The Goodarzi lab is on fire!<\/p>\n
RNA-binding proteins (RBPs) recognize RNA by sequence and shape, but which matters varies by site. Introducing Pythia, an interpretable AI model trained on a new high-resolution atlas of 67 RBPs\u2014a collaboration between Genophoria, Chan Zuckerberg Biohub, UCSF, Calico, & Vector Institute.<\/p>\n
What if an AI could look at a strand of RNA, predict how it folds in 3D, and instantly tell you which proteins will attach to it? A groundbreaking new dataset and neural network are doing just that\u2014bringing high-resolution clarity to the invisible mechanics of gene regulation.<\/p>\n
Decoding how proteins shape our RNA: a new atlas and AI tool for cellular control.<\/p>\n
Inside our cells, DNA serves as the master blueprint, but RNA acts as the working copy that delivers instructions to build proteins. Once RNA is made, it is heavily managed by a special class of molecules called RNA-binding proteins (RBPs). These proteins dictate how long an RNA molecule lasts, where it travels inside the cell, and how efficiently it is translated into functional proteins.<\/p>\n
Despite their importance, scientists have lacked high-resolution, uniform maps showing exactly where these proteins bind across the entire human genome.<\/p>\n
To bridge this gap, a team of researchers has developed a powerful suite of new experimental and computational tools to decode how proteins and RNA interact.<\/p>\n
Key Breakthroughs of the Study.<\/p>\n