Distance-based Protein Folding Powered by Deep Learning

Understand

Contact-assisted protein folding has made very good progress, but two challenges remain.

• One is accurate contact prediction for proteins lack of many sequence homologs and the other is that time-consuming folding simulation is often needed to predict good 3D models from predicted contacts.
• We show that protein distance matrix can be predicted well by deep learning and then directly used to construct 3D models without folding simulation at all.
• Using distance geometry to construct 3D models from our predicted distance matrices, we successfully folded 21 of the 37 CASP12 hard targets with a median family size of 58 effective sequence homologs within 4 hours on a Linux computer of 20 CPUs.