted proteins play a role in everyday products like medicines, biofuels and even laundry detergent. Scientists usually need to swap out multiple amino acids during the design process to boost a protein’s performance. But replacing one amino acid with another can change how the next swap will affect the protein’s function, so finding combinations of swaps that work well together often requires many iterative rounds of modifications and laboratory tests. “It’s this very high-dimensional search problem where we effectively do guess and check,” says Patrick Hsu, a bioengineer at the University of California, Berkeley, and the Arc Institute in Palo Alto, Calif. Sign up for our newsletter We summarize the week's scientific breakthroughs every Thursday. Hsu and colleagues built the MULTI-evolve workflow to cut out most of those iterations and predict high-performing proteins with multiple swaps, or mutations, in one round of testing. To do that, they needed information about how different mutations affected each other. For each protein the team targeted, the workflow had three steps. First, the researchers used either previous data or machine learning techniques to predict how single amino acid swaps would affect protein function. Then, to establish how the mutations interacted with each other, they made a series of proteins that each had two of those mutations in the lab and tested how well each one worked. Finally, they trained a machine learning model on that laboratory data and asked it to predict how well the target protein would function with five or more mutations. The team tested MULTI-evolve on three proteins, including an antibody relevant to autoimmune diseases and a protein used in CRISPR gene editing. In each case, the model found several combinations of mutations that in laboratory tests outperformed the original proteins, suggesting the model could pick out a set of swaps that work well together. Among the many protein jobs MULTI-evolve could streamline, Hsu highlighted two: using one protein to track another’s movement inside a cell and building better gene therapies for people whose bodies don’t produce certain enzymes. “We’re excited about this work,” Hsu says. “I think there’s tremendous interest in how this actually changes the practice of science.” Questions or comments on this article? E-mail us at feedback@sciencenews.org | Reprints FAQ Citations V. Q. Tran et al. Rapid directed evolution guided by protein language models and epistatic interactions. Science, published online Feb. 19, 2026. doi: 10.1126/science.aea1820. About Skyler Ware E-mail Skyler Ware was the 2023 AAAS