Accurate prediction of protein structures and interactions using a three-track neural network
This paper presented RoseTTAFold, a three-track neural network that simultaneously processes one-dimensional sequence, two-dimensional residue-pair distances, and three-dimensional atomic coordinate information, with information flowing between the tracks. The method achieved protein structure prediction accuracy approaching that of AlphaFold2 while being more computationally efficient. It also demonstrated rapid generation of accurate models for protein-protein complexes.