Researchers from the Technion–Israel Institute of Technology and Tel Aviv University have unveiled BetaDescribe, an artificial intelligence system capable of translating raw protein sequences into detailed natural-language descriptions. The findings were published in the Proceedings of the National Academy of Sciences, marking a significant advance in computational biology and drug discovery. Led by doctoral candidate Edo Dotan and jointly supervised by Professor Yonatan Belinkov of the Technion and Professor Tal Pupkin of Tel Aviv University, the project also involved researchers from Tel Aviv University School of Life Sciences. Protein analysis remains a cornerstone of modern medicine and biotechnology, yet experimental characterization is notoriously slow and costly. While large language models have attempted to address this bottleneck, they have struggled to accurately predict functions for proteins lacking close evolutionary relatives. BetaDescribe overcomes these limitations by integrating a generative framework with dedicated verification and evaluation mechanisms. Rather than relying solely on sequence similarity, the system infers functional properties, catalytic activity, metabolic involvement, and potential binding sites even for previously uncharacterized sequences. In validation trials, BetaDescribe successfully generated accurate functional descriptions for six novel proteins that had not been previously studied in laboratory settings. The technology effectively bridges the gap between the hundreds of thousands of proteins currently documented in experimental databases and the estimated billions that exist in nature. By converting complex biochemical data into accessible textual insights, the system enables researchers to rapidly formulate evidence-based hypotheses about protein behavior. The implications for biomedical research and industrial biotechnology are substantial. Accelerating the functional annotation of unknown proteins shortens the timeline from basic scientific discovery to practical applications, including targeted drug development, agricultural innovation, and advanced material design. This capability is particularly relevant for translating natural biological mechanisms into therapeutic solutions, a pathway previously exemplified by peptides derived from desert lizard saliva that informed the creation of obesity and diabetes treatments. As biological datasets continue to expand, automated interpretation tools like BetaDescribe will become increasingly critical. By reducing reliance on lengthy experimental workflows and providing reliable functional predictions at scale, the system positions computational biology to meet the growing demands of precision medicine and synthetic biology. The research team anticipates that widespread adoption will streamline target identification, optimize lead compound development, and ultimately reduce the time and capital required to bring novel biologics to clinical trials.