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. Author manuscript; available in PMC: 2022 Jan 13.
Published in final edited form as: Science. 2021 Jul 15;373(6557):871–876. doi: 10.1126/science.abj8754

Fig. 1. Network architecture and performance.

Fig. 1

(A) RoseTTAFold architecture with 1D, 2D, and 3D attention tracks. Multiple connections between tracks allow the network to simultaneously learn relationships within and between sequences, distances, and coordinates (see Methods and fig. S1 for details). (B) Average TM-score of prediction methods on the CASP14 targets. Zhang-server and BAKER-ROSETTASERVER were the top 2 server groups while AlphaFold2 and BAKER were the top 2 human groups in CASP14; BAKER-ROSETTASERVER and BAKER predictions were based on trRosetta. Predictions with the 2-track model and RoseTTAFold (both end-to-end and pyRosetta version) were completely automated. (C) Blind benchmark results on CAMEO medium and hard targets; model accuracies are TM-score values from the CAMEO website (https://cameo3d.org/).