• Breadcrumb Home News Turning Proteins Into PROSE Turning proteins into PROSE PROtein SEquencing launches to build microsystems that decipher complex proteins The ability to rapidly detect and identify unknown proteins remains a critical gap for a number of areas, such as healthcare, biotechnology, and national security. • Existing tools can characterize proteins with known sequences, but they struggle to read long, chemically complex, and modified proteins, including toxins and engineered threats designed to evade detection methods. • As advances in synthetic biology and artificial intelligence accelerate the creation of novel proteins, this gap is expected to widen. • Addressing it will require not only biological insight, but engineered platforms capable of directly measuring and interpreting molecular information at scale. • DARPA launched thePROtein SEquencing (PROSE) programto pursue fundamentally new approaches to this challenge. • Todayâ s protein sequencing methods are largely adaptations of tools designed for other tasks: Mass spectrometry performs well for short proteins but becomes less reliable as proteins grow longer, while optical and nanopore techniques developed for DNA and RNA struggle with proteinsâ far greater chemical diversity.

Article Summaries:

  • DARPA has launched the PROtein SEquencing (PROSE) program to develop microsystems that can directly read complex proteins without relying on reference sequences. Current methods-mass spectrometry, optical, and nanopore techniques-perform poorly on long, chemically diverse proteins, a gap that will widen as synthetic biology creates novel proteins. PROSE will integrate biophysics, nanofabrication, sensing, and AI to convert molecular signatures into digital data. The first 36‑month phase, led by Electronic Biosciences, Northeastern’s Kostas Research Institute, and Pumpkinseed, focuses on proof‑of‑concept sequencing and scalable integration. Phase two will target high‑fidelity sequencing of long proteins. The effort also explores bio‑microsystem integration for future hybrid computing.

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