Life depends on molecular machines made of proteins that interact with each other to form functional complexes. Researchers need accurate descriptions of protein-protein interactions to understand molecular biosystems, but obtaining such descriptions is very challenging, especially for theoretical approaches. Generalizing AlphaFold 2, a powerful deep learning algorithm for predicting protein structures from sequence, researchers at Georgia Institute of Technology and Oak Ridge National Laboratory proposed a computational approach, AF2Complex, to not only predict the atomic structural models of interacting proteins, but also to predict whether multiple proteins interact, even if they experience transient interactions that are difficult to capture experimentally. The Georgia Tech School of Biological Sciences researchers are Mu Gao, senior research scientist, and Jeffrey Skolnick, Regents' Professor; Mary and Maisie Gibson Chair & GRA Eminent Scholar in Computational Systems Biology. (Their study is funded in part by the U.S. Dept. of Energy and the National Institutes of Health.)